CLASSIFICATION OF ULTRASONIC IMAGE TEXTURE BY STATISTICAL DISCRIMINANT-ANALYSIS AND NEURAL NETWORKS

被引：40
|
作者：
DAPONTE, JS
SHERMAN, P
机构：
[1] Computer Science Department, Southern Connecticut State University, New Haven
[2] Department of Computer Science and Engineering, University of Bridgeport, Bridgeport
来源：
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS | 1991年 / 15卷 / 01期
关键词：
ULTRASOUND; IMAGE TEXTURE; LINEAR DISCRIMINANT ANALYSIS; NEURAL NETWORKS; NEAREST NEIGHBOR ANALYSIS;
D O I：
10.1016/0895-6111(91)90100-A
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
In this paper the ability of two common statistical discriminant analysis procedures are compared with two commercial neural network software packages. The major objective of this study was to determine which of the procedures could best discriminate between normal and abnormal ultrasonic liver textures. The same set of features were input into both statistical discriminant analysis procedures and both neural network models. Preliminary results have found the restricted Coulomb Energy (RCE) neural network model to have a testing accuracy of 90.6% which is approximately 10% better than any of the other techniques investigated.
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页码：3 / 9
页数：7
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