Search results for: Hadjidj Ismahen

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Ismahen</title> <meta name="description" content="Search results for: Hadjidj Ismahen"> <meta name="keywords" content="Hadjidj Ismahen"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Hadjidj Ismahen"> <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> 4</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Hadjidj Ismahen</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</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">3</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鈥檚 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">2</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">1</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> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul 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Search results for: Hadjidj Ismahen