Feature-based fuzzy classification for interpretation of mammograms

被引：44
|
作者：
Iyer, NS
Kandel, A
Schneider, M
机构：
[1] Univ S Florida, Dept Comp Sci & Engn, Tampa, FL 33620 USA
[2] Tel Aviv Univ, Dept Elect Engn Syst, IL-69978 Tel Aviv, Israel
来源：
FUZZY SETS AND SYSTEMS | 2000年 / 114卷 / 02期
关键词：
fuzzy c-means clustering; binary decision tree; convariance matrix; norm distance measure; membership functions; medicine; artificial intelligence;
D O I：
10.1016/S0165-0114(98)00175-4
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Methods in fuzzy logic have been applied to serve as secondary classifier for a hierarchical classification model. The use of this model in interpretation of mammograms is discussed. Also is discussed, the inevitability of using a fuzzy approach in the problem. Finally, the two different fuzzy approaches for secondary classification are compared on basis of their performance as far as clustering is concerned. The idea of using a fuzzy covariance matrix [5,6] in the distance metric of the classical c-means algorithm [1-3] has also been tried. (C) 2000 Published by Elsevier Science B.V. All rights reserved.
引用
收藏
页码：271 / 280
页数：10
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