title: Improving the Classification of Cirrhotic Liver by using Texture Features.
abstract: We have been developing a computer-aided diagnosis (CAD) system for distinguishing the cirrhosis in MR images by shape and texture analysis. Two shape features are calculated from a segmented liver region, and seven texture features are quantified by using grey level difference method (GLDM) within the small region-of-interests (ROIs). The degree of cirrhosis is derived from integrating the shape and texture features of the liver into a three-layer feed-forward artificial neural network (ANN). A liver is regarded as cirrhosis if the percentage of the ROIs with a degree over 0.5 is greater than 50%. The initial experimental result showed that the ANN can learn all of the patterns in the training data sets. In testing of the whole liver regions, 82% cirrhosis and 100% normal cases were correctly differentiated from 18 test cases, that indicates our proposed method is effective to the cirrhosis prediction on MRI.
authors.affiliation: Dept. of Intelligent Image Inf., Gifu Univ.
authors.forename: Xuejun
authors.initials: X
authors.lastname: Zhang
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Fujita
authors.forename: Masayuki
authors.initials: M
authors.lastname: Kanematsu
authors.forename: Xiangrong
authors.initials: X
authors.lastname: Zhou
authors.forename: Takeshi
authors.initials: T
authors.lastname: Hara
authors.forename: Hiroki
authors.initials: H
authors.lastname: Kato
authors.forename: Ryujiro
authors.initials: R
authors.lastname: Yokoyama
authors.forename: Hiroaki
authors.initials: H
authors.lastname: Hoshi