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Automatic liver segmentation on computed tomography using random walkers for treatment planning

Segmentation of the liver from Computed Tomography (CT) volumes plays an important role during the choice of treatment strategies for liver diseases. Despite lots of attention, liver segmentation remains a challenging task due to the lack of visible edges on most boundaries of the liver coupled w...

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Prif Awduron: Moghbel, Mehrdad, Mashohor, Syamsiah, Mahmud, Rozi, Saripan, M. Iqbal
Fformat: Erthygl
Iaith:English
Cyhoeddwyd: IfADo - Leibniz Research Centre for Working Environment and Human Factors 2016
Mynediad Ar-lein:http://psasir.upm.edu.my/id/eprint/55179/1/Automatic%20liver%20segmentation%20on%20computed%20tomography%20using%20random%20walkers%20for%20treatment%20planning.pdf
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spelling oai:psasir.upm.edu.my:55179 http://psasir.upm.edu.my/id/eprint/55179/ Automatic liver segmentation on computed tomography using random walkers for treatment planning Moghbel, Mehrdad Mashohor, Syamsiah Mahmud, Rozi Saripan, M. Iqbal Segmentation of the liver from Computed Tomography (CT) volumes plays an important role during the choice of treatment strategies for liver diseases. Despite lots of attention, liver segmentation remains a challenging task due to the lack of visible edges on most boundaries of the liver coupled with high variability of both intensity patterns and anatomical appearances with all these difficulties becoming more prominent in pathological livers . To achieve a more accurate segmentation, a random walker based framework is proposed that can segment contrast-enhanced livers CT images with great accuracy and speed. Based on the location of the right lung lobe, the liver dome is automatically detected thus eliminating the need for manual initialization. The computational requirements are further minimized utilizing rib-caged area segmentation, the liver is then extracted by utilizing random walker method. The proposed method was able to achieve one of the highest accuracies reported in the literature against a mixed healthy and pathological liver dataset compared to other segmentation methods with an overlap error of 4.47 % and dice similarity coefficient of 0.94 while it showed exceptional accuracy on segmenting the pathological livers with an overlap error of 5.95% and dice similarity coefficient of 0.91. IfADo - Leibniz Research Centre for Working Environment and Human Factors 2016 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/55179/1/Automatic%20liver%20segmentation%20on%20computed%20tomography%20using%20random%20walkers%20for%20treatment%20planning.pdf Moghbel, Mehrdad and Mashohor, Syamsiah and Mahmud, Rozi and Saripan, M. Iqbal (2016) Automatic liver segmentation on computed tomography using random walkers for treatment planning. EXCLI Journal, 15. pp. 500-517. ISSN 1611-2156 http://www.excli.de/ 10.17179/excli2016-473
institution UPM IR
collection UPM IR
language English
description Segmentation of the liver from Computed Tomography (CT) volumes plays an important role during the choice of treatment strategies for liver diseases. Despite lots of attention, liver segmentation remains a challenging task due to the lack of visible edges on most boundaries of the liver coupled with high variability of both intensity patterns and anatomical appearances with all these difficulties becoming more prominent in pathological livers . To achieve a more accurate segmentation, a random walker based framework is proposed that can segment contrast-enhanced livers CT images with great accuracy and speed. Based on the location of the right lung lobe, the liver dome is automatically detected thus eliminating the need for manual initialization. The computational requirements are further minimized utilizing rib-caged area segmentation, the liver is then extracted by utilizing random walker method. The proposed method was able to achieve one of the highest accuracies reported in the literature against a mixed healthy and pathological liver dataset compared to other segmentation methods with an overlap error of 4.47 % and dice similarity coefficient of 0.94 while it showed exceptional accuracy on segmenting the pathological livers with an overlap error of 5.95% and dice similarity coefficient of 0.91.
format Article
author Moghbel, Mehrdad
Mashohor, Syamsiah
Mahmud, Rozi
Saripan, M. Iqbal
spellingShingle Moghbel, Mehrdad
Mashohor, Syamsiah
Mahmud, Rozi
Saripan, M. Iqbal
Automatic liver segmentation on computed tomography using random walkers for treatment planning
author_facet Moghbel, Mehrdad
Mashohor, Syamsiah
Mahmud, Rozi
Saripan, M. Iqbal
author_sort Moghbel, Mehrdad
title Automatic liver segmentation on computed tomography using random walkers for treatment planning
title_short Automatic liver segmentation on computed tomography using random walkers for treatment planning
title_full Automatic liver segmentation on computed tomography using random walkers for treatment planning
title_fullStr Automatic liver segmentation on computed tomography using random walkers for treatment planning
title_full_unstemmed Automatic liver segmentation on computed tomography using random walkers for treatment planning
title_sort automatic liver segmentation on computed tomography using random walkers for treatment planning
publisher IfADo - Leibniz Research Centre for Working Environment and Human Factors
publishDate 2016
url http://psasir.upm.edu.my/id/eprint/55179/1/Automatic%20liver%20segmentation%20on%20computed%20tomography%20using%20random%20walkers%20for%20treatment%20planning.pdf
_version_ 1782723408191029248
score 12.933938

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