Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Space Medicine & Medical Engineering》 2004-02
Add to Favorite Get Latest Update

B-Scan Ultrasonic Image Recognition of Fatty Liver Based on Texture Analysis

WANG Xiao-yi,LIN Jiang-li,LI De-yu,WANG Tian-fu,ZHENG Chang-qiong,CHENG Yin-rong Department of Biomedical Engineering, Sichuan University, Chengdu 610065, China  
Objective To provide a computer-aided method for diagnosis of fatty liver by B-scan ultrasonic imaging. Method Fatty liver referred to the infiltration of triglycerides and other fats into liver cells, which affected the texture of the liver tissue. In this paper, texture features including angular second moment, entropy and inverse differential moment were calculated from gray-level co-occurrence matrices of B-scan ultrasonic liver images. Feature vectors indicating two classes of the images were established with the three features. Then these vectors were classified using k-means clustering algorithm and self-organized feature mapping (SOFM) artificial neural network. Result The accuracy rates of k-means clustering algorithm were 63.6% for normal liver and 90.9% for fatty liver. The neural network algorithm showed accuracy rate of 84.8% for normal liver and 90.9% for fatty liver. Conclusion This technology shows the characteristics of B-scan images of both normal liver and fatty liver more accurately than eyes do. It greatly improves the diagnosing accuracy of fatty liver.
【Fund】: 四川省应用基础研究资助项目 ( 0 3JY0 2 9 0 72 2 )
【CateGory Index】: R445.1
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved