12 April 2004 Computer-aided diagnosis in breast MRI based on unsupervised clustering techniques
Author Affiliations +
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.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Anke Meyer-Baese, Anke Meyer-Baese, Axel Wismueller, Axel Wismueller, Oliver Lange, Oliver Lange, Gerda Leinsinger, 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); doi: 10.1117/12.542249; https://doi.org/10.1117/12.542249
PROCEEDINGS
9 PAGES


SHARE
Back to Top