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Proceedings Paper

Comparing the role of shape and texture on staging hepatic fibrosis from medical imaging
Author(s): Xuejun Zhang; Ryan Louie; Brent J. Liu; Xin Gao; Xiaomin Tan; Xianghe Qu; Liling Long
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Paper Abstract

The purpose of this study is to investigate the role of shape and texture in the classification of hepatic fibrosis by selecting the optimal parameters for a better Computer-aided diagnosis (CAD) system. 10 surface shape features are extracted from a standardized profile of liver; while15 texture features calculated from gray level co-occurrence matrix (GLCM) are extracted within an ROI in liver. Each combination of these input subsets is checked by using support vector machine (SVM) with leave-one-case-out method to differentiate fibrosis into two groups: normal or abnormal. The accurate rate value of all 10/15 types number of features is 66.83% by texture, while 85.74% by shape features, respectively. The irregularity of liver shape can demonstrate fibrotic grade efficiently and texture feature of CT image is not recommended to use with shape feature for interpretation of cirrhosis.

Paper Details

Date Published: 25 March 2016
PDF: 9 pages
Proc. SPIE 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, 97890W (25 March 2016); doi: 10.1117/12.2216560
Show Author Affiliations
Xuejun Zhang, Guangxi Univ. (China)
The Univ. of Southern California (United States)
Ryan Louie, The Univ. of Southern California (United States)
Brent J. Liu, The Univ. of Southern California (United States)
Xin Gao, Suzhou Institute of Biomedical Engineering and Technology (China)
Xiaomin Tan, Guangxi Univ. (China)
Xianghe Qu, Guangxi Univ. (China)
Liling Long, Guangxi Medical Univ. (China)


Published in SPIE Proceedings Vol. 9789:
Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations
Jianguo Zhang; Tessa S. Cook, Editor(s)

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