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

Anke Meyer-Baese; Axel Wismueller M.D.; Oliver Lange; Gerda Leinsinger

doi:10.1117/12.542249

12 April 2004 Computer-aided diagnosis in breast MRI based on unsupervised clustering techniques

Anke Meyer-Baese, Axel Wismueller M.D., Oliver Lange, Gerda Leinsinger

Proceedings Volume 5421, Intelligent Computing: Theory and Applications II; (2004) https://doi.org/10.1117/12.542249
Event: Defense and Security, 2004, Orlando, Florida, United States

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.

Citation Download Citation

Anke Meyer-Baese, Axel Wismueller M.D., Oliver Lange, and Gerda Leinsinger "Computer-aided diagnosis in breast MRI based on unsupervised clustering techniques", Proc. SPIE 5421, Intelligent Computing: Theory and Applications II, (12 April 2004); https://doi.org/10.1117/12.542249

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