Computer-aided classification of liver tumors in 3D ultrasound images with combined deformable model segmentation and support vector machine

Myungeun Lee; Jong Hyo Kim; Moon Ho Park; Ye-Hoon Kim; Yeong Kyeong Seong; Baek Hwan Cho; Kyoung-Gu Woo

doi:10.1117/12.2043427

21 March 2014 Computer-aided classification of liver tumors in 3D ultrasound images with combined deformable model segmentation and support vector machine

Myungeun Lee, Jong Hyo Kim, Moon Ho Park, Ye-Hoon Kim, Yeong Kyeong Seong, Baek Hwan Cho, Kyoung-Gu Woo

Author Affiliations +

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90341N (2014) https://doi.org/10.1117/12.2043427
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

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Myungeun Lee, Jong Hyo Kim, Moon Ho Park, Ye-Hoon Kim, Yeong Kyeong Seong, Baek Hwan Cho, and Kyoung-Gu Woo "Computer-aided classification of liver tumors in 3D ultrasound images with combined deformable model segmentation and support vector machine", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341N (21 March 2014); https://doi.org/10.1117/12.2043427

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KEYWORDS

Tumors

Image segmentation

3D modeling

Ultrasonography

Liver

Tumor growth modeling

3D image processing

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