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spleen segmentation</title> <meta name="description" content="Search results for: liver and spleen segmentation"> <meta name="keywords" content="liver and spleen 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> 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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 and spleen 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> 1214</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: liver and spleen segmentation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1214</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">1213</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">1212</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">1211</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">440</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">1210</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">1209</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">1208</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">1207</span> A Rare Case Report of Wandering Spleen Torsion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Steven%20Robinson">Steven Robinson</a>, <a href="https://publications.waset.org/abstracts/search?q=Adriana%20Dager"> Adriana Dager</a>, <a href="https://publications.waset.org/abstracts/search?q=Param%20Patel"> Param Patel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wandering spleen is a rare variant where there is abnormal development of the ligamentous peritoneal attachments of the spleen which normally anchor it in the left upper quadrant of the abdomen. Ligamentous abnormalities can be congenital, or acquired through pregnancy, injury, or iatrogenic causes. Absence or laxity of these ligaments allows migration of the spleen into ectopic portions of the abdomen, which is also associated with an elongated vascular pedicle. Incidence of wandering spleen is reported at less than 0.25% with a female to male ratio of approximately 6:1. The most common complication of a wandering spleen is torsion around its vascular pedicle which can lead to thrombosis and infarction. Torsion of a wandering spleen is a rare but important cause of an acute abdomen. Imaging, and specifically CT or ultrasound, is crucial in the diagnosis. We present a case of a torsed wandering spleen which was treated with splenectomy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wandering%20Spleen" title="Wandering Spleen">Wandering Spleen</a>, <a href="https://publications.waset.org/abstracts/search?q=Torsion" title=" Torsion"> Torsion</a>, <a href="https://publications.waset.org/abstracts/search?q=Splenic%20Torsion" title=" Splenic Torsion"> Splenic Torsion</a>, <a href="https://publications.waset.org/abstracts/search?q=Spleen" title=" Spleen"> Spleen</a> </p> <a href="https://publications.waset.org/abstracts/162557/a-rare-case-report-of-wandering-spleen-torsion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162557.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">81</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">1206</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">1205</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">1204</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">1203</span> Gold Nanoparticle: Synthesis, Characterization, Clinico-Pathological, Pathological and Bio-Distribution Studies in Rabbits</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20M.%20Bashandy">M. M. Bashandy</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Ahmed"> A. R. Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20El-Gaffary"> M. El-Gaffary</a>, <a href="https://publications.waset.org/abstracts/search?q=Sahar%20S.%20Abd%20El-Rahman"> Sahar S. Abd El-Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study evaluated the acute toxicity and tissue distribution of intravenously administered gold nanoparticles (AuNPs) in male rabbits. Rabbits were exposed to single dose of AuNPs (300 µg/ kg). Toxic effects were assessed via general behavior, hematological parameters, serum biochemical parameters and histopathological examination of various rabbits’ organs. Tissue distribution of AuNPs was evaluated at a dose of 300 µg/ kg in male rabbit. Inductively coupled plasma–mass spectrometry (ICP-MS) was used to determine gold concentrations in tissue samples collected at predetermined time intervals. After one week, AuNPs exerted no obvious acute toxicity in rabbits. However, inflammatory reactions in lung and liver cells were induced in rabbits treated at the300 µg/ kg dose level. The highest gold levels were found in the spleen, followed by liver, lungs and kidneys. These results indicated that AuNPs could be distributed extensively to various tissues in the body, but primarily in the spleen and liver. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gold%20nanoparticles" title="gold nanoparticles">gold nanoparticles</a>, <a href="https://publications.waset.org/abstracts/search?q=toxicity" title=" toxicity"> toxicity</a>, <a href="https://publications.waset.org/abstracts/search?q=pathology" title=" pathology"> pathology</a>, <a href="https://publications.waset.org/abstracts/search?q=hematology" title=" hematology"> hematology</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20function" title=" liver function"> liver function</a>, <a href="https://publications.waset.org/abstracts/search?q=kidney%20function" title=" kidney function"> kidney function</a> </p> <a href="https://publications.waset.org/abstracts/38067/gold-nanoparticle-synthesis-characterization-clinico-pathological-pathological-and-bio-distribution-studies-in-rabbits" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38067.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">1202</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">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">1201</span> Effect of Auraptene on the Enzymatic Glutathione Redox-System in Nrf2 Knockout Mice</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ludmila%20A.%20Gavriliuc">Ludmila A. Gavriliuc</a>, <a href="https://publications.waset.org/abstracts/search?q=Jerry%20McLarty"> Jerry McLarty</a>, <a href="https://publications.waset.org/abstracts/search?q=Heather%20E.%20Kleiner"> Heather E. Kleiner</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Michael%20Mathis"> J. Michael Mathis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Abstract -- Background: The citrus coumarine Auraptene (Aur) is an effective chemopreventive agent, as manifested in many models of diseases and cancer. Nuclear factor erythroid 2-related factor (Nrf2) is an important regulator of genes induced by oxidative stress, such as glutathione S-transferases, heme oxygenase-1, and peroxiredoxin 1, by activating the antioxidant response element (ARE). Genetic and biochemical evidence has demonstrated that glutathione (GSH) and glutathione-dependent enzymes, glutathione reductase (GR), glutathione peroxidases (GPs), glutathione S-transferases (GSTs) are responsible for the control of intracellular reduction-oxidation status and participate in cellular adaptation to oxidative stress. The effect of Aur on the activity of GR, GPs (Se-GP and Se-iGP), and content of GSH in the liver, kidney, and spleen is insufficiently explored. Aim: Our goal was the examination of the Aur influence on the redox-system of GSH in Nrf2 wild type and Nrf2 knockout mice via activation of Nrf2 and ARE. Methods: Twenty female mice, 10 Nrf2 wild-type (WT) and 10 Nrf2 (-/-) knockout (KO), were bred and genotyped for our study. The activity of GR, Se-GP, Se-iGP, GST, G6PD, CytP450 reductase, catalase (Cat), and content of GSH were analyzed in the liver, kidney, and spleen using Spectrophotometry methods. The results of the specific activity of enzymes and the amount of GSH were analyzed with ANOVA and Spearman statistical methods. Results: Aur (200 mg/kg) treatment induced hepatic GST, GR, Se-GP activity and inhibited their activity in the spleen of mice, most likely via activation of the ARE through Nrf2. Activation in kidney Se-GP and G6PD by Aur is also controlled, apparently through Nrf2. Results of the non-parametric Spearman correlation analysis indicated the strong positive correlation between GR and G6PD only in the liver in WT control mice (r=+0.972; p < 0.005) and in the kidney KO control mice (r=+0.958; p < 0.005). The observed low content of GSH in the liver of KO mice indicated an increase in its participation in the neutralization of toxic substances with the absence of induction of GSH-dependent enzymes, such as GST, GR, Se-GP, and Se-iGP. Activation of CytP450 in kidney and spleen and Cat in the liver in KO mice probably revealed another regulatory mechanism for these enzymes. Conclusion: Thereby, obtained results testify that Aur can modulate the activity of genes and antioxidant enzymatic redox-system of GSH, responsible for the control of intracellular reduction-oxidation status. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auraptene" title="auraptene">auraptene</a>, <a href="https://publications.waset.org/abstracts/search?q=glutathione" title=" glutathione"> glutathione</a>, <a href="https://publications.waset.org/abstracts/search?q=GST" title=" GST"> GST</a>, <a href="https://publications.waset.org/abstracts/search?q=Nrf2" title=" Nrf2"> Nrf2</a> </p> <a href="https://publications.waset.org/abstracts/133552/effect-of-auraptene-on-the-enzymatic-glutathione-redox-system-in-nrf2-knockout-mice" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133552.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">149</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">1200</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">1199</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">1198</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">1197</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">1196</span> Histopathological Examination of BALB/C Mice Receiving Strains of Acinetobacter baumannii Resistant to Colistin Antibiotic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahriar%20Sepahvand">Shahriar Sepahvand</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ali%20Davarpanah"> Mohammad Ali Davarpanah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Infections caused by Acinetobacter baumannii are among the common hospital-acquired infections that have seen an increase in antibiotic resistance in recent years. Colistin is the last treatment option against this pathogen. The aim of this study is to investigate the histopathology of BALB/C mice receiving sensitive and resistant strains of Acinetobacter baumannii to colistin. A total of 68 female laboratory mice weighing 30 to 40 grams of the BALB/C breed were studied in this research for three weeks under appropriate laboratory conditions in terms of food and environment. The experimental groups included: control group, second group, third group, fourth group. Lung, liver, spleen, and kidney tissues were removed from anesthetized mice and, after washing in physiological serum, were fixed in 10% formalin for 14 days. For dehydration, alcohol with ascending degrees of 70, 80, 90, and 100 was used. After clearing and soaking in paraffin, the samples were embedded in paraffin. Then, sections with a thickness of 5 microns were prepared and, after staining by hematoxylin-eosin, the samples were ready for study with a light microscope. In liver, spleen, lung, and kidney tissues of mice receiving the colistin-sensitive strain of Acinetobacter baumannii, infiltration of inflammatory cells and hyperemia were observed compared to control group mice. Liver and lung tissues of mice receiving strains of Acinetobacter baumannii resistant to colistin showed tissue destruction in addition to infiltration of inflammatory cells and hyperemia, with more destruction observed in lung tissue. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acinetobacter%20baumannii" title="acinetobacter baumannii">acinetobacter baumannii</a>, <a href="https://publications.waset.org/abstracts/search?q=colistin%20antibiotic" title=" colistin antibiotic"> colistin antibiotic</a>, <a href="https://publications.waset.org/abstracts/search?q=histopathological%20examination" title=" histopathological examination"> histopathological examination</a>, <a href="https://publications.waset.org/abstracts/search?q=resistant" title=" resistant"> resistant</a> </p> <a href="https://publications.waset.org/abstracts/185152/histopathological-examination-of-balbc-mice-receiving-strains-of-acinetobacter-baumannii-resistant-to-colistin-antibiotic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185152.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">68</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">1195</span> SAR and B₁ Considerations for Multi-Nuclear RF Body Coils</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ria%20Forner">Ria Forner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Due to increases in the SNR at 7T and above, it becomes more favourable to make use of X-nuclear imaging. Integrated body coils tuned to 120MHz for 31P, 79MHz for 23Na, and 75 MHz for 13C at 7T were simulated with a human male, female, or child body model to assess strategies of use for metabolic MR imaging in the body. Methods: B1 and SAR efficiencies in the heart, liver, spleen, and kidneys were assessed using numerical simulations over the three frequencies with phase shimming. Results: B1+ efficiency is highly variable over the different organs, particularly for the highest frequency; however, local SAR efficiency remains relatively constant over the frequencies in all subjects. Although the optimal phase settings vary, one generic phase setting can be identified for each frequency at which the penalty in B1+ is at a max of 10%. Discussion: The simulations provide practical strategies for power optimization, B1 management, and maintaining safety. As expected, the B1 field is similar at 75MHz and 79MHz, but reduced at 120MHz. However, the B1 remains relatively constant when normalised by the square root of the peak local SAR. This is in contradiction to generalized SAR considerations of 1H MRI at different field strengths, which is defined by global SAR instead. Conclusion: Although the B1 decreases with frequency, SAR efficiency remains constant throughout the investigated frequency range. It is possible to shim the body coil to obtain a maximum of 10% extra B1+ in a specific organ in a body when compared to a generic setting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=birdcage" title="birdcage">birdcage</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-nuclear" title=" multi-nuclear"> multi-nuclear</a>, <a href="https://publications.waset.org/abstracts/search?q=B1%20shimming" title=" B1 shimming"> B1 shimming</a>, <a href="https://publications.waset.org/abstracts/search?q=7%20Tesla%20MRI" title=" 7 Tesla MRI"> 7 Tesla MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=liver" title=" liver"> liver</a>, <a href="https://publications.waset.org/abstracts/search?q=kidneys" title=" kidneys"> kidneys</a>, <a href="https://publications.waset.org/abstracts/search?q=heart" title=" heart"> heart</a>, <a href="https://publications.waset.org/abstracts/search?q=spleen" title=" spleen"> spleen</a> </p> <a href="https://publications.waset.org/abstracts/183402/sar-and-b1-considerations-for-multi-nuclear-rf-body-coils" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183402.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">67</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">1194</span> Physicochemical Properties and Toxicity Studies on a Lectin from the Bulb of Dioscorea bulbifera</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Uchenna%20Nkiruka%20Umeononihu">Uchenna Nkiruka Umeononihu</a>, <a href="https://publications.waset.org/abstracts/search?q=Adenike%20Kuku"> Adenike Kuku</a>, <a href="https://publications.waset.org/abstracts/search?q=Oludele%20Odekanyin"> Oludele Odekanyin</a>, <a href="https://publications.waset.org/abstracts/search?q=Olubunmi%20Babalola"> Olubunmi Babalola</a>, <a href="https://publications.waset.org/abstracts/search?q=Femi%20Agboola"> Femi Agboola</a>, <a href="https://publications.waset.org/abstracts/search?q=Rapheal%20Okonji"> Rapheal Okonji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a lectin from the bulb of Dioscorea bulbifera was purified, characterised, and its acute and sub-acute toxicity was investigated with a view to evaluate its toxic effects in mice. The protein from the bulb was extracted by homogenising 50 g of the bulb in 500 ml of phosphate buffered saline (0.025 M) of pH 7.2, stirred for 3 hr, and centrifuged at the speed of 3000 rpm. Blood group and sugar specificity assays of the crude extract were determined. The lectin was purified in a two-step procedure- gel filtration on Sephadex G-75 and affinity chromatography on Sepharose 4-B arabinose. The degree of purity of the purified lectin was ascertained by SDS-polyacrylamide gel electrophoresis. Detection of covalently bound carbohydrate was carried out with Periodic Acid-Schiffs (PAS) reagent staining technique. Effects of temperature, pH, and EDTA on the lectin were carried out using standard methods. This was followed by acute toxicity studies via oral and subcutaneous routes using mice. The animals were monitored for mortality and signs of toxicity. The sub-acute toxicity studies were carried out using rats. Different concentrations of the lectin were administered twice daily for 5 days via the subcutaneous route. The animals were sacrificed on the sixth day; blood samples and liver tissues were collected. Biochemical assays (determination of total protein, direct bilirubin, Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), catalase (CAT), and superoxide dismutase (SOD)) were carried out on the serum and liver homogenates. The collected organs (heart, liver, kidney, and spleen) were subjected to histopathological analysis. The results showed that lectin from the bulbs of Dioscorea bulbifera agglutinated non-specifically the erythrocytes of the human ABO system as well as rabbit erythrocytes. The haemagglutinating activity was strongly inhibited by arabinose and dulcitol with minimum inhibitory concentrations of 0.781 and 6.25, respectively. The lectin was purified to homogeneity with native and subunit molecular weights of 56,273 and 29,373 Daltons, respectively. The lectin was thermostable up to 30 0C and lost 25 %, 33.3 %, and 100 % of its heamagglutinating activity at 40°C, 50°C, and 60°C, respectively. The lectin was maximally active at pH 4 and 5 but lost its total activity at pH eight, while EDTA (10 mM) had no effect on its haemagglutinating activity. PAS reagent staining showed that the lectin was not a glycoprotein. The sub-acute studies on rats showed elevated levels of ALT, AST, serum bilirubin, total protein in serum and liver homogenates suggesting damage to liver and spleen. The study concluded that the aerial bulb of D. bulbifera lectin was non-specific in its heamagglutinating activity and dimeric in its structure. The lectin shared some physicochemical characteristics with lectins from other Dioscorecea species and was moderately toxic to the liver and spleen of treated animals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dioscorea%20bulbifera" title="Dioscorea bulbifera">Dioscorea bulbifera</a>, <a href="https://publications.waset.org/abstracts/search?q=heamagglutinin" title=" heamagglutinin"> heamagglutinin</a>, <a href="https://publications.waset.org/abstracts/search?q=lectin" title=" lectin"> lectin</a>, <a href="https://publications.waset.org/abstracts/search?q=toxicity" title=" toxicity"> toxicity</a> </p> <a href="https://publications.waset.org/abstracts/130539/physicochemical-properties-and-toxicity-studies-on-a-lectin-from-the-bulb-of-dioscorea-bulbifera" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130539.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">128</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">1193</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">1192</span> Pathological and Molecular Diagnosis of Caseous Lymphadenitis in Chinkara Deer (Gazella Bennettii), in Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mudassar%20Iqbal">Mudassar Iqbal</a>, <a href="https://publications.waset.org/abstracts/search?q=Riaz%20Hussain"> Riaz Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Khalid%20Mehmood"> Khalid Mehmood</a>, <a href="https://publications.waset.org/abstracts/search?q=Farah%20Ali"> Farah Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Fazal%20Mahmood"> Fazal Mahmood</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Ghaffar"> Abdul Ghaffar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Corynebacterium pseudotuberculosis is an important cause of caseous lymphadenitis (CL), a complex, chronic devastating and destructive disease of small ruminants. In present study, postmortem examination of Chinkara deer (n=25) was conducted in year 2014. Pus samples suggestive of CL were collected from the superficial lymph nodes, liver, spleen and lungs during necropsy and subjected to standard microbiological procedures for isolation and molecular analysis of bacterial pathogens. Pus samples collected from carcasses (25) presenting clinical lesions of C. pseudotuberculosis infection was identified in 19 (76%) carcasses on the basis of culture characteristics. The frequency of C. pseudotuberculosis bacterium was higher in older animals as compared to young animals. Grossly, multiple tubercles of variable size having caseous material were observed in liver, lungs, spleen and lymph nodes. Histopathologically, tissue sections from all the visceral organs were extensively plugged with abscess. In present study specific prolineiminopeptidase (PIP) gene of the C. pseudotuberculosis was amplified by the Polymerase chain reaction technique (PCR) in 17(25) cases. The efficient and reliable molecular analysis along with necropsy findings in present study can be used as valuable approach for diagnosis of caseous lymphadenitis in small ruminants. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinkara%20deer" title="Chinkara deer">Chinkara deer</a>, <a href="https://publications.waset.org/abstracts/search?q=Corynebacterium%20pseudotuberculosis" title=" Corynebacterium pseudotuberculosis"> Corynebacterium pseudotuberculosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Caseous%20lymphadenitis" title=" Caseous lymphadenitis"> Caseous lymphadenitis</a>, <a href="https://publications.waset.org/abstracts/search?q=PCR" title=" PCR"> PCR</a> </p> <a href="https://publications.waset.org/abstracts/24616/pathological-and-molecular-diagnosis-of-caseous-lymphadenitis-in-chinkara-deer-gazella-bennettii-in-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24616.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">482</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">1191</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">1190</span> Immune Responses and Pathological Manifestations in Chicken to Oral Infection with Salmonella typhimurium</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mudasir%20Ahmad%20Syed">Mudasir Ahmad Syed</a>, <a href="https://publications.waset.org/abstracts/search?q=Raashid%20Ahmd%20Wani"> Raashid Ahmd Wani</a>, <a href="https://publications.waset.org/abstracts/search?q=Mashooq%20Ahmad%20Dar"> Mashooq Ahmad Dar</a>, <a href="https://publications.waset.org/abstracts/search?q=Uneeb%20Urwat"> Uneeb Urwat</a>, <a href="https://publications.waset.org/abstracts/search?q=Riaz%20Ahmad%20Shah"> Riaz Ahmad Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Nazir%20Ahmad%20Ganai"> Nazir Ahmad Ganai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Salmonella enterica serovar Typhimurium (Salmonella Typhimurium) is a primary avian pathogen responsible for severe intestinal pathology in younger chickens and economic losses. However, the Salmonella Typhimurium is also able to cause infection in humans, described by typhoid fever and acute gastro-intestinal disease. A study was conducted at days to investigate pathological, histopathological, haemato-biochemical, immunological and expression kinetics of NRAMP (natural resistance associated macrophage protein) gene family (NRAMP1 and NRAMP2) in broiler chickens following experimental infection of Salmonella Typhimurium at 0,1,3,5,7,9,11,13 and 15 days respectively. Infection was developed in birds through oral route at 2×108 CFU/ml. Clinical symptoms appeared 4 days post infection (dpi) and after one-week birds showed progressive weakness, anorexia, diarrhea and lowering of head. On postmortem examination, liver showed congestion, hemorrhage and necrotic foci on surface, while as spleen, lungs and intestines revealed congestion and hemorrhages. Histopathological alterations were principally observed in liver in second week post infection. Changes in liver comprised of congestion, areas of necrosis, reticular endothelial hyperplasia in association with mononuclear cell and heterophilic infiltration. Hematological studies confirm a significant decrease (P<0.05) in RBC count, Hb concentration and PCV. White blood cell count showed significant increase throughout the experimental study. An increase in heterophils was found up to 7dpi and a decreased pattern was observed afterwards. Initial lymphopenia followed by lymphocytosis was found in infected chicks. Biochemical studies showed a significant increase in glucose, AST and ALT concentration and a significant decrease (P<0.05) in total protein and albumin level in the infected group. Immunological studies showed higher titers of IgG in infected group as compared to control group. The real time gene expression of NRAMPI and NRAMP2 genes increased significantly (P<0.05) in infected group as compared to controls. The peak expression of NRAMP1 gene was seen in liver, spleen and caecum of infected birds at 3dpi, 5dpi and 7dpi respectively, while as peak expression of NRAMP2 gene in liver, spleen and caecum of infected chicken was seen at 9dpi, 5dpi and 9dpi respectively. This study has role in diagnostics and prognostics in the poultry industry for the detection of salmonella infections at early stages of poultry development. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biochemistry" title="biochemistry">biochemistry</a>, <a href="https://publications.waset.org/abstracts/search?q=histopathology" title=" histopathology"> histopathology</a>, <a href="https://publications.waset.org/abstracts/search?q=NRAMP" title=" NRAMP"> NRAMP</a>, <a href="https://publications.waset.org/abstracts/search?q=poultry" title=" poultry"> poultry</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time%20expression" title=" real time expression"> real time expression</a>, <a href="https://publications.waset.org/abstracts/search?q=Salmonella%20Typhimurium" title=" Salmonella Typhimurium"> Salmonella Typhimurium</a> </p> <a href="https://publications.waset.org/abstracts/56065/immune-responses-and-pathological-manifestations-in-chicken-to-oral-infection-with-salmonella-typhimurium" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56065.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">1189</span> Protective Effect of Protocatechuic Acid Alone and in Combination with Ascorbic Acid in Aniline Hydrochloride Induced Spleen Toxicity in Rats</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aman%20Upaganlawar">Aman Upaganlawar</a>, <a href="https://publications.waset.org/abstracts/search?q=Upasana%20Khairnar"> Upasana Khairnar</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandrashekhar%20Upasani"> Chandrashekhar Upasani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study was designed to evaluate the protective effects of protocatechuic acid alone and in combination with ascorbic acid in aniline hydrochloride-induced spleen toxicity in rats. Male Wistar rats of either sex (200-250g) were used and divided into different groups. Spleen toxicity was induced by aniline hydrochloride (100 ppm) in drinking water for 28 days. Treatment group received protocatechuic acid (40 mg/kg/day, p.o), ascorbic acid (40 mg/kg/day, p.o), and combination of protocatechuic acid (20 mg/kg/day, p.o) and ascorbic acid (20 mg/kg/day, p.o) followed by aniline hydrochloride. At the end of treatment period, serum and tissue parameters were evaluated. Rats supplemented with aniline hydrochloride showed a significant alteration in body weight, spleen weight, feed consumption, water intake, hematological parameters (Hemoglobin content, Red Blood Cells, White Blood Cells and Total iron content), tissue parameters (Lipid peroxidation, Reduced glutathione, Nitric oxide content) compared to control group. Histopathology of aniline hydrochloride-induced spleen showed significant damage compared to control rats. Treatment with Protocatechuic acid along with ascorbic acid showed better protection as compared to protocatechuic acid or ascorbic acid alone in aniline hydrochloride-induced spleen toxicity. In conclusion Treatment with protocatechuic acid and ascorbic acid in combination showed significant protection in aniline hydrochloride-induced splenic toxicity in rats. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aniline" title="aniline">aniline</a>, <a href="https://publications.waset.org/abstracts/search?q=spleen%20toxicity" title=" spleen toxicity"> spleen toxicity</a>, <a href="https://publications.waset.org/abstracts/search?q=protocatechuic%20acid" title=" protocatechuic acid"> protocatechuic acid</a>, <a href="https://publications.waset.org/abstracts/search?q=ascorbic%20acid" title=" ascorbic acid"> ascorbic acid</a>, <a href="https://publications.waset.org/abstracts/search?q=antioxidants" title=" antioxidants"> antioxidants</a> </p> <a href="https://publications.waset.org/abstracts/52559/protective-effect-of-protocatechuic-acid-alone-and-in-combination-with-ascorbic-acid-in-aniline-hydrochloride-induced-spleen-toxicity-in-rats" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52559.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">358</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">1188</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">1187</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">1186</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">1185</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> <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%20and%20spleen%20segmentation&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=liver%20and%20spleen%20segmentation&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=liver%20and%20spleen%20segmentation&page=4">4</a></li> <li class="page-item"><a class="page-link" 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