@inproceedings{99712d02246c48cd8216dd566bfd8900,
title = "Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm",
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.",
author = "Su, {Ting Yu} and Yang, {Wei Tse} and Cheng, {Tsu Chi} and He, {Yi Fei} and Yang, {Ching Juei} and Fang, {Yu Hua}",
note = "Publisher Copyright: {\textcopyright} 2019 SPIE.; International Forum on Medical Imaging in Asia 2019 ; Conference date: 07-01-2019 Through 09-01-2019",
year = "2019",
doi = "10.1117/12.2521631",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Hiroshi Fujita and Kim, {Jong Hyo} and Feng Lin",
booktitle = "International Forum on Medical Imaging in Asia 2019",
address = "United States",
}