CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: image segmentation</title> <meta name="description" content="Search results for: image segmentation"> <meta name="keywords" content="image segmentation"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="image segmentation" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="image segmentation"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1651</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: image segmentation</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1651</span> A Comparative Study of Image Segmentation Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mehdi%20Hosseinzadeh">Mehdi Hosseinzadeh</a>, <a href="https://publications.waset.org/search?q=Parisa%20Khoshvaght"> Parisa Khoshvaght</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In some applications, such as image recognition or compression, segmentation refers to the process of partitioning a digital image into multiple segments. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. Image segmentation is to classify or cluster an image into several parts (regions) according to the feature of image, for example, the pixel value or the frequency response. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. Several image segmentation algorithms were proposed to segment an image before recognition or compression. Up to now, many image segmentation algorithms exist and be extensively applied in science and daily life. According to their segmentation method, we can approximately categorize them into region-based segmentation, data clustering, and edge-base segmentation. In this paper, we give a study of several popular image segmentation algorithms that are available. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20Segmentation" title="Image Segmentation">Image Segmentation</a>, <a href="https://publications.waset.org/search?q=hierarchical%20segmentation" title=" hierarchical segmentation"> hierarchical segmentation</a>, <a href="https://publications.waset.org/search?q=partitional%20segmentation" title=" partitional segmentation"> partitional segmentation</a>, <a href="https://publications.waset.org/search?q=density%20estimation." title=" density estimation."> density estimation.</a> </p> <a href="https://publications.waset.org/10002407/a-comparative-study-of-image-segmentation-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10002407/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10002407/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10002407/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10002407/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10002407/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10002407/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10002407/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10002407/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10002407/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10002407/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10002407.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2918</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1650</span> A Comparative Study of Image Segmentation using Edge-Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Rajiv%20Kumar">Rajiv Kumar</a>, <a href="https://publications.waset.org/search?q=Arthanariee%20A.%20M."> Arthanariee A. M.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Image segmentation is the process to segment a given image into several parts so that each of these parts present in the image can be further analyzed. There are numerous techniques of image segmentation available in literature. In this paper, authors have been analyzed the edge-based approach for image segmentation. They have been implemented the different edge operators like Prewitt, Sobel, LoG, and Canny on the basis of their threshold parameter. The results of these operators have been shown for various images.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Edge%20Operator" title="Edge Operator">Edge Operator</a>, <a href="https://publications.waset.org/search?q=Edge-based%20Segmentation" title=" Edge-based Segmentation"> Edge-based Segmentation</a>, <a href="https://publications.waset.org/search?q=Image%20Segmentation" title=" Image Segmentation"> Image Segmentation</a>, <a href="https://publications.waset.org/search?q=Matlab%2010.4." title=" Matlab 10.4."> Matlab 10.4.</a> </p> <a href="https://publications.waset.org/16809/a-comparative-study-of-image-segmentation-using-edge-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16809/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16809/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16809/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16809/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16809/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16809/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16809/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16809/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16809/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16809/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16809.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3606</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1649</span> A Review on Image Segmentation Techniques and Performance Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=David%20Libouga%20Li%20Gwet">David Libouga Li Gwet</a>, <a href="https://publications.waset.org/search?q=Marius%20Otesteanu"> Marius Otesteanu</a>, <a href="https://publications.waset.org/search?q=Ideal%20Oscar%20Libouga"> Ideal Oscar Libouga</a>, <a href="https://publications.waset.org/search?q=Laurent%20Bitjoka"> Laurent Bitjoka</a>, <a href="https://publications.waset.org/search?q=Gheorghe%20D.%20Popa"> Gheorghe D. Popa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image segmentation is a method to extract regions of interest from an image. It remains a fundamental problem in computer vision. The increasing diversity and the complexity of segmentation algorithms have led us firstly, to make a review and classify segmentation techniques, secondly to identify the most used measures of segmentation performance and thirdly, discuss deeply on segmentation philosophy in order to help the choice of adequate segmentation techniques for some applications. To justify the relevance of our analysis, recent algorithms of segmentation are presented through the proposed classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Classification" title="Classification">Classification</a>, <a href="https://publications.waset.org/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/search?q=measures%20of%20performance." title=" measures of performance."> measures of performance.</a> </p> <a href="https://publications.waset.org/10009909/a-review-on-image-segmentation-techniques-and-performance-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10009909/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10009909/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10009909/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10009909/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10009909/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10009909/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10009909/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10009909/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10009909/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10009909/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10009909.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2051</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1648</span> Color Image Segmentation Using SVM Pixel Classification Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=K.%20Sakthivel">K. Sakthivel</a>, <a href="https://publications.waset.org/search?q=R.%20Nallusamy"> R. Nallusamy</a>, <a href="https://publications.waset.org/search?q=C.%20Kavitha"> C. Kavitha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The goal of image segmentation is to cluster pixels into salient image regions. Segmentation could be used for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing, or image database lookup. In this paper, we present a color image segmentation using support vector machine (SVM) pixel classification. Firstly, the pixel level color and texture features of the image are extracted and they are used as input to the SVM classifier. These features are extracted using the homogeneity model and Gabor Filter. With the extracted pixel level features, the SVM Classifier is trained by using FCM (Fuzzy C-Means).The image segmentation takes the advantage of both the pixel level information of the image and also the ability of the SVM Classifier. The Experiments show that the proposed method has a very good segmentation result and a better efficiency, increases the quality of the image segmentation compared with the other segmentation methods proposed in the literature.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20Segmentation" title="Image Segmentation">Image Segmentation</a>, <a href="https://publications.waset.org/search?q=Support%20Vector%20Machine" title=" Support Vector Machine"> Support Vector Machine</a>, <a href="https://publications.waset.org/search?q=Fuzzy%20C%E2%80%93Means" title=" Fuzzy C鈥揗eans"> Fuzzy C鈥揗eans</a>, <a href="https://publications.waset.org/search?q=Pixel%20Feature" title=" Pixel Feature"> Pixel Feature</a>, <a href="https://publications.waset.org/search?q=Texture%20Feature" title=" Texture Feature"> Texture Feature</a>, <a href="https://publications.waset.org/search?q=Homogeneity%0D%0Amodel" title=" Homogeneity model"> Homogeneity model</a>, <a href="https://publications.waset.org/search?q=Gabor%20Filter." title=" Gabor Filter."> Gabor Filter.</a> </p> <a href="https://publications.waset.org/10000781/color-image-segmentation-using-svm-pixel-classification-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10000781/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10000781/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10000781/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10000781/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10000781/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10000781/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10000781/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10000781/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10000781/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10000781/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10000781.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">6746</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1647</span> Image Segmentation Using 2-D Histogram in RGB Color Space in Digital Libraries </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=El%20Asnaoui%20Khalid">El Asnaoui Khalid</a>, <a href="https://publications.waset.org/search?q=Aksasse%20Brahim"> Aksasse Brahim</a>, <a href="https://publications.waset.org/search?q=Ouanan%20Mohammed"> Ouanan Mohammed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an unsupervised color image segmentation method. It is based on a hierarchical analysis of 2-D histogram in RGB color space. This histogram minimizes storage space of images and thus facilitates the operations between them. The improved segmentation approach shows a better identification of objects in a color image and, at the same time, the system is fast. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20segmentation" title="Image segmentation">Image segmentation</a>, <a href="https://publications.waset.org/search?q=hierarchical%20analysis" title=" hierarchical analysis"> hierarchical analysis</a>, <a href="https://publications.waset.org/search?q=2-D%20histogram" title=" 2-D histogram"> 2-D histogram</a>, <a href="https://publications.waset.org/search?q=Classification." title=" Classification."> Classification.</a> </p> <a href="https://publications.waset.org/10003798/image-segmentation-using-2-d-histogram-in-rgb-color-space-in-digital-libraries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003798/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10003798/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10003798/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10003798/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10003798/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10003798/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10003798/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10003798/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10003798/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10003798/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10003798.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1626</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1646</span> Image Segmentation Based on Graph Theoretical Approach to Improve the Quality of Image Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Deepthi%20Narayan">Deepthi Narayan</a>, <a href="https://publications.waset.org/search?q=Srikanta%20Murthy%20K."> Srikanta Murthy K.</a>, <a href="https://publications.waset.org/search?q=G.%20Hemantha%20Kumar"> G. Hemantha Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Graph based image segmentation techniques are considered to be one of the most efficient segmentation techniques which are mainly used as time & space efficient methods for real time applications. How ever, there is need to focus on improving the quality of segmented images obtained from the earlier graph based methods. This paper proposes an improvement to the graph based image segmentation methods already described in the literature. We contribute to the existing method by proposing the use of a weighted Euclidean distance to calculate the edge weight which is the key element in building the graph. We also propose a slight modification of the segmentation method already described in the literature, which results in selection of more prominent edges in the graph. The experimental results show the improvement in the segmentation quality as compared to the methods that already exist, with a slight compromise in efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Graph%20based%20image%20segmentation" title="Graph based image segmentation">Graph based image segmentation</a>, <a href="https://publications.waset.org/search?q=threshold" title=" threshold"> threshold</a>, <a href="https://publications.waset.org/search?q=Weighted%20Euclidean%20distance." title="Weighted Euclidean distance.">Weighted Euclidean distance.</a> </p> <a href="https://publications.waset.org/7317/image-segmentation-based-on-graph-theoretical-approach-to-improve-the-quality-of-image-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7317/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7317/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7317/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7317/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7317/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7317/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7317/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7317/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7317/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7317/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7317.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1563</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1645</span> An Automatic Gridding and Contour Based Segmentation Approach Applied to DNA Microarray Image Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Alexandra%20Oliveros">Alexandra Oliveros</a>, <a href="https://publications.waset.org/search?q=Miguel%20Sotaquir%C3%A1"> Miguel Sotaquir谩</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DNA microarray technology is widely used by geneticists to diagnose or treat diseases through gene expression. This technology is based on the hybridization of a tissue-s DNA sequence into a substrate and the further analysis of the image formed by the thousands of genes in the DNA as green, red or yellow spots. The process of DNA microarray image analysis involves finding the location of the spots and the quantification of the expression level of these. In this paper, a tool to perform DNA microarray image analysis is presented, including a spot addressing method based on the image projections, the spot segmentation through contour based segmentation and the extraction of relevant information due to gene expression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Contour%20segmentation" title="Contour segmentation">Contour segmentation</a>, <a href="https://publications.waset.org/search?q=DNA%20microarrays" title=" DNA microarrays"> DNA microarrays</a>, <a href="https://publications.waset.org/search?q=edge%0Adetection" title=" edge detection"> edge detection</a>, <a href="https://publications.waset.org/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/search?q=spot%20addressing." title=" spot addressing."> spot addressing.</a> </p> <a href="https://publications.waset.org/13665/an-automatic-gridding-and-contour-based-segmentation-approach-applied-to-dna-microarray-image-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13665/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13665/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13665/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13665/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13665/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13665/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13665/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13665/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13665/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13665/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13665.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1390</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1644</span> A Quantum-Inspired Evolutionary Algorithm forMultiobjective Image Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hichem%20Talbi">Hichem Talbi</a>, <a href="https://publications.waset.org/search?q=Mohamed%20Batouche"> Mohamed Batouche</a>, <a href="https://publications.waset.org/search?q=Amer%20Draa"> Amer Draa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present a new approach to deal with image segmentation. The fact that a single segmentation result do not generally allow a higher level process to take into account all the elements included in the image has motivated the consideration of image segmentation as a multiobjective optimization problem. The proposed algorithm adopts a split/merge strategy that uses the result of the k-means algorithm as input for a quantum evolutionary algorithm to establish a set of non-dominated solutions. The evaluation is made simultaneously according to two distinct features: intra-region homogeneity and inter-region heterogeneity. The experimentation of the new approach on natural images has proved its efficiency and usefulness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20segmentation" title="Image segmentation">Image segmentation</a>, <a href="https://publications.waset.org/search?q=multiobjective%20optimization" title=" multiobjective optimization"> multiobjective optimization</a>, <a href="https://publications.waset.org/search?q=quantum%20computing" title="quantum computing">quantum computing</a>, <a href="https://publications.waset.org/search?q=evolutionary%20algorithms." title=" evolutionary algorithms."> evolutionary algorithms.</a> </p> <a href="https://publications.waset.org/7741/a-quantum-inspired-evolutionary-algorithm-formultiobjective-image-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7741/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7741/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7741/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7741/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7741/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7741/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7741/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7741/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7741/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7741/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7741.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2359</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1643</span> Color Image Segmentation and Multi-Level Thresholding by Maximization of Conditional Entropy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=R.Sukesh%20Kumar">R.Sukesh Kumar</a>, <a href="https://publications.waset.org/search?q=Abhisek%20Verma"> Abhisek Verma</a>, <a href="https://publications.waset.org/search?q=Jasprit%20Singh"> Jasprit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work a novel approach for color image segmentation using higher order entropy as a textural feature for determination of thresholds over a two dimensional image histogram is discussed. A similar approach is applied to achieve multi-level thresholding in both grayscale and color images. The paper discusses two methods of color image segmentation using RGB space as the standard processing space. The threshold for segmentation is decided by the maximization of conditional entropy in the two dimensional histogram of the color image separated into three grayscale images of R, G and B. The features are first developed independently for the three ( R, G, B ) spaces, and combined to get different color component segmentation. By considering local maxima instead of the maximum of conditional entropy yields multiple thresholds for the same image which forms the basis for multilevel thresholding. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=conditional%20entropy" title="conditional entropy">conditional entropy</a>, <a href="https://publications.waset.org/search?q=multi-level%20thresholding" title=" multi-level thresholding"> multi-level thresholding</a>, <a href="https://publications.waset.org/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/search?q=two%20dimensional%20image%20histogram" title=" two dimensional image histogram"> two dimensional image histogram</a> </p> <a href="https://publications.waset.org/4566/color-image-segmentation-and-multi-level-thresholding-by-maximization-of-conditional-entropy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4566/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4566/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4566/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4566/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4566/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4566/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4566/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4566/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4566/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4566/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4566.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2998</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1642</span> A Comparative Study of Medical Image Segmentation Methods for Tumor Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mayssa%20Bensalah">Mayssa Bensalah</a>, <a href="https://publications.waset.org/search?q=Atef%20Boujelben"> Atef Boujelben</a>, <a href="https://publications.waset.org/search?q=Mouna%20Baklouti"> Mouna Baklouti</a>, <a href="https://publications.waset.org/search?q=Mohamed%20Abid"> Mohamed Abid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Features%20extraction" title="Features extraction">Features extraction</a>, <a href="https://publications.waset.org/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/search?q=medical%20images" title=" medical images"> medical images</a>, <a href="https://publications.waset.org/search?q=tumour%20detection." title=" tumour detection."> tumour detection.</a> </p> <a href="https://publications.waset.org/10011999/a-comparative-study-of-medical-image-segmentation-methods-for-tumor-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10011999/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10011999/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10011999/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10011999/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10011999/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10011999/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10011999/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10011999/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10011999/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10011999/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10011999.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">588</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1641</span> An Image Segmentation Algorithm for Gradient Target Based on Mean-Shift and Dictionary Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yanwen%20Li">Yanwen Li</a>, <a href="https://publications.waset.org/search?q=Shuguo%20Xie"> Shuguo Xie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gradient%20image" title="Gradient image">Gradient image</a>, <a href="https://publications.waset.org/search?q=segmentation%20and%20extract" title=" segmentation and extract"> segmentation and extract</a>, <a href="https://publications.waset.org/search?q=mean-shift%20algorithm" title=" mean-shift algorithm"> mean-shift algorithm</a>, <a href="https://publications.waset.org/search?q=dictionary%20learning." title=" dictionary learning."> dictionary learning.</a> </p> <a href="https://publications.waset.org/10008139/an-image-segmentation-algorithm-for-gradient-target-based-on-mean-shift-and-dictionary-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10008139/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10008139/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10008139/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10008139/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10008139/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10008139/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10008139/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10008139/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10008139/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10008139/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10008139.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">970</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1640</span> Nature Inspired Metaheuristic Algorithms for Multilevel Thresholding Image Segmentation - A Survey</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=C.%20Deepika">C. Deepika</a>, <a href="https://publications.waset.org/search?q=J.%20Nithya"> J. Nithya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Segmentation is one of the essential tasks in image processing. Thresholding is one of the simplest techniques for performing image segmentation. Multilevel thresholding is a simple and effective technique. The primary objective of bi-level or multilevel thresholding for image segmentation is to determine a best thresholding value. To achieve multilevel thresholding various techniques has been proposed. A study of some nature inspired metaheuristic algorithms for multilevel thresholding for image segmentation is conducted. Here, we study about Particle swarm optimization (PSO) algorithm, artificial bee colony optimization (ABC), Ant colony optimization (ACO) algorithm and Cuckoo search (CS) algorithm.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Ant%20colony%20optimization" title="Ant colony optimization">Ant colony optimization</a>, <a href="https://publications.waset.org/search?q=Artificial%20bee%20colony%0D%0Aoptimization" title=" Artificial bee colony optimization"> Artificial bee colony optimization</a>, <a href="https://publications.waset.org/search?q=Cuckoo%20search%20algorithm" title=" Cuckoo search algorithm"> Cuckoo search algorithm</a>, <a href="https://publications.waset.org/search?q=Image%20segmentation" title=" Image segmentation"> Image segmentation</a>, <a href="https://publications.waset.org/search?q=Multilevel%20thresholding" title=" Multilevel thresholding"> Multilevel thresholding</a>, <a href="https://publications.waset.org/search?q=Particle%20swarm%20optimization." title=" Particle swarm optimization."> Particle swarm optimization.</a> </p> <a href="https://publications.waset.org/9999738/nature-inspired-metaheuristic-algorithms-for-multilevel-thresholding-image-segmentation-a-survey" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999738/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999738/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999738/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999738/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999738/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999738/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999738/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999738/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999738/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999738/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999738.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3523</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1639</span> 3D Anisotropic Diffusion for Liver Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Wan%20Nural%20Jawahir%20Wan%20Yussof">Wan Nural Jawahir Wan Yussof</a>, <a href="https://publications.waset.org/search?q=Hans%20Burkhardt"> Hans Burkhardt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Liver segmentation is the first significant process for liver diagnosis of the Computed Tomography. It segments the liver structure from other abdominal organs. Sophisticated filtering techniques are indispensable for a proper segmentation. In this paper, we employ a 3D anisotropic diffusion as a preprocessing step. While removing image noise, this technique preserve the significant parts of the image, typically edges, lines or other details that are important for the interpretation of the image. The segmentation task is done by using thresholding with automatic threshold values selection and finally the false liver region is eliminated using 3D connected component. The result shows that by employing the 3D anisotropic filtering, better liver segmentation results could be achieved eventhough simple segmentation technique is used. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=3D%20Anisotropic%20Diffusion" title="3D Anisotropic Diffusion">3D Anisotropic Diffusion</a>, <a href="https://publications.waset.org/search?q=non-linear%20filtering" title=" non-linear filtering"> non-linear filtering</a>, <a href="https://publications.waset.org/search?q=CT%20Liver." title=" CT Liver."> CT Liver.</a> </p> <a href="https://publications.waset.org/9741/3d-anisotropic-diffusion-for-liver-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9741/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9741/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9741/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9741/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9741/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9741/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9741/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9741/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9741/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9741/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9741.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1597</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1638</span> Image Segmentation and Contour Recognition Based on Mathematical Morphology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Pinaki%20Pratim%20Acharjya">Pinaki Pratim Acharjya</a>, <a href="https://publications.waset.org/search?q=Esha%20Dutta"> Esha Dutta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In image segmentation contour detection is one of the important pre-processing steps in recent days. Contours characterize boundaries and contour detection is one of the most difficult tasks in image processing. Hence it is a problem of fundamental importance in image processing. Contour detection of an image decreases the volume of data considerably and useless information is removed, but the structural properties of the image remain same. In this research, a robust and effective contour detection technique has been proposed using mathematical morphology. Three different contour detection results are obtained by using morphological dilation and erosion. The comparative analyses of three different results also have been done. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20segmentation" title="Image segmentation">Image segmentation</a>, <a href="https://publications.waset.org/search?q=contour%20detection" title=" contour detection"> contour detection</a>, <a href="https://publications.waset.org/search?q=mathematical%20morphology." title=" mathematical morphology. "> mathematical morphology. </a> </p> <a href="https://publications.waset.org/10008703/image-segmentation-and-contour-recognition-based-on-mathematical-morphology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10008703/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10008703/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10008703/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10008703/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10008703/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10008703/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10008703/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10008703/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10008703/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10008703/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10008703.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1427</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1637</span> Color Image Segmentation Using Kekre-s Algorithm for Vector Quantization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=H.%20B.%20Kekre">H. B. Kekre</a>, <a href="https://publications.waset.org/search?q=Tanuja%20K.%20Sarode"> Tanuja K. Sarode</a>, <a href="https://publications.waset.org/search?q=Bhakti%20Raul"> Bhakti Raul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose segmentation approach based on Vector Quantization technique. Here we have used Kekre-s fast codebook generation algorithm for segmenting low-altitude aerial image. This is used as a preprocessing step to form segmented homogeneous regions. Further to merge adjacent regions color similarity and volume difference criteria is used. Experiments performed with real aerial images of varied nature demonstrate that this approach does not result in over segmentation or under segmentation. The vector quantization seems to give far better results as compared to conventional on-the-fly watershed algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20Segmentation" title="Image Segmentation">Image Segmentation</a>, <a href="https://publications.waset.org/search?q=" title=""></a>, <a href="https://publications.waset.org/search?q=Codebook" title=" Codebook"> Codebook</a>, <a href="https://publications.waset.org/search?q=Codevector" title=" Codevector"> Codevector</a>, <a href="https://publications.waset.org/search?q=data%0Acompression" title=" data compression"> data compression</a>, <a href="https://publications.waset.org/search?q=Encoding" title=" Encoding"> Encoding</a> </p> <a href="https://publications.waset.org/9491/color-image-segmentation-using-kekre-s-algorithm-for-vector-quantization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9491/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9491/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9491/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9491/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9491/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9491/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9491/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9491/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9491/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9491/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9491.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2195</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1636</span> Manipulation of Image Segmentation Using Cleverness Artificial Bee Colony Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Y.%20Harold%20Robinson">Y. Harold Robinson</a>, <a href="https://publications.waset.org/search?q=E.%20Golden%20Julie"> E. Golden Julie</a>, <a href="https://publications.waset.org/search?q=P.%20Joyce%20Beryl%20Princess"> P. Joyce Beryl Princess</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image segmentation is the concept of splitting the images into several images. Image Segmentation algorithm is used to manipulate the process of image segmentation. The advantage of ABC is that it conducts every worldwide exploration and inhabitant exploration for iteration. Particle Swarm Optimization (PSO) and Evolutionary Particle Swarm Optimization (EPSO) encompass a number of search problems. Cleverness Artificial Bee Colony algorithm has been imposed to increase the performance of a neighborhood search. The simulation results clearly show that the presented ABC methods outperform the existing methods. The result shows that the algorithms can be used to implement the manipulator for grasping of colored objects. The efficiency of the presented method is improved a lot by comparing to other methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Color%20information" title="Color information">Color information</a>, <a href="https://publications.waset.org/search?q=EPSO" title=" EPSO"> EPSO</a>, <a href="https://publications.waset.org/search?q=ABC" title=" ABC"> ABC</a>, <a href="https://publications.waset.org/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/search?q=active%20contour" title=" active contour"> active contour</a>, <a href="https://publications.waset.org/search?q=GMM." title=" GMM."> GMM.</a> </p> <a href="https://publications.waset.org/10005153/manipulation-of-image-segmentation-using-cleverness-artificial-bee-colony-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10005153/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10005153/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10005153/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10005153/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10005153/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10005153/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10005153/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10005153/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10005153/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10005153/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10005153.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1291</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1635</span> Image Segmentation by Mathematical Morphology: An Approach through Linear, Bilinear and Conformal Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dibyendu%20Ghoshal">Dibyendu Ghoshal</a>, <a href="https://publications.waset.org/search?q=Pinaki%20Pratim%20Acharjya"> Pinaki Pratim Acharjya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Image segmentation process based on mathematical morphology has been studied in the paper. It has been established from the first principles of the morphological process, the entire segmentation is although a nonlinear signal processing task, the constituent wise, the intermediate steps are linear, bilinear and conformal transformation and they give rise to a non linear affect in a cumulative manner.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20segmentation" title="Image segmentation">Image segmentation</a>, <a href="https://publications.waset.org/search?q=linear%20transform" title=" linear transform"> linear transform</a>, <a href="https://publications.waset.org/search?q=bilinear%20transform" title=" bilinear transform"> bilinear transform</a>, <a href="https://publications.waset.org/search?q=conformal%20transform" title=" conformal transform"> conformal transform</a>, <a href="https://publications.waset.org/search?q=mathematical%20morphology." title=" mathematical morphology. "> mathematical morphology. </a> </p> <a href="https://publications.waset.org/9999279/image-segmentation-by-mathematical-morphology-an-approach-through-linear-bilinear-and-conformal-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999279/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999279/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999279/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999279/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999279/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999279/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999279/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999279/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999279/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999279/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999279.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2192</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1634</span> Review of the Software Used for 3D Volumetric Reconstruction of the Liver</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=P.%20Strakos">P. Strakos</a>, <a href="https://publications.waset.org/search?q=M.%20Jaros"> M. Jaros</a>, <a href="https://publications.waset.org/search?q=T.%20Karasek"> T. Karasek</a>, <a href="https://publications.waset.org/search?q=T.%20Kozubek"> T. Kozubek</a>, <a href="https://publications.waset.org/search?q=P.%20Vavra"> P. Vavra</a>, <a href="https://publications.waset.org/search?q=T.%20Jonszta"> T. Jonszta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In medical imaging, segmentation of different areas of human body like bones, organs, tissues, etc. is an important issue. Image segmentation allows isolating the object of interest for further processing that can lead for example to 3D model reconstruction of whole organs. Difficulty of this procedure varies from trivial for bones to quite difficult for organs like liver. The liver is being considered as one of the most difficult human body organ to segment. It is mainly for its complexity, shape versatility and proximity of other organs and tissues. Due to this facts usually substantial user effort has to be applied to obtain satisfactory results of the image segmentation. Process of image segmentation then deteriorates from automatic or semi-automatic to fairly manual one. In this paper, overview of selected available software applications that can handle semi-automatic image segmentation with further 3D volume reconstruction of human liver is presented. The applications are being evaluated based on the segmentation results of several consecutive DICOM images covering the abdominal area of the human body.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20segmentation" title="Image segmentation">Image segmentation</a>, <a href="https://publications.waset.org/search?q=semi-automatic" title=" semi-automatic"> semi-automatic</a>, <a href="https://publications.waset.org/search?q=software" title=" software"> software</a>, <a href="https://publications.waset.org/search?q=3D%0D%0Avolumetric%20reconstruction." title=" 3D volumetric reconstruction."> 3D volumetric reconstruction.</a> </p> <a href="https://publications.waset.org/10000479/review-of-the-software-used-for-3d-volumetric-reconstruction-of-the-liver" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10000479/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10000479/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10000479/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10000479/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10000479/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10000479/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10000479/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10000479/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10000479/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10000479/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10000479.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">4469</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1633</span> Volterra Filter for Color Image Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.%20B.%20Meenavathi">M. B. Meenavathi</a>, <a href="https://publications.waset.org/search?q=K.%20Rajesh"> K. Rajesh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Color image segmentation plays an important role in computer vision and image processing areas. In this paper, the features of Volterra filter are utilized for color image segmentation. The discrete Volterra filter exhibits both linear and nonlinear characteristics. The linear part smoothes the image features in uniform gray zones and is used for getting a gross representation of objects of interest. The nonlinear term compensates for the blurring due to the linear term and preserves the edges which are mainly used to distinguish the various objects. The truncated quadratic Volterra filters are mainly used for edge preserving along with Gaussian noise cancellation. In our approach, the segmentation is based on K-means clustering algorithm in HSI space. Both the hue and the intensity components are fully utilized. For hue clustering, the special cyclic property of the hue component is taken into consideration. The experimental results show that the proposed technique segments the color image while preserving significant features and removing noise effects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Color%20image%20segmentation" title="Color image segmentation">Color image segmentation</a>, <a href="https://publications.waset.org/search?q=HSI%20space" title=" HSI space"> HSI space</a>, <a href="https://publications.waset.org/search?q=K%E2%80%93means%0Aclustering" title=" K鈥搈eans clustering"> K鈥搈eans clustering</a>, <a href="https://publications.waset.org/search?q=Volterra%20filter." title=" Volterra filter."> Volterra filter.</a> </p> <a href="https://publications.waset.org/11860/volterra-filter-for-color-image-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11860/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11860/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11860/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11860/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11860/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11860/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11860/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11860/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11860/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11860/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11860.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1857</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1632</span> Sequential Partitioning Brainbow Image Segmentation Using Bayesian</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yayun%20Hsu">Yayun Hsu</a>, <a href="https://publications.waset.org/search?q=Henry%20Horng-Shing%20Lu"> Henry Horng-Shing Lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate crosstalk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds, since biological information is inherently included inside the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Brainbow" title="Brainbow">Brainbow</a>, <a href="https://publications.waset.org/search?q=3D%20imaging" title=" 3D imaging"> 3D imaging</a>, <a href="https://publications.waset.org/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/search?q=neuron%20morphology" title=" neuron morphology"> neuron morphology</a>, <a href="https://publications.waset.org/search?q=biological%20data%20mining" title=" biological data mining"> biological data mining</a>, <a href="https://publications.waset.org/search?q=non-parametric%20learning." title=" non-parametric learning."> non-parametric learning.</a> </p> <a href="https://publications.waset.org/9996637/sequential-partitioning-brainbow-image-segmentation-using-bayesian" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9996637/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9996637/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9996637/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9996637/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9996637/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9996637/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9996637/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9996637/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9996637/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9996637/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9996637.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2259</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1631</span> Application of Fuzzy Neural Network for Image Tumor Description</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nahla%20Ibraheem%20Jabbar">Nahla Ibraheem Jabbar</a>, <a href="https://publications.waset.org/search?q=Monica%20Mehrotra"> Monica Mehrotra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper used a fuzzy kohonen neural network for medical image segmentation. Image segmentation plays a important role in the many of medical imaging applications by automating or facilitating the diagnostic. The paper analyses the tumor by extraction of the features of (area, entropy, means and standard deviation).These measurements gives a description for a tumor.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=FCM" title="FCM">FCM</a>, <a href="https://publications.waset.org/search?q=features%20extraction" title=" features extraction"> features extraction</a>, <a href="https://publications.waset.org/search?q=medical%20image%20processing" title=" medical image processing"> medical image processing</a>, <a href="https://publications.waset.org/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/search?q=segmentation." title=" segmentation."> segmentation.</a> </p> <a href="https://publications.waset.org/15604/application-of-fuzzy-neural-network-for-image-tumor-description" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15604/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15604/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15604/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15604/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15604/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15604/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15604/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15604/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15604/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15604/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15604.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2109</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1630</span> Data Oriented Model of Image: as a Framework for Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=A.%20Habibizad%20Navin">A. Habibizad Navin</a>, <a href="https://publications.waset.org/search?q=A.%20Sadighi"> A. Sadighi</a>, <a href="https://publications.waset.org/search?q=M.%20Naghian%20Fesharaki"> M. Naghian Fesharaki</a>, <a href="https://publications.waset.org/search?q=M.%20Mirnia"> M. Mirnia</a>, <a href="https://publications.waset.org/search?q=M.%20Teshnelab"> M. Teshnelab</a>, <a href="https://publications.waset.org/search?q=R.%20Keshmiri"> R. Keshmiri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper presents a new data oriented model of image. Then a representation of it, ADBT, is introduced. The ability of ADBT is clustering, segmentation, measuring similarity of images etc, with desired precision and corresponding speed.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Data%20oriented%20modelling" title="Data oriented modelling">Data oriented modelling</a>, <a href="https://publications.waset.org/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/search?q=ADBT%20and%20image%20processing." title=" ADBT and image processing."> ADBT and image processing.</a> </p> <a href="https://publications.waset.org/97/data-oriented-model-of-image-as-a-framework-for-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/97/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/97/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/97/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/97/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/97/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/97/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/97/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/97/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/97/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/97/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/97.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1799</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1629</span> Dual Pyramid of Agents for Image Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=K.%20Idir">K. Idir</a>, <a href="https://publications.waset.org/search?q=H.%20Merouani"> H. Merouani</a>, <a href="https://publications.waset.org/search?q=Y.%20Tlili."> Y. Tlili.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An effective method for the early detection of breast cancer is the mammographic screening. One of the most important signs of early breast cancer is the presence of microcalcifications. For the detection of microcalcification in a mammography image, we propose to conceive a multiagent system based on a dual irregular pyramid. An initial segmentation is obtained by an incremental approach; the result represents level zero of the pyramid. The edge information obtained by application of the Canny filter is taken into account to affine the segmentation. The edge-agents and region-agents cooper level by level of the pyramid by exploiting its various characteristics to provide the segmentation process convergence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Dual%20Pyramid" title="Dual Pyramid">Dual Pyramid</a>, <a href="https://publications.waset.org/search?q=Image%20Segmentation" title=" Image Segmentation"> Image Segmentation</a>, <a href="https://publications.waset.org/search?q=Multi-agent%0ASystem" title=" Multi-agent System"> Multi-agent System</a>, <a href="https://publications.waset.org/search?q=Region%2FEdge%20Cooperation." title=" Region/Edge Cooperation."> Region/Edge Cooperation.</a> </p> <a href="https://publications.waset.org/10867/dual-pyramid-of-agents-for-image-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10867/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10867/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10867/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10867/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10867/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10867/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10867/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10867/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10867/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10867/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10867.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1916</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1628</span> Deficiencies of Lung Segmentation Techniques using CT Scan Images for CAD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nisar%20Ahmed%20Memon">Nisar Ahmed Memon</a>, <a href="https://publications.waset.org/search?q=Anwar%20Majid%20Mirza"> Anwar Majid Mirza</a>, <a href="https://publications.waset.org/search?q=S.A.M.%20Gilani"> S.A.M. Gilani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Segmentation is an important step in medical image analysis and classification for radiological evaluation or computer aided diagnosis. This paper presents the problem of inaccurate lung segmentation as observed in algorithms presented by researchers working in the area of medical image analysis. The different lung segmentation techniques have been tested using the dataset of 19 patients consisting of a total of 917 images. We obtained datasets of 11 patients from Ackron University, USA and of 8 patients from AGA Khan Medical University, Pakistan. After testing the algorithms against datasets, the deficiencies of each algorithm have been highlighted. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Computer%20Aided%20Diagnosis%20%28CAD%29" title="Computer Aided Diagnosis (CAD)">Computer Aided Diagnosis (CAD)</a>, <a href="https://publications.waset.org/search?q=MathematicalMorphology" title=" MathematicalMorphology"> MathematicalMorphology</a>, <a href="https://publications.waset.org/search?q=Medical%20Image%20Analysis" title=" Medical Image Analysis"> Medical Image Analysis</a>, <a href="https://publications.waset.org/search?q=Region%20Growing" title=" Region Growing"> Region Growing</a>, <a href="https://publications.waset.org/search?q=Segmentation" title="Segmentation">Segmentation</a>, <a href="https://publications.waset.org/search?q=Thresholding" title=" Thresholding"> Thresholding</a>, <a href="https://publications.waset.org/search?q=" title=""></a> </p> <a href="https://publications.waset.org/6510/deficiencies-of-lung-segmentation-techniques-using-ct-scan-images-for-cad" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/6510/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/6510/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/6510/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/6510/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/6510/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/6510/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/6510/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/6510/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/6510/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/6510/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/6510.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2340</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1627</span> Medical Image Segmentation and Detection of MR Images Based on Spatial Multiple-Kernel Fuzzy C-Means Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=J.%20Mehena">J. Mehena</a>, <a href="https://publications.waset.org/search?q=M.%20C.%20Adhikary"> M. C. Adhikary</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a spatial multiple-kernel fuzzy C-means (SMKFCM) algorithm is introduced for segmentation problem. A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. The proposed SMKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in medical image segmentation and detection of MR images. To evaluate the robustness of the proposed segmentation algorithm in noisy environment, we add noise in medical brain tumor MR images and calculated the success rate and segmentation accuracy. From the experimental results it is clear that the proposed algorithm has better performance than those of other FCM based techniques for noisy medical MR images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering" title="Clustering">Clustering</a>, <a href="https://publications.waset.org/search?q=fuzzy%20C-means" title=" fuzzy C-means"> fuzzy C-means</a>, <a href="https://publications.waset.org/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/search?q=MR%0D%0Aimages" title=" MR images"> MR images</a>, <a href="https://publications.waset.org/search?q=multiple%20kernels." title=" multiple kernels."> multiple kernels.</a> </p> <a href="https://publications.waset.org/10002499/medical-image-segmentation-and-detection-of-mr-images-based-on-spatial-multiple-kernel-fuzzy-c-means-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10002499/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10002499/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10002499/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10002499/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10002499/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10002499/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10002499/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10002499/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10002499/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10002499/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10002499.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2129</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1626</span> Color Image Segmentation Using Competitive and Cooperative Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yinggan%20Tang">Yinggan Tang</a>, <a href="https://publications.waset.org/search?q=Xinping%20Guan"> Xinping Guan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Color image segmentation can be considered as a cluster procedure in feature space. k-means and its adaptive version, i.e. competitive learning approach are powerful tools for data clustering. But k-means and competitive learning suffer from several drawbacks such as dead-unit problem and need to pre-specify number of cluster. In this paper, we will explore to use competitive and cooperative learning approach to perform color image segmentation. In competitive and cooperative learning approach, seed points not only compete each other, but also the winner will dynamically select several nearest competitors to form a cooperative team to adapt to the input together, finally it can automatically select the correct number of cluster and avoid the dead-units problem. Experimental results show that CCL can obtain better segmentation result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Color%20image%20segmentation" title="Color image segmentation">Color image segmentation</a>, <a href="https://publications.waset.org/search?q=competitive%20learning" title=" competitive learning"> competitive learning</a>, <a href="https://publications.waset.org/search?q=cluster" title="cluster">cluster</a>, <a href="https://publications.waset.org/search?q=k-means%20algorithm" title=" k-means algorithm"> k-means algorithm</a>, <a href="https://publications.waset.org/search?q=competitive%20and%20cooperative%20learning." title=" competitive and cooperative learning."> competitive and cooperative learning.</a> </p> <a href="https://publications.waset.org/12161/color-image-segmentation-using-competitive-and-cooperative-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12161/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12161/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12161/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12161/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12161/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12161/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12161/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12161/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12161/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12161/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12161.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1616</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1625</span> An Improved C-Means Model for MRI Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ying%20Shen">Ying Shen</a>, <a href="https://publications.waset.org/search?q=Weihua%20Zhu"> Weihua Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Magnetic%20Resonance%20Image" title="Magnetic Resonance Image">Magnetic Resonance Image</a>, <a href="https://publications.waset.org/search?q=C-means%20model" title=" C-means model"> C-means model</a>, <a href="https://publications.waset.org/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/search?q=information%20entropy." title=" information entropy. "> information entropy. </a> </p> <a href="https://publications.waset.org/10008259/an-improved-c-means-model-for-mri-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10008259/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10008259/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10008259/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10008259/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10008259/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10008259/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10008259/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10008259/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10008259/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10008259/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10008259.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">918</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1624</span> A New Approach for Image Segmentation using Pillar-Kmeans Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ali%20Ridho%20Barakbah">Ali Ridho Barakbah</a>, <a href="https://publications.waset.org/search?q=Yasushi%20Kiyoki"> Yasushi Kiyoki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new approach for image segmentation by applying Pillar-Kmeans algorithm. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies K-means clustering to the image segmentation after optimized by Pillar Algorithm. The Pillar algorithm considers the pillars- placement which should be located as far as possible from each other to withstand against the pressure distribution of a roof, as identical to the number of centroids amongst the data distribution. This algorithm is able to optimize the K-means clustering for image segmentation in aspects of precision and computation time. It designates the initial centroids- positions by calculating the accumulated distance metric between each data point and all previous centroids, and then selects data points which have the maximum distance as new initial centroids. This algorithm distributes all initial centroids according to the maximum accumulated distance metric. This paper evaluates the proposed approach for image segmentation by comparing with K-means and Gaussian Mixture Model algorithm and involving RGB, HSV, HSL and CIELAB color spaces. The experimental results clarify the effectiveness of our approach to improve the segmentation quality in aspects of precision and computational time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20segmentation" title="Image segmentation">Image segmentation</a>, <a href="https://publications.waset.org/search?q=K-means%20clustering" title=" K-means clustering"> K-means clustering</a>, <a href="https://publications.waset.org/search?q=Pillaralgorithm" title=" Pillaralgorithm"> Pillaralgorithm</a>, <a href="https://publications.waset.org/search?q=color%20spaces." title=" color spaces."> color spaces.</a> </p> <a href="https://publications.waset.org/354/a-new-approach-for-image-segmentation-using-pillar-kmeans-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/354/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/354/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/354/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/354/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/354/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/354/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/354/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/354/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/354/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/354/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/354.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3372</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1623</span> A New Approach to Image Segmentation via Fuzzification of R猫nyi Entropy of Generalized Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Samy%20Sadek">Samy Sadek</a>, <a href="https://publications.waset.org/search?q=Ayoub%20Al-Hamadi"> Ayoub Al-Hamadi</a>, <a href="https://publications.waset.org/search?q=Axel%20Panning"> Axel Panning</a>, <a href="https://publications.waset.org/search?q=Bernd%20Michaelis"> Bernd Michaelis</a>, <a href="https://publications.waset.org/search?q=Usama%20Sayed"> Usama Sayed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a novel approach for image segmentation via fuzzification of R猫nyi Entropy of Generalized Distributions (REGD). The fuzzy REGD is used to precisely measure the structural information of image and to locate the optimal threshold desired by segmentation. The proposed approach draws upon the postulation that the optimal threshold concurs with maximum information content of the distribution. The contributions in the paper are as follow: Initially, the fuzzy REGD as a measure of the spatial structure of image is introduced. Then, we propose an efficient entropic segmentation approach using fuzzy REGD. However the proposed approach belongs to entropic segmentation approaches (i.e. these approaches are commonly applied to grayscale images), it is adapted to be viable for segmenting color images. Lastly, diverse experiments on real images that show the superior performance of the proposed method are carried out. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Entropy%20of%20generalized%20distributions" title="Entropy of generalized distributions">Entropy of generalized distributions</a>, <a href="https://publications.waset.org/search?q=entropy%0Afuzzification" title=" entropy fuzzification"> entropy fuzzification</a>, <a href="https://publications.waset.org/search?q=entropic%20image%20segmentation." title=" entropic image segmentation."> entropic image segmentation.</a> </p> <a href="https://publications.waset.org/5251/a-new-approach-to-image-segmentation-via-fuzzification-of-renyi-entropy-of-generalized-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5251/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5251/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5251/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5251/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5251/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5251/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5251/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5251/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5251/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5251/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5251.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3232</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1622</span> Genetic-Based Multi Resolution Noisy Color Image Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Raghad%20Jawad%20Ahmed">Raghad Jawad Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Segmentation of a color image composed of different kinds of regions can be a hard problem, namely to compute for an exact texture fields. The decision of the optimum number of segmentation areas in an image when it contains similar and/or un stationary texture fields. A novel neighborhood-based segmentation approach is proposed. A genetic algorithm is used in the proposed segment-pass optimization process. In this pass, an energy function, which is defined based on Markov Random Fields, is minimized. In this paper we use an adaptive threshold estimation method for image thresholding in the wavelet domain based on the generalized Gaussian distribution (GGD) modeling of sub band coefficients. This method called Normal Shrink is computationally more efficient and adaptive because the parameters required for estimating the threshold depend on sub band data energy that used in the pre-stage of segmentation. A quad tree is employed to implement the multi resolution framework, which enables the use of different strategies at different resolution levels, and hence, the computation can be accelerated. The experimental results using the proposed segmentation approach are very encouraging. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Color%20image%20segmentation" title="Color image segmentation">Color image segmentation</a>, <a href="https://publications.waset.org/search?q=Genetic%20algorithm" title=" Genetic algorithm"> Genetic algorithm</a>, <a href="https://publications.waset.org/search?q=Markov%20random%20field" title="Markov random field">Markov random field</a>, <a href="https://publications.waset.org/search?q=Scale%20space%20filter." title=" Scale space filter."> Scale space filter.</a> </p> <a href="https://publications.waset.org/11017/genetic-based-multi-resolution-noisy-color-image-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11017/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11017/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11017/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11017/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11017/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11017/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11017/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11017/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11017/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11017/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1576</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=55">55</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=56">56</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/search?q=image%0Asegmentation&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>