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Segmentation and classification of dynamic breast magnetic resonance image data

[+] Author Affiliations
Axel Wismüller

Florida State University, Department of Electrical and Computer Engineering, Tallahassee, Florida 32310-6046 and Ludwig-Maximilians University, Department of Clinical Radiology, Munich 80336, Germany

Anke Meyer-Bäse, Oliver Lange

Florida State University, Department of Electrical and Computer Engineering, Tallahassee, Florida 32310-6046

Thomas Schlossbauer

Ludwig-Maximilians University, Department of Clinical Radiology, Munich 80336, Germany

Maria Kallergi

University of South Florida, Department of Radiology, Tampa, Florida 33612-4799

Maximilian F. Reiser, Gerda Leinsinger

Ludwig-Maximilians University, Department of Clinical Radiology, Munich 80336, Germany

J. Electron. Imaging. 15(1), 013020 (March 03, 2006). doi:10.1117/1.2178776
History: Received January 06, 2005; Revised June 15, 2005; Accepted August 26, 2005; Published March 03, 2006
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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.

© 2006 SPIE and IS&T

Citation

Axel Wismüller ; Anke Meyer-Bäse ; Oliver Lange ; Thomas Schlossbauer ; Maria Kallergi, et al.
"Segmentation and classification of dynamic breast magnetic resonance image data", J. Electron. Imaging. 15(1), 013020 (March 03, 2006). ; http://dx.doi.org/10.1117/1.2178776


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