Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm

Ting-Yu Su; Wei-Tse Yang; Tsu-Chi Cheng; Yi Fei He; Ching-Juei Yang; Yu-Hua Fang

doi:10.1117/12.2521631

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27 March 2019 Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm

Ting-Yu Su, Wei-Tse Yang, Tsu-Chi Cheng, Yi Fei He, Ching-Juei Yang, Yu-Hua Fang

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Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 1105011 (2019) https://doi.org/10.1117/12.2521631
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

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.

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Ting-Yu Su, Wei-Tse Yang, Tsu-Chi Cheng, Yi Fei He, Ching-Juei Yang, and Yu-Hua Fang "Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 1105011 (27 March 2019); https://doi.org/10.1117/12.2521631

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