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Proceedings Paper

Dependent component analysis applied to lesions' characterization in breast MRI
Author(s): Anke Meyer-Bäse; Oliver Lange; Thomas Schlossbauer; Axel Wismueller
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Paper Abstract

An application of dependent component analysis techniques is reported for the detection and characterization of small indeterminate breast lesions in dynamic contrast-enhanced MRI. These 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, this method provides both a set of prototypical time-series and a corresponding set of cluster assignment maps which further provides a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions. We present two different segmentation methods for the evaluation of signal intensity time courses for the differential diagnosis of enhancing lesions inStarting from the conventional methodology, we proceed by introducing the separate concepts of threshold segmentation and dependent component analysis and in the last step by combining those two concepts. The results suggest that the dependent component approach has the potential to increase the diagnostic accuracy of MRI mammography by improving the sensitivity without reduction of specificity.

Paper Details

Date Published: 23 April 2008
PDF: 11 pages
Proc. SPIE 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI, 69790D (23 April 2008); doi: 10.1117/12.777288
Show Author Affiliations
Anke Meyer-Bäse, Florida State Univ. (United States)
Oliver Lange, Florida State Univ. (United States)
Thomas Schlossbauer, Ludwig-Maximilians-Univ. München (Germany)
Axel Wismueller, Univ. of Rochester (United States)

Published in SPIE Proceedings Vol. 6979:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI
Harold H. Szu; F. Jack Agee, Editor(s)

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