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Proceedings Paper

Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm
Author(s): Ting-Yu Su; Wei-Tse Yang; Tsu-Chi Cheng; Yi Fei He; Ching-Juei Yang; Yu-Hua Fang
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Paper Abstract

In this paper, a new computer-aided diagnosis system is proposed to automatically diagnose liver cirrhosis based on fourphases CT images, which included non-contrast phase, arterial phase, delay phase and portal venous phase. It is developed for the purpose of discriminating the cirrhosis into mild or severe level by automatic liver segmentation method and classification method using machine learning algorithm. First, the gradient-inverse map of CT images are calculated to derive the relative-smooth features in local area. Then we compared the centroid and area of each binary labeled groups through each slice to quantitatively extract the volume of interest (VOI) of liver automatically. In classification step, some first-order features and texture features are calculated to describe the intensity representation of liver parenchyma. Some parameters are also used to quantify the distribution of intensity in VOI. By the way, we also quantified the shape of VOI and derived some structural features. Finally, the trained support vector machine (SVM) and Neural Network (NN) classifier is applied to classify the subjects into clinical stages of the liver cirrhosis.

Paper Details

Date Published: 27 March 2019
PDF: 6 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 1105011 (27 March 2019); doi: 10.1117/12.2521631
Show Author Affiliations
Ting-Yu Su, National Cheng Kung Univ. (Taiwan)
Wei-Tse Yang, National Cheng Kung Univ. (Taiwan)
Tsu-Chi Cheng, National Cheng Kung Univ. (Taiwan)
Yi Fei He, National Cheng Kung Univ. (Taiwan)
Ching-Juei Yang, National Cheng Kung Univ. (Taiwan)
National Cheng Kung Univ. Hospital (Taiwan)
Yu-Hua Fang, National Cheng Kung Univ. (Taiwan)

Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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