Articles liés à Liver and Tumor Segmentation in CT Images
Synopsis
A new proposed method of fully automatic processing frameworks is given based on Geodesic Graph-cut Active Contour algorithms. The algorithm is applied to image segmentation using two different kinds of local neighborhoods in constructing the graph. The major problem with Graph-Cut approach is the incorrect selection of Liver Region with coloring similar to user’s scribbles being identified as a tumor region. Results can be improved by using the proposed new technique based on Geodesic Graph-Cut method. This system has concentrated on finding a fast and interactive segmentation method for liver and tumor segmentation. In the preprocessing stage, the CT image process is carried over with mean shift filter and statistical thresholding method for reducing processing area with improving detection rate. Second stage is Liver Segmentation; the liver region has been segmented using the algorithm of the proposed method. In the next stage, Tumor segmentation also followed the same steps. Finally the liver and tumor regions are separately segmented from the computer tomography image.
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Liver and Tumor Segmentation in CT Images
Edité par
LAP LAMBERT Academic Publishing Sep 2017, 2017
ISBN 10 : 6202053356
ISBN 13 : 9786202053358
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A new proposed method of fully automatic processing frameworks is given based on Geodesic Graph-cut Active Contour algorithms. The algorithm is applied to image segmentation using two different kinds of local neighborhoods in constructing the graph. The major problem with Graph-Cut approach is the incorrect selection of Liver Region with coloring similar to user's scribbles being identified as a tumor region. Results can be improved by using the proposed new technique based on Geodesic Graph-Cut method. This system has concentrated on finding a fast and interactive segmentation method for liver and tumor segmentation. In the preprocessing stage, the CT image process is carried over with mean shift filter and statistical thresholding method for reducing processing area with improving detection rate. Second stage is Liver Segmentation; the liver region has been segmented using the algorithm of the proposed method. In the next stage, Tumor segmentation also followed the same steps. Finally the liver and tumor regions are separately segmented from the computer tomography image. 64 pp. Englisch. N° de réf. du vendeur 9786202053358
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Liver and Tumor Segmentation in CT Images
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LAP LAMBERT Academic Publishing, 2017
ISBN 10 : 6202053356
ISBN 13 : 9786202053358
Neuf
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Paperback. Etat : Brand New. 64 pages. 8.66x5.91x0.15 inches. In Stock. N° de réf. du vendeur zk6202053356
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Liver and Tumor Segmentation in CT Images
Edité par
LAP LAMBERT Academic Publishing, 2017
ISBN 10 : 6202053356
ISBN 13 : 9786202053358
Neuf
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Vendeur : moluna, Greven, Allemagne
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ananth ChristoChristo Ananth, Researcher is a member of 133 Professional Bodies over the globe.He is serving as Editorial board member/Reviewer of 410 SCI/ISI/Scopus/Web of Science indexed International Journals and 151 Refereed and . N° de réf. du vendeur 175752656
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Liver and Tumor Segmentation in CT Images
Edité par
LAP LAMBERT Academic Publishing Sep 2017, 2017
ISBN 10 : 6202053356
ISBN 13 : 9786202053358
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -A new proposed method of fully automatic processing frameworks is given based on Geodesic Graph-cut Active Contour algorithms. The algorithm is applied to image segmentation using two different kinds of local neighborhoods in constructing the graph. The major problem with Graph-Cut approach is the incorrect selection of Liver Region with coloring similar to user's scribbles being identified as a tumor region. Results can be improved by using the proposed new technique based on Geodesic Graph-Cut method. This system has concentrated on finding a fast and interactive segmentation method for liver and tumor segmentation. In the preprocessing stage, the CT image process is carried over with mean shift filter and statistical thresholding method for reducing processing area with improving detection rate. Second stage is Liver Segmentation; the liver region has been segmented using the algorithm of the proposed method. In the next stage, Tumor segmentation also followed the same steps. Finally the liver and tumor regions are separately segmented from the computer tomography image.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch. N° de réf. du vendeur 9786202053358
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Liver and Tumor Segmentation in CT Images
Edité par
LAP LAMBERT Academic Publishing, 2017
ISBN 10 : 6202053356
ISBN 13 : 9786202053358
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A new proposed method of fully automatic processing frameworks is given based on Geodesic Graph-cut Active Contour algorithms. The algorithm is applied to image segmentation using two different kinds of local neighborhoods in constructing the graph. The major problem with Graph-Cut approach is the incorrect selection of Liver Region with coloring similar to user's scribbles being identified as a tumor region. Results can be improved by using the proposed new technique based on Geodesic Graph-Cut method. This system has concentrated on finding a fast and interactive segmentation method for liver and tumor segmentation. In the preprocessing stage, the CT image process is carried over with mean shift filter and statistical thresholding method for reducing processing area with improving detection rate. Second stage is Liver Segmentation; the liver region has been segmented using the algorithm of the proposed method. In the next stage, Tumor segmentation also followed the same steps. Finally the liver and tumor regions are separately segmented from the computer tomography image. N° de réf. du vendeur 9786202053358
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