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Proceedings Paper

Computer-aided diagnosis in breast MRI based on ICA and unsupervised clustering techniques
Author(s): Anke Meyer-Baese; Oliver Lange; Axel Wismuller; 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. ICA and clustering techniques are tested on biomedical time-series representing breast MRI scans. This techniques enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By revealing regional properties of contrast-agent uptake characterized by subtle differences of signal amplitude and dynamics, these methods provide both a set of prototypical time-series and a corresponding set of cluster assignment maps which further provide a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions.

Paper Details

Date Published: 28 March 2005
PDF: 12 pages
Proc. SPIE 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, (28 March 2005); doi: 10.1117/12.601007
Show Author Affiliations
Anke Meyer-Baese, Florida State Univ. (United States)
Oliver Lange, Florida State Univ. (United States)
Ludwig-Maximilians-Univ. Munchen (Germany)
Axel Wismuller, Florida State Univ. (United States)
Ludwig-Maximilians-Univ. Munchen (Germany)
Gerda Leinsinger, Ludwig-Maximilians-Univ. Munchen (Germany)


Published in SPIE Proceedings Vol. 5818:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III
Harold H. Szu, Editor(s)

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