Computer-aided diagnosis in breast MRI based on unsupervised clustering techniques

被引：18
|
作者：
Meyer-Bäse, A [1 ]
Wismüller, A [1 ]
Lange, O [1 ]
Leinsinger, G [1 ]
机构：
[1] Florida State Univ, Dept Elect & Comp Engn, Tallahassee, FL 32310 USA
来源：
INTELLIGENT COMPUTING: THEORY AND APPLICATIONS II | 2004年 / 5421卷
关键词：
fuzzy clustering based on deterministic annealing; neural gas" network; topographic ICA; computer-aided diagnosis (CAD); breast magnetic resonance imaging;
D O I：
10.1117/12.542249
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Exploratory data analysis techniques are applied to the segmentation of lesions in MRI mammography as a first step of a computer-aided diagnosis system. Three new unsupervised clustering techniques are tested on biomedical time-series representing breast MRI scans: fuzzy clustering based on deterministic annealing, "neural gas" network, and topographic independent component analysis. While the first two methods enable a correct segmentation of the lesion, the latter, although incorporating a topographic mapping, fails to detect and subclassify lesions.
引用
收藏
页码：29 / 37
页数：9
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