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segmentation</title> <meta name="description" content="Search results for: liver segmentation"> <meta name="keywords" content="liver 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 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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="liver 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> 1165</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: liver segmentation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1165</span> Computer-Aided Detection of Liver and Spleen from CT Scans using Watershed Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belgherbi%20Aicha">Belgherbi Aicha</a>, <a href="https://publications.waset.org/abstracts/search?q=Bessaid%20Abdelhafid"> Bessaid Abdelhafid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the recent years a great deal of research work has been devoted to the development of semi-automatic and automatic techniques for the analysis of abdominal CT images. The first and fundamental step in all these studies is the semi-automatic liver and spleen segmentation that is still an open problem. In this paper, a semi-automatic liver and spleen segmentation method by the mathematical morphology based on watershed algorithm has been proposed. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological to extract the liver and spleen. The second step consists to improve the quality of the image gradient. In this step, we propose a method for improving the image gradient to reduce the over-segmentation problem by applying the spatial filters followed by the morphological filters. Thereafter we proceed to the segmentation of the liver, spleen. The aim of this work is to develop a method for semi-automatic segmentation liver and spleen based on watershed algorithm, improve the accuracy and the robustness of the liver and spleen segmentation and evaluate a new semi-automatic approach with the manual for liver segmentation. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work. The system has been evaluated by computing the sensitivity and specificity between the semi-automatically segmented (liver and spleen) contour and the manually contour traced by radiological experts. Liver segmentation has achieved the sensitivity and specificity; sens Liver=96% and specif Liver=99% respectively. Spleen segmentation achieves similar, promising results sens Spleen=95% and specif Spleen=99%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CT%20images" title="CT images">CT images</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20and%20spleen%20segmentation" title=" liver and spleen segmentation"> liver and spleen segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20diffusion%20filter" title=" anisotropic diffusion filter"> anisotropic diffusion filter</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20filters" title=" morphological filters"> morphological filters</a>, <a href="https://publications.waset.org/abstracts/search?q=watershed%20algorithm" title=" watershed algorithm"> watershed algorithm</a> </p> <a href="https://publications.waset.org/abstracts/7381/computer-aided-detection-of-liver-and-spleen-from-ct-scans-using-watershed-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7381.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">325</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">1164</span> Liver and Liver Lesion Segmentation From Abdominal CT Scans</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belgherbi%20Aicha">Belgherbi Aicha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadjidj%20Ismahen"> Hadjidj Ismahen</a>, <a href="https://publications.waset.org/abstracts/search?q=Bessaid%20Abdelhafid"> Bessaid Abdelhafid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The interpretation of medical images benefits from anatomical and physiological priors to optimize computer- aided diagnosis applications. Segmentation of liver and liver lesion is regarded as a major primary step in computer aided diagnosis of liver diseases. Precise liver segmentation in abdominal CT images is one of the most important steps for the computer-aided diagnosis of liver pathology. In this papers, a semi- automated method for medical image data is presented for the liver and liver lesion segmentation data using mathematical morphology. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological filters to extract the liver. The second step consists to detect the liver lesion. In this task; we proposed a new method developed for the semi-automatic segmentation of the liver and hepatic lesions. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to improve the quality of the original image and image gradient by applying the spatial filter followed by the morphological filters. The second step consists to calculate the internal and external markers of the liver and hepatic lesions. Thereafter we proceed to the liver and hepatic lesions segmentation by the watershed transform controlled by markers. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20diffusion%20filter" title="anisotropic diffusion filter">anisotropic diffusion filter</a>, <a href="https://publications.waset.org/abstracts/search?q=CT%20images" title=" CT images"> CT images</a>, <a href="https://publications.waset.org/abstracts/search?q=hepatic%20lesion%20segmentation" title=" hepatic lesion segmentation"> hepatic lesion segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=Liver%20segmentation" title=" Liver segmentation"> Liver segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20filter" title=" morphological filter"> morphological filter</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20watershed%20algorithm" title=" the watershed algorithm"> the watershed algorithm</a> </p> <a href="https://publications.waset.org/abstracts/20381/liver-and-liver-lesion-segmentation-from-abdominal-ct-scans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20381.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">451</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">1163</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/abstracts/search?q=P.%20Strakos">P. Strakos</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Jaros"> M. Jaros</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Karasek"> T. Karasek</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kozubek"> T. Kozubek</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Vavra"> P. Vavra</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Jonszta"> T. Jonszta</a> </p> <p class="card-text"><strong>Abstract:</strong></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 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=semi-automatic" title=" semi-automatic"> semi-automatic</a>, <a href="https://publications.waset.org/abstracts/search?q=software" title=" software"> software</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20volumetric%20reconstruction" title=" 3D volumetric reconstruction"> 3D volumetric reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/23701/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/abstracts/23701.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">1162</span> Segmentation of Liver Using Random Forest Classifier </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gajendra%20Kumar%20%20Mourya">Gajendra Kumar Mourya</a>, <a href="https://publications.waset.org/abstracts/search?q=Dinesh%20%20Bhatia"> Dinesh Bhatia</a>, <a href="https://publications.waset.org/abstracts/search?q=Akash%20%20Handique"> Akash Handique</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunita%20Warjri"> Sunita Warjri</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Achaab%20Amir"> Syed Achaab Amir </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, Medical imaging has become an integral part of modern healthcare. Abdominal CT images are an invaluable mean for abdominal organ investigation and have been widely studied in the recent years. Diagnosis of liver pathologies is one of the major areas of current interests in the field of medical image processing and is still an open problem. To deeply study and diagnose the liver, segmentation of liver is done to identify which part of the liver is mostly affected. Manual segmentation of the liver in CT images is time-consuming and suﬀers from inter- and intra-observer diﬀerences. However, automatic or semi-automatic computer aided segmentation of the Liver is a challenging task due to inter-patient Liver shape and size variability. In this paper, we present a technique for automatic segmenting the liver from CT images using Random Forest Classifier. Random forests or random decision forests are an ensemble learning method for classification that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the individual trees. After comparing with various other techniques, it was found that Random Forest Classifier provide a better segmentation results with respect to accuracy and speed. We have done the validation of our results using various techniques and it shows above 89% accuracy in all the cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CT%20images" title="CT images">CT images</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20validation" title=" image validation"> image validation</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/77535/segmentation-of-liver-using-random-forest-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77535.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">313</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">1161</span> Segmentation of the Liver and Spleen From Abdominal CT Images Using Watershed Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belgherbi%20Aicha">Belgherbi Aicha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadjidj%20Ismahen"> Hadjidj Ismahen</a>, <a href="https://publications.waset.org/abstracts/search?q=Bessaid%20Abdelhafid"> Bessaid Abdelhafid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The phase of segmentation is an important step in the processing and interpretation of medical images. In this paper, we focus on the segmentation of liver and spleen from the abdomen computed tomography (CT) images. The importance of our study comes from the fact that the segmentation of ROI from CT images is usually a difficult task. This difficulty is the gray’s level of which is similar to the other organ also the ROI are connected to the ribs, heart, kidneys, etc. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to remove the surrounding and connected organs and tissues by applying morphological filters. This first step makes the extraction of interest regions easier. The second step consists of improving the quality of the image gradient. In this step, we propose a method for improving the image gradient to reduce these deficiencies by applying the spatial filters followed by the morphological filters. Thereafter we proceed to the segmentation of the liver, spleen. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.The system has been evaluated by computing the sensitivity and specificity between the semi-automatically segmented (liver and spleen) contour and the manually contour traced by radiological experts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CT%20images" title="CT images">CT images</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20and%20spleen%20segmentation" title=" liver and spleen segmentation"> liver and spleen segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20diffusion%20filter" title=" anisotropic diffusion filter"> anisotropic diffusion filter</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20filters" title=" morphological filters"> morphological filters</a>, <a href="https://publications.waset.org/abstracts/search?q=watershed%20algorithm" title=" watershed algorithm"> watershed algorithm</a> </p> <a href="https://publications.waset.org/abstracts/19950/segmentation-of-the-liver-and-spleen-from-abdominal-ct-images-using-watershed-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19950.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">495</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">1160</span> Abdominal Organ Segmentation in CT Images Based On Watershed Transform and Mosaic Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belgherbi%20Aicha">Belgherbi Aicha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadjidj%20Ismahen"> Hadjidj Ismahen</a>, <a href="https://publications.waset.org/abstracts/search?q=Bessaid%20Abdelhafid"> Bessaid Abdelhafid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate Liver, spleen and kidneys segmentation in abdominal CT images is one of the most important steps for computer aided abdominal organs pathology diagnosis. In this paper, we have proposed a new semi-automatic algorithm for Liver, spleen and kidneys area extraction in abdominal CT images. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. The algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20diffusion%20filter" title="anisotropic diffusion filter">anisotropic diffusion filter</a>, <a href="https://publications.waset.org/abstracts/search?q=CT%20images" title=" CT images"> CT images</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20filter" title=" morphological filter"> morphological filter</a>, <a href="https://publications.waset.org/abstracts/search?q=mosaic%20image" title=" mosaic image"> mosaic image</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-abdominal%20organ%20segmentation" title=" multi-abdominal organ segmentation"> multi-abdominal organ segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=mosaic%20image" title=" mosaic image"> mosaic image</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20watershed%20algorithm" title=" the watershed algorithm"> the watershed algorithm</a> </p> <a href="https://publications.waset.org/abstracts/20011/abdominal-organ-segmentation-in-ct-images-based-on-watershed-transform-and-mosaic-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20011.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">499</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">1159</span> Diagnosis and Analysis of Automated Liver and Tumor Segmentation on CT</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20R.%20Ramsheeja">R. R. Ramsheeja</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Sreeraj"> R. Sreeraj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For view the internal structures of the human body such as liver, brain, kidney etc have a wide range of different modalities for medical images are provided nowadays. Computer Tomography is one of the most significant medical image modalities. In this paper use CT liver images for study the use of automatic computer aided techniques to calculate the volume of the liver tumor. Segmentation method is used for the detection of tumor from the CT scan is proposed. Gaussian filter is used for denoising the liver image and Adaptive Thresholding algorithm is used for segmentation. Multiple Region Of Interest(ROI) based method that may help to characteristic the feature different. It provides a significant impact on classification performance. Due to the characteristic of liver tumor lesion, inherent difficulties appear selective. For a better performance, a novel proposed system is introduced. Multiple ROI based feature selection and classification are performed. In order to obtain of relevant features for Support Vector Machine(SVM) classifier is important for better generalization performance. The proposed system helps to improve the better classification performance, reason in which we can see a significant reduction of features is used. The diagnosis of liver cancer from the computer tomography images is very difficult in nature. Early detection of liver tumor is very helpful to save the human life. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography%20%28CT%29" title="computed tomography (CT)">computed tomography (CT)</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20region%20of%20interest%28ROI%29" title=" multiple region of interest(ROI)"> multiple region of interest(ROI)</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20values" title=" feature values"> feature values</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM%20classification" title=" SVM classification"> SVM classification</a> </p> <a href="https://publications.waset.org/abstracts/18207/diagnosis-and-analysis-of-automated-liver-and-tumor-segmentation-on-ct" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18207.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">509</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">1158</span> 3D Liver Segmentation from CT Images Using a Level Set Method Based on a Shape and Intensity Distribution Prior</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nuseiba%20M.%20Altarawneh">Nuseiba M. Altarawneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Suhuai%20Luo"> Suhuai Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Brian%20Regan"> Brian Regan</a>, <a href="https://publications.waset.org/abstracts/search?q=Guijin%20Tang"> Guijin Tang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Liver segmentation from medical images poses more challenges than analogous segmentations of other organs. This contribution introduces a liver segmentation method from a series of computer tomography images. Overall, we present a novel method for segmenting liver by coupling density matching with shape priors. Density matching signifies a tracking method which operates via maximizing the Bhattacharyya similarity measure between the photometric distribution from an estimated image region and a model photometric distribution. Density matching controls the direction of the evolution process and slows down the evolving contour in regions with weak edges. The shape prior improves the robustness of density matching and discourages the evolving contour from exceeding liver’s boundaries at regions with weak boundaries. The model is implemented using a modified distance regularized level set (DRLS) model. The experimental results show that the method achieves a satisfactory result. By comparing with the original DRLS model, it is evident that the proposed model herein is more effective in addressing the over segmentation problem. Finally, we gauge our performance of our model against matrices comprising of accuracy, sensitivity and specificity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bhattacharyya%20distance" title="Bhattacharyya distance">Bhattacharyya distance</a>, <a href="https://publications.waset.org/abstracts/search?q=distance%20regularized%20level%20set%20%28DRLS%29%20model" title=" distance regularized level set (DRLS) model"> distance regularized level set (DRLS) model</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20segmentation" title=" liver segmentation"> liver segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=level%20set%20method" title=" level set method"> level set method</a> </p> <a href="https://publications.waset.org/abstracts/35588/3d-liver-segmentation-from-ct-images-using-a-level-set-method-based-on-a-shape-and-intensity-distribution-prior" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35588.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">313</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">1157</span> Deep Learning-Based Liver 3D Slicer for Image-Guided Therapy: Segmentation and Needle Aspiration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmedou%20Moulaye%20Idriss">Ahmedou Moulaye Idriss</a>, <a href="https://publications.waset.org/abstracts/search?q=Tfeil%20Yahya"> Tfeil Yahya</a>, <a href="https://publications.waset.org/abstracts/search?q=Tamas%20Ungi"> Tamas Ungi</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabor%20Fichtinger"> Gabor Fichtinger</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20segmentation" title=" liver segmentation"> liver segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20slicer" title=" 3D slicer"> 3D slicer</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20guided%20therapy" title=" image guided therapy"> image guided therapy</a>, <a href="https://publications.waset.org/abstracts/search?q=needle%20aspiration" title=" needle aspiration"> needle aspiration</a> </p> <a href="https://publications.waset.org/abstracts/183469/deep-learning-based-liver-3d-slicer-for-image-guided-therapy-segmentation-and-needle-aspiration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183469.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">48</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">1156</span> Computer-Aided Classification of Liver Lesions Using Contrasting Features Difference</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hussein%20Alahmer">Hussein Alahmer</a>, <a href="https://publications.waset.org/abstracts/search?q=Amr%20Ahmed"> Amr Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CAD%20system" title="CAD system">CAD system</a>, <a href="https://publications.waset.org/abstracts/search?q=difference%20of%20feature" title=" difference of feature"> difference of feature</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c%20means" title=" fuzzy c means"> fuzzy c means</a>, <a href="https://publications.waset.org/abstracts/search?q=lesion%20detection" title=" lesion detection"> lesion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20segmentation" title=" liver segmentation"> liver segmentation</a> </p> <a href="https://publications.waset.org/abstracts/39526/computer-aided-classification-of-liver-lesions-using-contrasting-features-difference" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39526.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">326</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">1155</span> Computer-Aided Detection of Simultaneous Abdominal Organ CT Images by Iterative Watershed Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belgherbi%20Aicha">Belgherbi Aicha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadjidj%20Ismahen"> Hadjidj Ismahen</a>, <a href="https://publications.waset.org/abstracts/search?q=Bessaid%20Abdelhafid"> Bessaid Abdelhafid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of liver, spleen and kidneys is regarded as a major primary step in the computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for the abdominal organ segmentation data using mathematical morphology. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. Our algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20diffusion%20filter" title="anisotropic diffusion filter">anisotropic diffusion filter</a>, <a href="https://publications.waset.org/abstracts/search?q=CT%20images" title=" CT images"> CT images</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20filter" title=" morphological filter"> morphological filter</a>, <a href="https://publications.waset.org/abstracts/search?q=mosaic%20image" title=" mosaic image"> mosaic image</a>, <a href="https://publications.waset.org/abstracts/search?q=simultaneous%20organ%20segmentation" title=" simultaneous organ segmentation"> simultaneous organ segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20watershed%20algorithm" title=" the watershed algorithm"> the watershed algorithm</a> </p> <a href="https://publications.waset.org/abstracts/19602/computer-aided-detection-of-simultaneous-abdominal-organ-ct-images-by-iterative-watershed-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19602.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">441</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">1154</span> Liver Lesion Extraction with Fuzzy Thresholding in Contrast Enhanced Ultrasound Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abder-Rahman%20Ali">Abder-Rahman Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Ad%C3%A9la%C3%AFde%20Albouy-Kissi"> Adélaïde Albouy-Kissi</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20Grand-Brochier"> Manuel Grand-Brochier</a>, <a href="https://publications.waset.org/abstracts/search?q=Viviane%20Ladan-Marcus"> Viviane Ladan-Marcus</a>, <a href="https://publications.waset.org/abstracts/search?q=Christine%20Hoeffl"> Christine Hoeffl</a>, <a href="https://publications.waset.org/abstracts/search?q=Claude%20Marcus"> Claude Marcus</a>, <a href="https://publications.waset.org/abstracts/search?q=Antoine%20Vacavant"> Antoine Vacavant</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Yves%20Boire"> Jean-Yves Boire</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new segmentation approach for focal liver lesions in contrast enhanced ultrasound imaging. This approach, based on a two-cluster Fuzzy C-Means methodology, considers type-II fuzzy sets to handle uncertainty due to the image modality (presence of speckle noise, low contrast, etc.), and to calculate the optimum inter-cluster threshold. Fine boundaries are detected by a local recursive merging of ambiguous pixels. The method has been tested on a representative database. Compared to both Otsu and type-I Fuzzy C-Means techniques, the proposed method significantly reduces the segmentation errors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=defuzzification" title="defuzzification">defuzzification</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20clustering" title=" fuzzy clustering"> fuzzy clustering</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=type-II%20fuzzy%20sets" title=" type-II fuzzy sets"> type-II fuzzy sets</a> </p> <a href="https://publications.waset.org/abstracts/32293/liver-lesion-extraction-with-fuzzy-thresholding-in-contrast-enhanced-ultrasound-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32293.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">485</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">1153</span> Classifier for Liver Ultrasound Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soumya%20Sajjan">Soumya Sajjan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=segmentation" title="segmentation">segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Machine" title=" Support Vector Machine"> Support Vector Machine</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20liver%20lesion" title=" ultrasound liver lesion"> ultrasound liver lesion</a>, <a href="https://publications.waset.org/abstracts/search?q=co-occurance%20Matrix" title=" co-occurance Matrix"> co-occurance Matrix</a> </p> <a href="https://publications.waset.org/abstracts/10244/classifier-for-liver-ultrasound-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10244.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">1152</span> A Fuzzy Approach to Liver Tumor Segmentation with Zernike Moments </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abder-Rahman%20Ali">Abder-Rahman Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Antoine%20Vacavant"> Antoine Vacavant</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20Grand-Brochier"> Manuel Grand-Brochier</a>, <a href="https://publications.waset.org/abstracts/search?q=Ad%C3%A9la%C3%AFde%20Albouy-Kissi"> Adélaïde Albouy-Kissi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Yves%20Boire"> Jean-Yves Boire</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new segmentation approach for liver lesions in regions of interest within MRI (Magnetic Resonance Imaging). This approach, based on a two-cluster Fuzzy C-Means methodology, considers the parameter variable compactness to handle uncertainty. Fine boundaries are detected by a local recursive merging of ambiguous pixels with a sequential forward floating selection with Zernike moments. The method has been tested on both synthetic and real images. When applied on synthetic images, the proposed approach provides good performance, segmentations obtained are accurate, their shape is consistent with the ground truth, and the extracted information is reliable. The results obtained on MR images confirm such observations. Our approach allows, even for difficult cases of MR images, to extract a segmentation with good performance in terms of accuracy and shape, which implies that the geometry of the tumor is preserved for further clinical activities (such as automatic extraction of pharmaco-kinetics properties, lesion characterization, etc). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=defuzzification" title="defuzzification">defuzzification</a>, <a href="https://publications.waset.org/abstracts/search?q=floating%20search" title=" floating search"> floating search</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20clustering" title=" fuzzy clustering"> fuzzy clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=Zernike%20moments" title=" Zernike moments "> Zernike moments </a> </p> <a href="https://publications.waset.org/abstracts/32509/a-fuzzy-approach-to-liver-tumor-segmentation-with-zernike-moments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32509.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">452</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">1151</span> An Investigation of Etiology of Liver Cirrhosis and Its Complications with Other Co-morbid Diseases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tayba%20Akram">Tayba Akram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> our main objective of this study is to work on the etiology of liver cirrhosis, to find basic reasons and causes of liver damage, and to find the pattern of liver cirrhosis in hepatic patients either suffering from hepatitis B/C or simple jaundice. We can evaluate medical treatment and the latest trends in patients suffering from liver cirrhosis. We can evaluate the side effects and adverse effects induced by drug therapy used to treat liver cirrhosis. The conclusion is based on the etiology of liver cirrhosis. The most common cause of liver cirrhosis is the viral Hepatitis C virus. Other common causes of liver cirrhosis that are estimated from our research are Hepatitis B virus, Diabetes Mellitus, Ascites, and very rarely found Hepatitis D virus. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=etiology" title="etiology">etiology</a>, <a href="https://publications.waset.org/abstracts/search?q=liver" title=" liver"> liver</a>, <a href="https://publications.waset.org/abstracts/search?q=cirrhosis" title=" cirrhosis"> cirrhosis</a>, <a href="https://publications.waset.org/abstracts/search?q=co-morbid%20diseases" title=" co-morbid diseases"> co-morbid diseases</a> </p> <a href="https://publications.waset.org/abstracts/193100/an-investigation-of-etiology-of-liver-cirrhosis-and-its-complications-with-other-co-morbid-diseases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193100.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">14</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">1150</span> Diallyl Trisulfide Protects the Rat Liver from CCl4-Induced Injury and Fibrogenesis by Attenuating Oxidative Stress</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiao-Jing%20Zhu">Xiao-Jing Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Liang%20Zhou"> Liang Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Shi-Zhong%20Zheng"> Shi-Zhong Zheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Various studies have shown that diallyl trisulfide (DATS) can protect the liver injury, and DATS has a strong antioxidant property. The aim of this study is to evaluate the in vivo role of DATS in protecting the liver against injury and fibrogenesis and further explores the underlying mechanisms. Our results demonstrated that DATS protected the liver from CCl4-caused injury by suppressing the elevation of ALT and AST activities, and by improving the histological architecture of the liver. Treatment with DATS or colchicine improved the liver fibrosis by sirius red staining and immunofluorescence. In addition, immunohistochemistry, western blot, and RT-PCR analyses indicated that DATS inhibited HSC activation. Furthermore, DATS attenuated oxidative stress by increasing glutathione and reducing lipid peroxides and malondialdehyde. These findings suggest that the protective effect of DATS on CCl4-caused liver injury and liver fibrogenesis was, at least partially, attributed to its antioxidant activity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=liver%20fibrogenesis" title="liver fibrogenesis">liver fibrogenesis</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20injury" title=" liver injury"> liver injury</a>, <a href="https://publications.waset.org/abstracts/search?q=oxidative%20stress" title=" oxidative stress"> oxidative stress</a>, <a href="https://publications.waset.org/abstracts/search?q=DATS" title=" DATS"> DATS</a> </p> <a href="https://publications.waset.org/abstracts/2858/diallyl-trisulfide-protects-the-rat-liver-from-ccl4-induced-injury-and-fibrogenesis-by-attenuating-oxidative-stress" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2858.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">431</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">1149</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">168</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">1148</span> Grape Seed Extract in Prevention and Treatment of Liver Toxic Cirrhosis in Rats </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Buloyan">S. Buloyan</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Mamikonyan"> V. Mamikonyan</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Hakobyan"> H. Hakobyan</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Harutyunyan"> H. Harutyunyan</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Gasparyan"> H. Gasparyan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The liver is the strongest regenerating organ of the organism, and even with 2/3 surgically removed, it can regenerate completely. Hence, liver cirrhosis may only develop when the regenerating system is off. We present the results of a comparative study of structural and functional characteristics of rat liver tissue under the conditions of toxic liver cirrhosis development, induced by carbon tetrachloride, and its prevention/treatment by natural compounds with antioxidant and immune stimulating action. Studies were made on Wister rats, weighing 120~140 g. Grape seeds extracts, separately and in combination with well known anticirrhotic drug ursodeoxycholic acid (ursodiol) have demonstrated effectiveness in prevention of liver cirrhosis development and its treatment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=carbon%20tetrachloride" title="carbon tetrachloride">carbon tetrachloride</a>, <a href="https://publications.waset.org/abstracts/search?q=GSE" title=" GSE"> GSE</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20cirrhosis" title=" liver cirrhosis"> liver cirrhosis</a>, <a href="https://publications.waset.org/abstracts/search?q=prevention" title=" prevention"> prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=treatment" title=" treatment "> treatment </a> </p> <a href="https://publications.waset.org/abstracts/15653/grape-seed-extract-in-prevention-and-treatment-of-liver-toxic-cirrhosis-in-rats" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15653.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">486</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">1147</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">1146</span> A Product-Specific/Unobservable Approach to Segmentation for a Value Expressive Credit Card Service</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manfred%20F.%20Maute">Manfred F. Maute</a>, <a href="https://publications.waset.org/abstracts/search?q=Olga%20Naumenko"> Olga Naumenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Raymond%20T.%20Kong"> Raymond T. Kong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Using data from a nationally representative financial panel of Canadian households, this study develops a psychographic segmentation of the customers of a value-expressive credit card service and tests for effects on relational response differences. The variety of segments elicited by agglomerative and k means clustering and the familiar profiles of individual clusters suggest that the face validity of the psychographic segmentation was quite high. Segmentation had a significant effect on customer satisfaction and relationship depth. However, when socio-demographic characteristics like household size and income were accounted for in the psychographic segmentation, the effect on relational response differences was magnified threefold. Implications for the segmentation of financial services markets are considered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20satisfaction" title="customer satisfaction">customer satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20services" title=" financial services"> financial services</a>, <a href="https://publications.waset.org/abstracts/search?q=psychographics" title=" psychographics"> psychographics</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20differences" title=" response differences"> response differences</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/39282/a-product-specificunobservable-approach-to-segmentation-for-a-value-expressive-credit-card-service" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39282.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">334</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">1145</span> A Comparison between Different Segmentation Techniques Used in Medical Imaging </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ibtihal%20D.%20Mustafa">Ibtihal D. Mustafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Mawia%20A.%20Hassan"> Mawia A. Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tumor segmentation from MRI image is important part of medical images experts. This is particularly a challenging task because of the high assorting appearance of tumor tissue among different patients. MRI images are advance of medical imaging because it is give richer information about human soft tissue. There are different segmentation techniques to detect MRI brain tumor. In this paper, different procedure segmentation methods are used to segment brain tumors and compare the result of segmentations by using correlation and structural similarity index (SSIM) to analysis and see the best technique that could be applied to MRI image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MRI" title="MRI">MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation" title=" correlation"> correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20similarity" title=" structural similarity"> structural similarity</a> </p> <a href="https://publications.waset.org/abstracts/51091/a-comparison-between-different-segmentation-techniques-used-in-medical-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51091.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">410</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">1144</span> Multidimensional Sports Spectators Segmentation and Social Media Marketing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Schmid">B. Schmid</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Kexel"> C. Kexel</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Djafarova"> E. Djafarova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding consumers is elementary for practitioners in marketing. Consumers of sports events, the sports spectators, are a particularly complex consumer crowd. In order to identify and define their profiles different segmentation approaches can be found in literature, one of them being multidimensional segmentation. Multidimensional segmentation models correspond to the broad range of attitudes, behaviours, motivations and beliefs of sports spectators, other than earlier models. Moreover, in sports there are some well-researched disciplines (e.g. football or North American sports) where consumer profiles and marketing strategies are elaborate and others where no research at all can be found. For example, there is almost no research on athletics spectators. This paper explores the current state of research on sports spectators segmentation. An in-depth literature review provides the framework for a spectators segmentation in athletics. On this basis, additional potential consumer groups and implications for social media marketing will be explored. The findings are the basis for further research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20segmentation" title="multidimensional segmentation">multidimensional segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=sports%20marketing" title=" sports marketing"> sports marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=sports%20spectators%20segmentation" title=" sports spectators segmentation"> sports spectators segmentation</a> </p> <a href="https://publications.waset.org/abstracts/47477/multidimensional-sports-spectators-segmentation-and-social-media-marketing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47477.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">307</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">1143</span> Arabic Handwriting Recognition Using Local Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Arif">Mohammed Arif</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdessalam%20Kifouche"> Abdessalam Kifouche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optical character recognition (OCR) has a main role in the present time. It's capable to solve many serious problems and simplify human activities. The OCR yields to 70's, since many solutions has been proposed, but unfortunately, it was supportive to nothing but Latin languages. This work proposes a system of recognition of an off-line Arabic handwriting. This system is based on a structural segmentation method and uses support vector machines (SVM) in the classification phase. We have presented a state of art of the characters segmentation methods, after that a view of the OCR area, also we will address the normalization problems we went through. After a comparison between the Arabic handwritten characters & the segmentation methods, we had introduced a contribution through a segmentation algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OCR" title="OCR">OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic%20characters" title=" Arabic characters"> Arabic characters</a>, <a href="https://publications.waset.org/abstracts/search?q=PAW" title=" PAW"> PAW</a>, <a href="https://publications.waset.org/abstracts/search?q=post-processing" title=" post-processing"> post-processing</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/183495/arabic-handwriting-recognition-using-local-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183495.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">72</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">1142</span> Physiochemical and Histological Study on the Effect of the Hibernation on the Liver of Uromastyx acanthinura (Bell, 1825)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Youssef.%20K.%20A.%20Abdalhafid">Youssef. K. A. Abdalhafid</a>, <a href="https://publications.waset.org/abstracts/search?q=Ezaldin%20A.%20M.%20Mohammed"> Ezaldin A. M. Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Masoud%20M.%20M.%20Zatout"> Masoud M. M. Zatout </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study described the changes in the liver of Uromastyx acanthinura (Bell, 1825) males and females during hibernation and activity seasons. The results revealed that, hibernation causes increase fatty liver and pigment cells with abundant damage, comparing with nearly normal structure and less fatty liver after the hibernation with almost normal pattern. Genomic DNA showed apparent separation during hibernation. Also, caspase 3 and caspase 7 activity reached a high level in the liver tissue during hibernation comparing with activity season. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=histological%20liver" title="histological liver">histological liver</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20fragmentation" title=" DNA fragmentation"> DNA fragmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=hibernation" title=" hibernation"> hibernation</a>, <a href="https://publications.waset.org/abstracts/search?q=caspase%203%20and%20caspase%207" title=" caspase 3 and caspase 7 "> caspase 3 and caspase 7 </a> </p> <a href="https://publications.waset.org/abstracts/14146/physiochemical-and-histological-study-on-the-effect-of-the-hibernation-on-the-liver-of-uromastyx-acanthinura-bell-1825" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14146.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">317</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">1141</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/abstracts/search?q=El%20Asnaoui%20Khalid">El Asnaoui Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=Aksasse%20Brahim"> Aksasse Brahim</a>, <a href="https://publications.waset.org/abstracts/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/abstracts/search?q=image%20segmentation" title="image segmentation">image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20analysis" title=" hierarchical analysis"> hierarchical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=2-D%20histogram" title=" 2-D histogram"> 2-D histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/42096/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/abstracts/42096.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">380</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">1140</span> Imp_hist-Si: Improved Hybrid Image Segmentation Technique for Satellite Imagery to Decrease the Segmentation Error Rate</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neetu%20Manocha">Neetu Manocha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image segmentation is a technique where a picture is parted into distinct parts having similar features which have a place with similar items. Various segmentation strategies have been proposed as of late by prominent analysts. But, after ultimate thorough research, the novelists have analyzed that generally, the old methods do not decrease the segmentation error rate. Then author finds the technique HIST-SI to decrease the segmentation error rates. In this technique, cluster-based and threshold-based segmentation techniques are merged together. After then, to improve the result of HIST-SI, the authors added the method of filtering and linking in this technique named Imp_HIST-SI to decrease the segmentation error rates. The goal of this research is to find a new technique to decrease the segmentation error rates and produce much better results than the HIST-SI technique. For testing the proposed technique, a dataset of Bhuvan – a National Geoportal developed and hosted by ISRO (Indian Space Research Organisation) is used. Experiments are conducted using Scikit-image & OpenCV tools of Python, and performance is evaluated and compared over various existing image segmentation techniques for several matrices, i.e., Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite%20image" title="satellite image">satellite image</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=edge%20detection" title=" edge detection"> edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=error%20rate" title=" error rate"> error rate</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR" title=" PSNR"> PSNR</a>, <a href="https://publications.waset.org/abstracts/search?q=HIST-SI" title=" HIST-SI"> HIST-SI</a>, <a href="https://publications.waset.org/abstracts/search?q=linking" title=" linking"> linking</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering" title=" filtering"> filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=imp_HIST-SI" title=" imp_HIST-SI"> imp_HIST-SI</a> </p> <a href="https://publications.waset.org/abstracts/149905/imp-hist-si-improved-hybrid-image-segmentation-technique-for-satellite-imagery-to-decrease-the-segmentation-error-rate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149905.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">141</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">1139</span> Performance Evaluation of Various Segmentation Techniques on MRI of Brain Tissue</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.V.%20Suryawanshi">U.V. Suryawanshi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.S.%20Chowhan"> S.S. Chowhan</a>, <a href="https://publications.waset.org/abstracts/search?q=U.V%20Kulkarni"> U.V Kulkarni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accuracy of segmentation methods is of great importance in brain image analysis. Tissue classification in Magnetic Resonance brain images (MRI) is an important issue in the analysis of several brain dementias. This paper portraits performance of segmentation techniques that are used on Brain MRI. A large variety of algorithms for segmentation of Brain MRI has been developed. The objective of this paper is to perform a segmentation process on MR images of the human brain, using Fuzzy c-means (FCM), Kernel based Fuzzy c-means clustering (KFCM), Spatial Fuzzy c-means (SFCM) and Improved Fuzzy c-means (IFCM). The review covers imaging modalities, MRI and methods for noise reduction and segmentation approaches. All methods are applied on MRI brain images which are degraded by salt-pepper noise demonstrate that the IFCM algorithm performs more robust to noise than the standard FCM algorithm. We conclude with a discussion on the trend of future research in brain segmentation and changing norms in IFCM for better results. <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=preprocessing" title=" preprocessing"> preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=FCM" title=" FCM"> FCM</a>, <a href="https://publications.waset.org/abstracts/search?q=KFCM" title=" KFCM"> KFCM</a>, <a href="https://publications.waset.org/abstracts/search?q=SFCM" title=" SFCM"> SFCM</a>, <a href="https://publications.waset.org/abstracts/search?q=IFCM" title=" IFCM"> IFCM</a> </p> <a href="https://publications.waset.org/abstracts/12406/performance-evaluation-of-various-segmentation-techniques-on-mri-of-brain-tissue" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12406.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">332</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">1138</span> Undifferentiated Embryonal Sarcoma of Liver: A Rare Case Report</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thieu-Thi%20Tra%20My">Thieu-Thi Tra My</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Undifferentiated embryonal sarcoma of the liver (UESL), a rare malignant mesenchymal tumor, is commonly seen in children. The symptoms and imaging were not specific, so it could be mimicked with other tumors or liver abscesses. The tumor often appears as a large heterogeneous echoic solid mass with small cystic areas while showing a cyst-like appearance on CT and MRI. The histopathological manifestation of the UESL consisted of stellate-shaped and spindle cells scattered on a myxoid background with high mitotic count. Cells with multiple or bizarre nuclear were also observed. Here, we aimed to describe a 9-year-old male diagnosed with UESL focused on imaging and histopathological characteristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=undifferentiated%20embryonal%20sarcoma%20of%20liver" title="undifferentiated embryonal sarcoma of liver">undifferentiated embryonal sarcoma of liver</a>, <a href="https://publications.waset.org/abstracts/search?q=UESL" title=" UESL"> UESL</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20sarcoma" title=" liver sarcoma"> liver sarcoma</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20tumor" title=" liver tumor"> liver tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=children" title=" children"> children</a> </p> <a href="https://publications.waset.org/abstracts/170077/undifferentiated-embryonal-sarcoma-of-liver-a-rare-case-report" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170077.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">74</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">1137</span> Sea-Land Segmentation Method Based on the Transformer with Enhanced Edge Supervision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lianzhong%20Zhang">Lianzhong Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chao%20Huang"> Chao Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sea-land segmentation is a basic step in many tasks such as sea surface monitoring and ship detection. The existing sea-land segmentation algorithms have poor segmentation accuracy, and the parameter adjustments are cumbersome and difficult to meet actual needs. Also, the current sea-land segmentation adopts traditional deep learning models that use Convolutional Neural Networks (CNN). At present, the transformer architecture has achieved great success in the field of natural images, but its application in the field of radar images is less studied. Therefore, this paper proposes a sea-land segmentation method based on the transformer architecture to strengthen edge supervision. It uses a self-attention mechanism with a gating strategy to better learn relative position bias. Meanwhile, an additional edge supervision branch is introduced. The decoder stage allows the feature information of the two branches to interact, thereby improving the edge precision of the sea-land segmentation. Based on the Gaofen-3 satellite image dataset, the experimental results show that the method proposed in this paper can effectively improve the accuracy of sea-land segmentation, especially the accuracy of sea-land edges. The mean IoU (Intersection over Union), edge precision, overall precision, and F1 scores respectively reach 96.36%, 84.54%, 99.74%, and 98.05%, which are superior to those of the mainstream segmentation models and have high practical application values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SAR" title="SAR">SAR</a>, <a href="https://publications.waset.org/abstracts/search?q=sea-land%20segmentation" title=" sea-land segmentation"> sea-land segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a> </p> <a href="https://publications.waset.org/abstracts/148759/sea-land-segmentation-method-based-on-the-transformer-with-enhanced-edge-supervision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148759.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">181</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">1136</span> Histomorphological Comparisons of Liver of Broiler Chickens and Wild Boar in Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khenenou%20Tarek">Khenenou Tarek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aim: The objective of present study was to compare the normal macro and microscopic appearance of the liver in two very different species, one is an omnivorous mammal; the wild boar and the other belongs to the family of poultry; broiler chicken from the region of Bouhmama (Khenchela). Materials and methods: Eight broilers (58 days of age) and eight wild boars were included in the experiment to obtain information about the morpho-histological appearances of liver in two species. Results: There is a big difference in the liver appearance between the two species, in the wild boar it is of firm consistency with a tiger aspect and divided into four lobes, whereas in the broiler, the liver is brown and sometimes pale during the first 10-14 days, so it was divided into two lobes. Concerning the liver parenchyma, we used the Russian LOMBO MBS-10 stereo microscope, our results showed that the liver parenchyma was well developed in wild boar than in broiler chickens whereas, in broiler chickens; an excessive development of the sinus; the latter were less developed in the wild boar. Conclusion: The macroscopic observation showed a marked difference in liver between the two species. The microscopic examination of liver showed that the parenchyma is less pronounced in broilers whereas the sinuses were highly developed in the wild boar. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=broiler%20chicken" title="broiler chicken">broiler chicken</a>, <a href="https://publications.waset.org/abstracts/search?q=liver" title=" liver"> liver</a>, <a href="https://publications.waset.org/abstracts/search?q=macro%20and%20microscopic%20appearances" title=" macro and microscopic appearances"> macro and microscopic appearances</a>, <a href="https://publications.waset.org/abstracts/search?q=wild%20boar" title=" wild boar"> wild boar</a>, <a href="https://publications.waset.org/abstracts/search?q=Algeria" title=" Algeria"> Algeria</a> </p> <a href="https://publications.waset.org/abstracts/190129/histomorphological-comparisons-of-liver-of-broiler-chickens-and-wild-boar-in-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190129.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">21</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=liver%20segmentation&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=liver%20segmentation&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=liver%20segmentation&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=liver%20segmentation&page=5">5</a></li> <li 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