Differentiation of cirrhosis from normal liver based on textural features via T1WI computer-aided diagnosis with a genetic algorithm

DOI

10.2991/icmii-15.2015.59How to use a DOI?

Keywords

Cirrhosis; Magnetic resonance imaging; Texture feature; BP classifier; Genetic algorithm.

Abstract

A computer-aided diagnosis (CAD) system for classification of liver cirrhosis from MRI is presented. The system consists of feature extraction and selection, classification, and classifier optimization modules. In general, biomedical imaging is based on textural features, visualized via grey level co-occurrence matrices. However, these features are so numerous that it is difficult to determine which are the most effective for classification. Then feature selection was facilitated by application of a box plot. In addition to ensure the stability of the back-propagation (BP) classifier and improve its performance, a genetic algorithm (GA) was incorporated. We demonstrated that the proposed CAD system is suitable for differentiation through analysis of 170 regions of interest in T1WIs of advanced cirrhosis and normal livers. The GA improved classification performance of the BP classifier, allowing fewer iterations, less time expense, and a high accuracy rate.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dong-Mei Guo
AU  - Hui Liu
AU  - Tian-Shuang Qiu
AU  - Xiang-Bo Lin
PY  - 2015/10
DA  - 2015/10
TI  - Differentiation of cirrhosis from normal liver based on textural features via T1WI computer-aided diagnosis with a genetic algorithm
BT  - Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics
PB  - Atlantis Press
SP  - 328
EP  - 331
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmii-15.2015.59
DO  - 10.2991/icmii-15.2015.59
ID  - Guo2015/10
ER  -