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Proceedings Paper

Computer-aided classification of liver tumors in 3D ultrasound images with combined deformable model segmentation and support vector machine
Author(s): Myungeun Lee; Jong Hyo Kim; Moon Ho Park; Ye-Hoon Kim; Yeong Kyeong Seong; Baek Hwan Cho; Kyoung-Gu Woo
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Paper Abstract

In this study, we propose a computer-aided classification scheme of liver tumor in 3D ultrasound by using a combination of deformable model segmentation and support vector machine. For segmentation of tumors in 3D ultrasound images, a novel segmentation model was used which combined edge, region, and contour smoothness energies. Then four features were extracted from the segmented tumor including tumor edge, roundness, contrast, and internal texture. We used a support vector machine for the classification of features. The performance of the developed method was evaluated with a dataset of 79 cases including 20 cysts, 20 hemangiomas, and 39 hepatocellular carcinomas, as determined by the radiologist's visual scoring. Evaluation of the results showed that our proposed method produced tumor boundaries that were equal to or better than acceptable in 89.8% of cases, and achieved 93.7% accuracy in classification of cyst and hemangioma.

Paper Details

Date Published: 21 March 2014
PDF: 8 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341N (21 March 2014); doi: 10.1117/12.2043427
Show Author Affiliations
Myungeun Lee, Seoul National Univ. (Korea, Republic of)
Seoul National Univ. Hospital (Korea, Republic of)
Jong Hyo Kim, Seoul National Univ. (Korea, Republic of)
Seoul National Univ. Hospital (Korea, Republic of)
Moon Ho Park, Samsung Advanced Institute of Technology (Korea, Republic of)
Ye-Hoon Kim, Samsung Advanced Institute of Technology (Korea, Republic of)
Yeong Kyeong Seong, Samsung Advanced Institute of Technology (Korea, Republic of)
Baek Hwan Cho, Samsung Advanced Institute of Technology (Korea, Republic of)
Kyoung-Gu Woo, Samsung Advanced Institute of Technology (Korea, Republic of)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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