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Proceedings Paper

Computer-aided diagnosis in breast MRI based on unsupervised clustering techniques
Author(s): Anke Meyer-Baese; Axel Wismueller; Oliver Lange; Gerda Leinsinger
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Paper Abstract

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.

Paper Details

Date Published: 12 April 2004
PDF: 9 pages
Proc. SPIE 5421, Intelligent Computing: Theory and Applications II, (12 April 2004); doi: 10.1117/12.542249
Show Author Affiliations
Anke Meyer-Baese, Florida State Univ. (United States)
Axel Wismueller, Florida State Univ. (United States)
Ludwig-Maximilians-Univ. Munchen (Germany)
Oliver Lange, Florida State Univ. (United States)
Ludwig-Maximilians-Univ. Munchen (Germany)
Gerda Leinsinger, Ludwig-Maximilians-Univ. München (Germany)


Published in SPIE Proceedings Vol. 5421:
Intelligent Computing: Theory and Applications II
Kevin L. Priddy, Editor(s)

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