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Journal of Electronic Imaging

Segmentation and classification of dynamic breast magnetic resonance image data
Author(s): Axel Wismüller; Anke Meyer-Bäse; Oliver Lange; Thomas Schlossbauer; Maria Kallergi; Maximilian Reiser; Gerda Leinsinger
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Paper Abstract

An application of an unsupervised self-organizing neural network—the minimal free energy vector quantization neural network—is reported for the detection and characterization of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (MRI). This technique enables 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 three different segmentation methods for the evaluation of signal intensity time courses for the differential diagnosis of enhancing lesions in breast MRI. Starting from the conventional methodology, we proceed by introducing the separate concepts of threshold segmentation and cluster analysis based on the minimal free energy vector quantization neural network, and in the last step by combining those two concepts. The results suggest that the minimal free energy vector quantization neural network has the potential to increase the diagnostic accuracy of MRI mammography by improving sensitivity without reduction of specificity.

Paper Details

Date Published: 1 January 2006
PDF: 13 pages
J. Electron. Imag. 15(1) 013020 doi: 10.1117/1.2178776
Published in: Journal of Electronic Imaging Volume 15, Issue 1
Show Author Affiliations
Axel Wismüller, Ludwig-Maximilians-Univ. München (Germany)
Anke Meyer-Bäse, Florida State Univ. (United States)
Oliver Lange, Florida State Univ. (United States)
Thomas Schlossbauer, Ludwig-Maximilians-Univ. München (Germany)
Maria Kallergi, Univ. of South Florida (United States)
Maximilian Reiser, Ludwig-Maximilians-Univ. München (Germany)
Gerda Leinsinger

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