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: Medical Image</title> <meta name="description" content="Search results for: Medical Image"> <meta name="keywords" content="Medical Image"> <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="Medical Image" 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/abstracts/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="Medical Image"> <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> 5944</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Medical Image</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5944</span> Enhanced Visual Sharing Method for Medical Image Security</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalaivani%20Pachiappan">Kalaivani Pachiappan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sabari%20Annaji"> Sabari Annaji</a>, <a href="https://publications.waset.org/abstracts/search?q=Nithya%20Jayakumar"> Nithya Jayakumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, Information security has emerged as foremost challenges in many fields. Especially in medical information systems security is a major issue, in handling reports such as patients’ diagnosis and medical images. These sensitive data require confidentiality for transmission purposes. Image sharing is a secure and fault-tolerant method for protecting digital images, which can use the cryptography techniques to reduce the information loss. In this paper, visual sharing method is proposed which embeds the patient’s details into a medical image. Then the medical image can be divided into numerous shared images and protected by various users. The original patient details and medical image can be retrieved by gathering the shared images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20security" title="information security">information security</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20images" title=" medical images"> medical images</a>, <a href="https://publications.waset.org/abstracts/search?q=cryptography" title=" cryptography"> cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20sharing" title=" visual sharing"> visual sharing</a> </p> <a href="https://publications.waset.org/abstracts/2990/enhanced-visual-sharing-method-for-medical-image-security" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2990.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">414</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5943</span> Medical Images Enhancement Using New Dynamic Band Pass Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdellatif%20Baba">Abdellatif Baba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to facilitate medical images analysis by improving their quality and readability, we present in this paper a new dynamic band pass filter as a general and suitable operator for different types of medical images. Our objective is to enrich the details of any treated medical image to make it sufficiently clear enough to give an understood and simplified meaning even for unspecialized people in the medical domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20enhancement" title="medical image enhancement">medical image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20band%20pass%20filter" title=" dynamic band pass filter"> dynamic band pass filter</a>, <a href="https://publications.waset.org/abstracts/search?q=analysis%20improvement" title=" analysis improvement"> analysis improvement</a> </p> <a href="https://publications.waset.org/abstracts/14660/medical-images-enhancement-using-new-dynamic-band-pass-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14660.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">289</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5942</span> Medical Image Classification Using Legendre Multifractal Spectrum Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Korchiyne">R. Korchiyne</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sbihi"> A. Sbihi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Farssi"> S. M. Farssi</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Touahni"> R. Touahni</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Tahiri%20Alaoui"> M. Tahiri Alaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trabecular bone structure is important texture in the study of osteoporosis. Legendre multifractal spectrum can reflect the complex and self-similarity characteristic of structures. The main objective of this paper is to develop a new technique of medical image classification based on Legendre multifractal spectrum. Novel features have been developed from basic geometrical properties of this spectrum in a supervised image classification. The proposed method has been successfully used to classify medical images of bone trabeculations, and could be a useful supplement to the clinical observations for osteoporosis diagnosis. A comparative study with existing data reveals that the results of this approach are concordant. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multifractal%20analysis" title="multifractal analysis">multifractal analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20image" title=" medical image"> medical image</a>, <a href="https://publications.waset.org/abstracts/search?q=osteoporosis" title=" osteoporosis"> osteoporosis</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=Legendre%20spectrum" title=" Legendre spectrum"> Legendre spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20classification" title=" supervised classification"> supervised classification</a> </p> <a href="https://publications.waset.org/abstracts/15795/medical-image-classification-using-legendre-multifractal-spectrum-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15795.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">514</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5941</span> Objective Evaluation on Medical Image Compression Using Wavelet Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amhimmid%20Mohammed%20Saffour">Amhimmid Mohammed Saffour</a>, <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Mohamed%20Abdullah"> Mustafa Mohamed Abdullah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of computers for handling image data in the healthcare is growing. However, the amount of data produced by modern image generating techniques is vast. This data might be a problem from a storage point of view or when the data is sent over a network. This paper using wavelet transform technique for medical images compression. MATLAB program, are designed to evaluate medical images storage and transmission time problem at Sebha Medical Center Libya. In this paper, three different Computed Tomography images which are abdomen, brain and chest have been selected and compressed using wavelet transform. Objective evaluation has been performed to measure the quality of the compressed images. For this evaluation, the results show that the Peak Signal to Noise Ratio (PSNR) which indicates the quality of the compressed image is ranging from (25.89db to 34.35db for abdomen images, 23.26db to 33.3db for brain images and 25.5db to 36.11db for chest images. These values shows that the compression ratio is nearly to 30:1 is acceptable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20image" title="medical image">medical image</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab" title=" Matlab"> Matlab</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title=" image compression"> image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%27s" title=" wavelet's"> wavelet's</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20evaluation" title=" objective evaluation"> objective evaluation</a> </p> <a href="https://publications.waset.org/abstracts/45414/objective-evaluation-on-medical-image-compression-using-wavelet-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45414.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">285</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5940</span> GPU Accelerated Fractal Image Compression for Medical Imaging in Parallel Computing Platform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md.%20Enamul%20Haque">Md. Enamul Haque</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Al%20Kaisan"> Abdullah Al Kaisan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmudur%20R.%20Saniat"> Mahmudur R. Saniat</a>, <a href="https://publications.waset.org/abstracts/search?q=Aminur%20Rahman"> Aminur Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have implemented both sequential and parallel version of fractal image compression algorithms using CUDA (Compute Unified Device Architecture) programming model for parallelizing the program in Graphics Processing Unit for medical images, as they are highly similar within the image itself. There is several improvements in the implementation of the algorithm as well. Fractal image compression is based on the self similarity of an image, meaning an image having similarity in majority of the regions. We take this opportunity to implement the compression algorithm and monitor the effect of it using both parallel and sequential implementation. Fractal compression has the property of high compression rate and the dimensionless scheme. Compression scheme for fractal image is of two kinds, one is encoding and another is decoding. Encoding is very much computational expensive. On the other hand decoding is less computational. The application of fractal compression to medical images would allow obtaining much higher compression ratios. While the fractal magnification an inseparable feature of the fractal compression would be very useful in presenting the reconstructed image in a highly readable form. However, like all irreversible methods, the fractal compression is connected with the problem of information loss, which is especially troublesome in the medical imaging. A very time consuming encoding process, which can last even several hours, is another bothersome drawback of the fractal compression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerated%20GPU" title="accelerated GPU">accelerated GPU</a>, <a href="https://publications.waset.org/abstracts/search?q=CUDA" title=" CUDA"> CUDA</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20computing" title=" parallel computing"> parallel computing</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20image%20compression" title=" fractal image compression"> fractal image compression</a> </p> <a href="https://publications.waset.org/abstracts/5645/gpu-accelerated-fractal-image-compression-for-medical-imaging-in-parallel-computing-platform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5645.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">335</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5939</span> Medical Image Watermark and Tamper Detection Using Constant Correlation Spread Spectrum Watermarking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peter%20U.%20Eze">Peter U. Eze</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Udaya"> P. Udaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Robin%20J.%20Evans"> Robin J. Evans</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data hiding can be achieved by Steganography or invisible digital watermarking. For digital watermarking, both accurate retrieval of the embedded watermark and the integrity of the cover image are important. Medical image security in Teleradiology is one of the applications where the embedded patient record needs to be extracted with accuracy as well as the medical image integrity verified. In this research paper, the Constant Correlation Spread Spectrum digital watermarking for medical image tamper detection and accurate embedded watermark retrieval is introduced. In the proposed method, a watermark bit from a patient record is spread in a medical image sub-block such that the correlation of all watermarked sub-blocks with a spreading code, W, would have a constant value, <em>p.</em> The constant correlation <em>p</em>, spreading code, W and the size of the sub-blocks constitute the secret key. Tamper detection is achieved by flagging any sub-block whose correlation value deviates by more than a small value, ℇ, from <em>p</em>. The major features of our new scheme include: (1) Improving watermark detection accuracy for high-pixel depth medical images by reducing the Bit Error Rate (BER) to Zero and (2) block-level tamper detection in a single computational process with simultaneous watermark detection, thereby increasing utility with the same computational cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Constant%20Correlation" title="Constant Correlation">Constant Correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=Medical%20Image" title=" Medical Image"> Medical Image</a>, <a href="https://publications.waset.org/abstracts/search?q=Spread%20Spectrum" title=" Spread Spectrum"> Spread Spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=Tamper%20Detection" title=" Tamper Detection"> Tamper Detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Watermarking" title=" Watermarking"> Watermarking</a> </p> <a href="https://publications.waset.org/abstracts/84629/medical-image-watermark-and-tamper-detection-using-constant-correlation-spread-spectrum-watermarking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84629.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">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5938</span> Reversible and Adaptive Watermarking for MRI Medical Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nisar%20Ahmed%20Memon">Nisar Ahmed Memon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new medical image watermarking scheme delivering high embedding capacity is presented in this paper. Integer Wavelet Transform (IWT), Companding technique and adaptive thresholding are used in this scheme. The proposed scheme implants, recovers the hidden information and restores the input image to its pristine state at the receiving end. Magnetic Resonance Imaging (MRI) images are used for experimental purposes. The scheme first segment the MRI medical image into non-overlapping blocks and then inserts watermark into wavelet coefficients having a high frequency of each block. The scheme uses block-based watermarking adopting iterative optimization of threshold for companding in order to avoid the histogram pre and post processing. Results show that proposed scheme performs better than other reversible medical image watermarking schemes available in literature for MRI medical images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20thresholding" title="adaptive thresholding">adaptive thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=companding%20technique" title=" companding technique"> companding technique</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20authentication" title=" data authentication"> data authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20watermarking" title=" reversible watermarking"> reversible watermarking</a> </p> <a href="https://publications.waset.org/abstracts/57365/reversible-and-adaptive-watermarking-for-mri-medical-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57365.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">296</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5937</span> A Review on Medical Image Registration Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shadrack%20Mambo">Shadrack Mambo</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Djouani"> Karim Djouani</a>, <a href="https://publications.waset.org/abstracts/search?q=Yskandar%20Hamam"> Yskandar Hamam</a>, <a href="https://publications.waset.org/abstracts/search?q=Barend%20van%20Wyk"> Barend van Wyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Patrick%20Siarry"> Patrick Siarry</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses the current trends in medical image registration techniques and addresses the need to provide a solid theoretical foundation for research endeavours. Methodological analysis and synthesis of quality literature was done, providing a platform for developing a good foundation for research study in this field which is crucial in understanding the existing levels of knowledge. Research on medical image registration techniques assists clinical and medical practitioners in diagnosis of tumours and lesion in anatomical organs, thereby enhancing fast and accurate curative treatment of patients. Literature review aims to provide a solid theoretical foundation for research endeavours in image registration techniques. Developing a solid foundation for a research study is possible through a methodological analysis and synthesis of existing contributions. Out of these considerations, the aim of this paper is to enhance the scientific community’s understanding of the current status of research in medical image registration techniques and also communicate to them, the contribution of this research in the field of image processing. The gaps identified in current techniques can be closed by use of artificial neural networks that form learning systems designed to minimise error function. The paper also suggests several areas of future research in the image registration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20registration%20techniques" title="image registration techniques">image registration techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20images" title=" medical images"> medical images</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=optimisaztion" title=" optimisaztion"> optimisaztion</a>, <a href="https://publications.waset.org/abstracts/search?q=transformation" title=" transformation"> transformation</a> </p> <a href="https://publications.waset.org/abstracts/80442/a-review-on-medical-image-registration-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80442.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">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5936</span> A Modified Shannon Entropy Measure for Improved Image Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20A.%20U.%20Khan">Mohammad A. U. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Omar%20A.%20Kittaneh"> Omar A. Kittaneh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Akbar"> M. Akbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tariq%20M.%20Khan"> Tariq M. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Husam%20A.%20Bayoud"> Husam A. Bayoud </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Shannon Entropy measure has been widely used for measuring uncertainty. However, in partial settings, the histogram is used to estimate the underlying distribution. The histogram is dependent on the number of bins used. In this paper, a modification is proposed that makes the Shannon entropy based on histogram consistent. For providing the benefits, two application are picked in medical image processing applications. The simulations are carried out to show the superiority of this modified measure for image segmentation problem. The improvement may be contributed to robustness shown to uneven background in images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shannon%20entropy" title="Shannon entropy">Shannon entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20processing" title=" medical image processing"> medical image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=modification" title=" modification"> modification</a> </p> <a href="https://publications.waset.org/abstracts/19414/a-modified-shannon-entropy-measure-for-improved-image-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19414.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">497</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5935</span> A Hybrid Digital Watermarking Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nazish%20Saleem%20Abbas">Nazish Saleem Abbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Haris%20Jamil"> Muhammad Haris Jamil</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Sharif"> Hamid Sharif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Digital watermarking is a technique that allows an individual to add and hide secret information, copyright notice, or other verification message inside a digital audio, video, or image. Today, with the advancement of technology, modern healthcare systems manage patients’ diagnostic information in a digital way in many countries. When transmitted between hospitals through the internet, the medical data becomes vulnerable to attacks and requires security and confidentiality. Digital watermarking techniques are used in order to ensure the authenticity, security and management of medical images and related information. This paper proposes a watermarking technique that embeds a watermark in medical images imperceptibly and securely. In this work, digital watermarking on medical images is carried out using the Least Significant Bit (LSB) with the Discrete Cosine Transform (DCT). The proposed methods of embedding and extraction of a watermark in a watermarked image are performed in the frequency domain using LSB by XOR operation. The quality of the watermarked medical image is measured by the Peak signal-to-noise ratio (PSNR). It was observed that the watermarked medical image obtained performing XOR operation between DCT and LSB survived compression attack having a PSNR up to 38.98. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=watermarking" title="watermarking">watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT" title=" DCT"> DCT</a>, <a href="https://publications.waset.org/abstracts/search?q=LSB" title=" LSB"> LSB</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR" title=" PSNR"> PSNR</a> </p> <a href="https://publications.waset.org/abstracts/185946/a-hybrid-digital-watermarking-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185946.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">47</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5934</span> Medical Imaging Fusion: A Teaching-Learning Simulation Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cristina%20Maria%20Ribeiro%20Martins%20Pereira%20Caridade">Cristina Maria Ribeiro Martins Pereira Caridade</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Rita%20Ferreira%20Morais"> Ana Rita Ferreira Morais</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of computational tools has become essential in the context of interactive learning, especially in engineering education. In the medical industry, teaching medical image processing techniques is a crucial part of training biomedical engineers, as it has integrated applications with healthcare facilities and hospitals. The aim of this article is to present a teaching-learning simulation tool developed in MATLAB using a graphical user interface for medical image fusion that explores different image fusion methodologies and processes in combination with image pre-processing techniques. The application uses different algorithms and medical fusion techniques in real time, allowing you to view original images and fusion images, compare processed and original images, adjust parameters, and save images. The tool proposed in an innovative teaching and learning environment consists of a dynamic and motivating teaching simulation for biomedical engineering students to acquire knowledge about medical image fusion techniques and necessary skills for the training of biomedical engineers. In conclusion, the developed simulation tool provides real-time visualization of the original and fusion images and the possibility to test, evaluate and progress the student’s knowledge about the fusion of medical images. It also facilitates the exploration of medical imaging applications, specifically image fusion, which is critical in the medical industry. Teachers and students can make adjustments and/or create new functions, making the simulation environment adaptable to new techniques and methodologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching-learning%20simulation%20tool" title=" teaching-learning simulation tool"> teaching-learning simulation tool</a>, <a href="https://publications.waset.org/abstracts/search?q=biomedical%20engineering%20education" title=" biomedical engineering education"> biomedical engineering education</a> </p> <a href="https://publications.waset.org/abstracts/164987/medical-imaging-fusion-a-teaching-learning-simulation-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164987.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">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5933</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/abstracts/search?q=Mayssa%20Bensalah">Mayssa Bensalah</a>, <a href="https://publications.waset.org/abstracts/search?q=Atef%20Boujelben"> Atef Boujelben</a>, <a href="https://publications.waset.org/abstracts/search?q=Mouna%20Baklouti"> Mouna Baklouti</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Abid"> Mohamed Abid</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=features%20extraction" title="features extraction">features extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20images" title=" medical images"> medical images</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20detection" title=" tumor detection"> tumor detection</a> </p> <a href="https://publications.waset.org/abstracts/132616/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/abstracts/132616.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">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5932</span> Toward Automatic Chest CT Image Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Angely%20Sim%20Jia%20Wun">Angely Sim Jia Wun</a>, <a href="https://publications.waset.org/abstracts/search?q=Sasa%20Arsovski"> Sasa Arsovski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Numerous studies have been conducted on the segmentation of medical images. Segmenting the lungs is one of the common research topics in those studies. Our research stemmed from the lack of solutions for automatic bone, airway, and vessel segmentation, despite the existence of multiple lung segmentation techniques. Consequently, currently, available software tools used for medical image segmentation do not provide automatic lung, bone, airway, and vessel segmentation. This paper presents segmentation techniques along with an interactive software tool architecture for segmenting bone, lung, airway, and vessel tissues. Additionally, we propose a method for creating binary masks from automatically generated segments. The key contribution of our approach is the technique for automatic image thresholding using adjustable Hounsfield values and binary mask extraction. Generated binary masks can be successfully used as a training dataset for deep-learning solutions in medical image segmentation. In this paper, we also examine the current software tools used for medical image segmentation, discuss our approach, and identify its advantages. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lung%20segmentation" title="lung segmentation">lung segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20masks" title=" binary masks"> binary masks</a>, <a href="https://publications.waset.org/abstracts/search?q=U-Net" title=" U-Net"> U-Net</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20software%20tools" title=" medical software tools"> medical software tools</a> </p> <a href="https://publications.waset.org/abstracts/168342/toward-automatic-chest-ct-image-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168342.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">98</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5931</span> Design and Performance Analysis of Advanced B-Spline Algorithm for Image Resolution Enhancement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Z.%20Kurian">M. Z. Kurian</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Chidananda%20Murthy"> M. V. Chidananda Murthy</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20S.%20Guruprasad"> H. S. Guruprasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An approach to super-resolve the low-resolution (LR) image is presented in this paper which is very useful in multimedia communication, medical image enhancement and satellite image enhancement to have a clear view of the information in the image. The proposed Advanced B-Spline method generates a high-resolution (HR) image from single LR image and tries to retain the higher frequency components such as edges in the image. This method uses B-Spline technique and Crispening. This work is evaluated qualitatively and quantitatively using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The method is also suitable for real-time applications. Different combinations of decimation and super-resolution algorithms in the presence of different noise and noise factors are tested. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=advanced%20b-spline" title="advanced b-spline">advanced b-spline</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20super-resolution" title=" image super-resolution"> image super-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20square%20error%20%28MSE%29" title=" mean square error (MSE)"> mean square error (MSE)</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20signal%20to%20noise%20ratio%20%28PSNR%29" title=" peak signal to noise ratio (PSNR)"> peak signal to noise ratio (PSNR)</a>, <a href="https://publications.waset.org/abstracts/search?q=resolution%20down%20converter" title=" resolution down converter"> resolution down converter</a> </p> <a href="https://publications.waset.org/abstracts/59499/design-and-performance-analysis-of-advanced-b-spline-algorithm-for-image-resolution-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59499.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">399</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5930</span> GPU Based High Speed Error Protection for Watermarked Medical Image Transmission</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md%20Shohidul%20Islam">Md Shohidul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Jongmyon%20Kim"> Jongmyon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ui-pil%20Chong"> Ui-pil Chong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Medical image is an integral part of e-health care and e-diagnosis system. Medical image watermarking is widely used to protect patients’ information from malicious alteration and manipulation. The watermarked medical images are transmitted over the internet among patients, primary and referred physicians. The images are highly prone to corruption in the wireless transmission medium due to various noises, deflection, and refractions. Distortion in the received images leads to faulty watermark detection and inappropriate disease diagnosis. To address the issue, this paper utilizes error correction code (ECC) with (8, 4) Hamming code in an existing watermarking system. In addition, we implement the high complex ECC on a graphics processing units (GPU) to accelerate and support real-time requirement. Experimental results show that GPU achieves considerable speedup over the sequential CPU implementation, while maintaining 100% ECC efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20watermarking" title="medical image watermarking">medical image watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=e-health%20system" title=" e-health system"> e-health system</a>, <a href="https://publications.waset.org/abstracts/search?q=error%20correction" title=" error correction"> error correction</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamming%20code" title=" Hamming code"> Hamming code</a>, <a href="https://publications.waset.org/abstracts/search?q=GPU" title=" GPU"> GPU</a> </p> <a href="https://publications.waset.org/abstracts/5248/gpu-based-high-speed-error-protection-for-watermarked-medical-image-transmission" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5248.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">290</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5929</span> Security System for Safe Transmission of Medical Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Jamal%20Al-Mansor">Mohammed Jamal Al-Mansor</a>, <a href="https://publications.waset.org/abstracts/search?q=Kok%20Beng%20Gan"> Kok Beng Gan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper develops an optimized embedding of payload in medical image by using genetic optimization. The goal is to preserve region of interest from being distorted because of the watermark. By using this developed system there is no need of manual defining of region of interest through experts as the system will apply the genetic optimization to select the parts of image that can carry the watermark with guaranteeing less distortion. The experimental results assure that genetic based optimization is useful for performing steganography with less mean square error percentage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AES" title="AES">AES</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=watermarking" title=" watermarking"> watermarking</a> </p> <a href="https://publications.waset.org/abstracts/52270/security-system-for-safe-transmission-of-medical-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52270.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">411</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5928</span> Detecting the Edge of Multiple Images in Parallel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prakash%20K.%20Aithal">Prakash K. Aithal</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20Dinesh%20Acharya"> U. Dinesh Acharya</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Gopakumar"> Rajesh Gopakumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Edge is variation of brightness in an image. Edge detection is useful in many application areas such as finding forests, rivers from a satellite image, detecting broken bone in a medical image etc. The paper discusses about finding edge of multiple aerial images in parallel .The proposed work tested on 38 images 37 colored and one monochrome image. The time taken to process N images in parallel is equivalent to time taken to process 1 image in sequential. The proposed method achieves pixel level parallelism as well as image level parallelism. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title="edge detection">edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=multicore" title=" multicore"> multicore</a>, <a href="https://publications.waset.org/abstracts/search?q=gpu" title=" gpu"> gpu</a>, <a href="https://publications.waset.org/abstracts/search?q=opencl" title=" opencl"> opencl</a>, <a href="https://publications.waset.org/abstracts/search?q=mpi" title=" mpi"> mpi</a> </p> <a href="https://publications.waset.org/abstracts/30818/detecting-the-edge-of-multiple-images-in-parallel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30818.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">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5927</span> Image Compression on Region of Interest Based on SPIHT Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sudeepti%20Dayal">Sudeepti Dayal</a>, <a href="https://publications.waset.org/abstracts/search?q=Neelesh%20Gupta"> Neelesh Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image abbreviation is utilized for reducing the size of a file without demeaning the quality of the image to an objectionable level. The depletion in file size permits more images to be deposited in a given number of spaces. It also minimizes the time necessary for images to be transferred. Storage of medical images is a most researched area in the current scenario. To store a medical image, there are two parameters on which the image is divided, regions of interest and non-regions of interest. The best way to store an image is to compress it in such a way that no important information is lost. Compression can be done in two ways, namely lossy, and lossless compression. Under that, several compression algorithms are applied. In the paper, two algorithms are used which are, discrete cosine transform, applied to non-region of interest (lossy), and discrete wavelet transform, applied to regions of interest (lossless). The paper introduces SPIHT (set partitioning hierarchical tree) algorithm which is applied onto the wavelet transform to obtain good compression ratio from which an image can be stored efficiently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Compression%20ratio" title="Compression ratio">Compression ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=SPIHT" title=" SPIHT"> SPIHT</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT" title=" DCT"> DCT</a> </p> <a href="https://publications.waset.org/abstracts/43377/image-compression-on-region-of-interest-based-on-spiht-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43377.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">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5926</span> Multi-Spectral Medical Images Enhancement Using a Weber’s law</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muna%20F.%20Al-Sammaraie">Muna F. Al-Sammaraie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this research is to present a multi spectral image enhancement methods used to achieve highly real digital image populates only a small portion of the available range of digital values. Also, a quantitative measure of image enhancement is presented. This measure is related with concepts of the Webers Low of the human visual system. For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied. This study involves changing the original values so that more of the available range is used; then increases the contrast between features and their backgrounds. It consists of reading the binary image on the basis of pixels taking them byte-wise and displaying it, calculating the statistics of an image, automatically enhancing the color of the image based on statistics calculation using algorithms and working with RGB color bands. Finally, the enhanced image is displayed along with image histogram. A number of experimental results illustrated the performance of these algorithms. Particularly the quantitative measure has helped to select optimal processing parameters: the best parameters and transform. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title="image enhancement">image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-spectral" title=" multi-spectral"> multi-spectral</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB" title=" RGB"> RGB</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram" title=" histogram"> histogram</a> </p> <a href="https://publications.waset.org/abstracts/8574/multi-spectral-medical-images-enhancement-using-a-webers-law" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8574.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">328</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5925</span> Design and Implementation of Image Super-Resolution for Myocardial Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Chidananda%20Murthy">M. V. Chidananda Murthy</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Z.%20Kurian"> M. Z. Kurian</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20S.%20Guruprasad"> H. S. Guruprasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Super-resolution is the technique of intelligently upscaling images, avoiding artifacts or blurring, and deals with the recovery of a high-resolution image from one or more low-resolution images. Single-image super-resolution is a process of obtaining a high-resolution image from a set of low-resolution observations by signal processing. While super-resolution has been demonstrated to improve image quality in scaled down images in the image domain, its effects on the Fourier-based technique remains unknown. Super-resolution substantially improved the spatial resolution of the patient LGE images by sharpening the edges of the heart and the scar. This paper aims at investigating the effects of single image super-resolution on Fourier-based and image based methods of scale-up. In this paper, first, generate a training phase of the low-resolution image and high-resolution image to obtain dictionary. In the test phase, first, generate a patch and then difference of high-resolution image and interpolation image from the low-resolution image. Next simulation of the image is obtained by applying convolution method to the dictionary creation image and patch extracted the image. Finally, super-resolution image is obtained by combining the fused image and difference of high-resolution and interpolated image. Super-resolution reduces image errors and improves the image quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20dictionary%20creation" title="image dictionary creation">image dictionary creation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20super-resolution" title=" image super-resolution"> image super-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=LGE%20images" title=" LGE images"> LGE images</a>, <a href="https://publications.waset.org/abstracts/search?q=patch%20extraction" title=" patch extraction"> patch extraction</a> </p> <a href="https://publications.waset.org/abstracts/59494/design-and-implementation-of-image-super-resolution-for-myocardial-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59494.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">375</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5924</span> Robust Medical Image Watermarking based on Contourlet and Extraction Using ICA </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Saju">S. Saju</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Thirugnanam"> G. Thirugnanam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a medical image watermarking algorithm based on contourlet is proposed. Medical image watermarking is a special subcategory of image watermarking in the sense that images have special requirements. Watermarked medical images should not differ perceptually from their original counterparts because clinical reading of images must not be affected. Watermarking techniques based on wavelet transform are reported in many literatures but robustness and security using contourlet are better when compared to wavelet transform. The main challenge in exploring geometry in images comes from the discrete nature of the data. In this paper, original image is decomposed to two level using contourlet and the watermark is embedded in the resultant sub-bands. Sub-band selection is based on the value of Peak Signal to Noise Ratio (PSNR) that is calculated between watermarked and original image. To extract the watermark, Kernel ICA is used and it has a novel characteristic is that it does not require the transformation process to extract the watermark. Simulation results show that proposed scheme is robust against attacks such as Salt and Pepper noise, Median filtering and rotation. The performance measures like PSNR and Similarity measure are evaluated and compared with Discrete Wavelet Transform (DWT) to prove the robustness of the scheme. Simulations are carried out using Matlab Software. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20watermarking" title="digital watermarking">digital watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis" title=" independent component analysis"> independent component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=contourlet" title=" contourlet"> contourlet</a> </p> <a href="https://publications.waset.org/abstracts/21817/robust-medical-image-watermarking-based-on-contourlet-and-extraction-using-ica" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21817.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">528</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5923</span> An Image Stitching Approach for Scoliosis Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siti%20Salbiah%20Samsudin">Siti Salbiah Samsudin</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamzah%20Arof"> Hamzah Arof</a>, <a href="https://publications.waset.org/abstracts/search?q=Ainuddin%20Wahid%20Abdul%20Wahab"> Ainuddin Wahid Abdul Wahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Yamani%20Idna%20Idris"> Mohd Yamani Idna Idris</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Standard X-ray spine images produced by conventional screen-film technique have a limited field of view. This limitation may obstruct a complete inspection of the spine unless images of different parts of the spine are placed next to each other contiguously to form a complete structure. Another solution to producing a whole spine image is by assembling the digitized x-ray images of its parts automatically using image stitching. This paper presents a new Medical Image Stitching (MIS) method that utilizes Minimum Average Correlation Energy (MACE) filters to identify and merge pairs of x-ray medical images. The effectiveness of the proposed method is demonstrated in two sets of experiments involving two databases which contain a total of 40 pairs of overlapping and non-overlapping spine images. The experimental results are compared to those produced by the Normalized Cross Correlation (NCC) and Phase Only Correlation (POC) methods for comparison. It is found that the proposed method outperforms those of the NCC and POC methods in identifying both the overlapping and non-overlapping medical images. The efficacy of the proposed method is further vindicated by its average execution time which is about two to five times shorter than those of the POC and NCC methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20stitching" title="image stitching">image stitching</a>, <a href="https://publications.waset.org/abstracts/search?q=MACE%20filter" title=" MACE filter"> MACE filter</a>, <a href="https://publications.waset.org/abstracts/search?q=panorama%20image" title=" panorama image"> panorama image</a>, <a href="https://publications.waset.org/abstracts/search?q=scoliosis" title=" scoliosis"> scoliosis</a> </p> <a href="https://publications.waset.org/abstracts/21723/an-image-stitching-approach-for-scoliosis-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21723.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">458</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5922</span> Medical Image Compression Based on Region of Interest: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sudeepti%20Dayal">Sudeepti Dayal</a>, <a href="https://publications.waset.org/abstracts/search?q=Neelesh%20Gupta"> Neelesh Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In terms of transmission, bigger the size of any image, longer the time the channel takes for transmission. It is understood that the bandwidth of the channel is fixed. Therefore, if the size of an image is reduced, a larger number of data or images can be transmitted over the channel. Compression is the technique used to reduce the size of an image. In terms of storage, compression reduces the file size which it occupies on the disk. Any image is based on two parameters, region of interest and non-region of interest. There are several algorithms of compression that compress the data more economically. In this paper we have reviewed region of interest and non-region of interest based compression techniques and the algorithms which compress the image most efficiently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compression%20ratio" title="compression ratio">compression ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20of%20interest" title=" region of interest"> region of interest</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT" title=" DCT"> DCT</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a> </p> <a href="https://publications.waset.org/abstracts/43380/medical-image-compression-based-on-region-of-interest-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43380.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">374</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5921</span> Robust Medical Image Watermarking Using Frequency Domain and Least Significant Bits Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Volkan%20Kaya">Volkan Kaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Ersin%20Elbasi"> Ersin Elbasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Watermarking and stenography are getting importance recently because of copyright protection and authentication. In watermarking we embed stamp, logo, noise or image to multimedia elements such as image, video, audio, animation and text. There are several works have been done in watermarking for different purposes. In this research work, we used watermarking techniques to embed patient information into the medical magnetic resonance (MR) images. There are two methods have been used; frequency domain (Digital Wavelet Transform-DWT, Digital Cosine Transform-DCT, and Digital Fourier Transform-DFT) and spatial domain (Least Significant Bits-LSB) domain. Experimental results show that embedding in frequency domains resist against one type of attacks, and embedding in spatial domain is resist against another group of attacks. Peak Signal Noise Ratio (PSNR) and Similarity Ratio (SR) values are two measurement values for testing. These two values give very promising result for information hiding in medical MR images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=watermarking" title="watermarking">watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20image" title=" medical image"> medical image</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20domain" title=" frequency domain"> frequency domain</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20significant%20bits" title=" least significant bits"> least significant bits</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a> </p> <a href="https://publications.waset.org/abstracts/75214/robust-medical-image-watermarking-using-frequency-domain-and-least-significant-bits-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75214.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">287</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5920</span> A Method of the Semantic on Image Auto-Annotation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lin%20Huo">Lin Huo</a>, <a href="https://publications.waset.org/abstracts/search?q=Xianwei%20Liu"> Xianwei Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jingxiong%20Zhou"> Jingxiong Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, due to the existence of semantic gap between image visual features and human concepts, the semantic of image auto-annotation has become an important topic. Firstly, by extract low-level visual features of the image, and the corresponding Hash method, mapping the feature into the corresponding Hash coding, eventually, transformed that into a group of binary string and store it, image auto-annotation by search is a popular method, we can use it to design and implement a method of image semantic auto-annotation. Finally, Through the test based on the Corel image set, and the results show that, this method is effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20auto-annotation" title="image auto-annotation">image auto-annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20correlograms" title=" color correlograms"> color correlograms</a>, <a href="https://publications.waset.org/abstracts/search?q=Hash%20code" title=" Hash code"> Hash code</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval" title=" image retrieval"> image retrieval</a> </p> <a href="https://publications.waset.org/abstracts/15628/a-method-of-the-semantic-on-image-auto-annotation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15628.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">497</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5919</span> Image Segmentation Techniques: Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lindani%20Mbatha">Lindani Mbatha</a>, <a href="https://publications.waset.org/abstracts/search?q=Suvendi%20Rimer"> Suvendi Rimer</a>, <a href="https://publications.waset.org/abstracts/search?q=Mpho%20Gololo"> Mpho Gololo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image segmentation is the process of dividing an image into several sections, such as the object's background and the foreground. It is a critical technique in both image-processing tasks and computer vision. Most of the image segmentation algorithms have been developed for gray-scale images and little research and algorithms have been developed for the color images. Most image segmentation algorithms or techniques vary based on the input data and the application. Nearly all of the techniques are not suitable for noisy environments. Most of the work that has been done uses the Markov Random Field (MRF), which involves the computations and is said to be robust to noise. In the past recent years' image segmentation has been brought to tackle problems such as easy processing of an image, interpretation of the contents of an image, and easy analysing of an image. This article reviews and summarizes some of the image segmentation techniques and algorithms that have been developed in the past years. The techniques include neural networks (CNN), edge-based techniques, region growing, clustering, and thresholding techniques and so on. The advantages and disadvantages of medical ultrasound image segmentation techniques are also discussed. The article also addresses the applications and potential future developments that can be done around image segmentation. This review article concludes with the fact that no technique is perfectly suitable for the segmentation of all different types of images, but the use of hybrid techniques yields more accurate and efficient results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering-based" title="clustering-based">clustering-based</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution-network" title=" convolution-network"> convolution-network</a>, <a href="https://publications.waset.org/abstracts/search?q=edge-based" title=" edge-based"> edge-based</a>, <a href="https://publications.waset.org/abstracts/search?q=region-growing" title=" region-growing"> region-growing</a> </p> <a href="https://publications.waset.org/abstracts/166513/image-segmentation-techniques-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166513.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">96</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5918</span> Medical Image Augmentation Using Spatial Transformations for Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Trupti%20Chavan">Trupti Chavan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramachandra%20Guda"> Ramachandra Guda</a>, <a href="https://publications.waset.org/abstracts/search?q=Kameshwar%20Rao"> Kameshwar Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The lack of data is a pain problem in medical image analysis using a convolutional neural network (CNN). This work uses various spatial transformation techniques to address the medical image augmentation issue for knee detection and localization using an enhanced single shot detector (SSD) network. The spatial transforms like a negative, histogram equalization, power law, sharpening, averaging, gaussian blurring, etc. help to generate more samples, serve as pre-processing methods, and highlight the features of interest. The experimentation is done on the OpenKnee dataset which is a collection of knee images from the openly available online sources. The CNN called enhanced single shot detector (SSD) is utilized for the detection and localization of the knee joint from a given X-ray image. It is an enhanced version of the famous SSD network and is modified in such a way that it will reduce the number of prediction boxes at the output side. It consists of a classification network (VGGNET) and an auxiliary detection network. The performance is measured in mean average precision (mAP), and 99.96% mAP is achieved using the proposed enhanced SSD with spatial transformations. It is also seen that the localization boundary is comparatively more refined and closer to the ground truth in spatial augmentation and gives better detection and localization of knee joints. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title="data augmentation">data augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=enhanced%20SSD" title=" enhanced SSD"> enhanced SSD</a>, <a href="https://publications.waset.org/abstracts/search?q=knee%20detection%20and%20localization" title=" knee detection and localization"> knee detection and localization</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20analysis" title=" medical image analysis"> medical image analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=openKnee" title=" openKnee"> openKnee</a>, <a href="https://publications.waset.org/abstracts/search?q=Spatial%20transformations" title=" Spatial transformations"> Spatial transformations</a> </p> <a href="https://publications.waset.org/abstracts/122628/medical-image-augmentation-using-spatial-transformations-for-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122628.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">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5917</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/abstracts/search?q=Ying%20Shen">Ying Shen</a>, <a href="https://publications.waset.org/abstracts/search?q=Weihua%20Zhu"> Weihua Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20image%20%28MRI%29" title="magnetic resonance image (MRI)">magnetic resonance image (MRI)</a>, <a href="https://publications.waset.org/abstracts/search?q=c-means%20model" title=" c-means model"> c-means model</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20entropy" title=" information entropy"> information entropy</a> </p> <a href="https://publications.waset.org/abstracts/79824/an-improved-c-means-model-for-mri-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79824.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">225</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5916</span> Design of a Graphical User Interface for Data Preprocessing and Image Segmentation Process in 2D MRI Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Enver%20Kucukkulahli">Enver Kucukkulahli</a>, <a href="https://publications.waset.org/abstracts/search?q=Pakize%20Erdogmus"> Pakize Erdogmus</a>, <a href="https://publications.waset.org/abstracts/search?q=Kemal%20Polat"> Kemal Polat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The 2D image segmentation is a significant process in finding a suitable region in medical images such as MRI, PET, CT etc. In this study, we have focused on 2D MRI images for image segmentation process. We have designed a GUI (graphical user interface) written in MATLABTM for 2D MRI images. In this program, there are two different interfaces including data pre-processing and image clustering or segmentation. In the data pre-processing section, there are median filter, average filter, unsharp mask filter, Wiener filter, and custom filter (a filter that is designed by user in MATLAB). As for the image clustering, there are seven different image segmentations for 2D MR images. These image segmentation algorithms are as follows: PSO (particle swarm optimization), GA (genetic algorithm), Lloyds algorithm, k-means, the combination of Lloyds and k-means, mean shift clustering, and finally BBO (Biogeography Based Optimization). To find the suitable cluster number in 2D MRI, we have designed the histogram based cluster estimation method and then applied to these numbers to image segmentation algorithms to cluster an image automatically. Also, we have selected the best hybrid method for each 2D MR images thanks to this GUI software. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title="image segmentation">image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=GUI" title=" GUI"> GUI</a>, <a href="https://publications.waset.org/abstracts/search?q=2D%20MRI" title=" 2D MRI "> 2D MRI </a> </p> <a href="https://publications.waset.org/abstracts/68097/design-of-a-graphical-user-interface-for-data-preprocessing-and-image-segmentation-process-in-2d-mri-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68097.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">377</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5915</span> Image Denoising Using Spatial Adaptive Mask Filter for Medical Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Sumalatha">R. Sumalatha</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Subramanyam"> M. V. Subramanyam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In medical image processing the quality of the image is degraded in the presence of noise. Especially in ultra sound imaging and Magnetic resonance imaging the data was corrupted by signal dependent noise known as salt and pepper noise. Removal of noise from the medical images is a critical issue for researchers. In this paper, a new type of technique Adaptive Spatial Mask Filter (ASMF) has been proposed. The proposed filter is used to increase the quality of MRI and ultra sound images. Experimental results show that the proposed filter outperforms the implementation of mean, median, adaptive median filters in terms of MSE and PSNR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=salt%20and%20pepper%20noise" title="salt and pepper noise">salt and pepper noise</a>, <a href="https://publications.waset.org/abstracts/search?q=ASMF" title=" ASMF"> ASMF</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR" title=" PSNR"> PSNR</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a> </p> <a href="https://publications.waset.org/abstracts/3843/image-denoising-using-spatial-adaptive-mask-filter-for-medical-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3843.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">436</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/abstracts/search?q=Medical%20Image&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Medical%20Image&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Medical%20Image&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Medical%20Image&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Medical%20Image&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Medical%20Image&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Medical%20Image&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Medical%20Image&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Medical%20Image&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/abstracts/search?q=Medical%20Image&page=198">198</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Medical%20Image&page=199">199</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Medical%20Image&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>