Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2009, Article ID 326924, 10 pages
Research Article

Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI

1Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32310, USA
2Institute for Clinical Radiology, University of Munich, 81377 Munich, Germany
3Department of Biomedical Engineering, University of Rochester, Rochester, NY 14642, USA

Received 8 September 2009; Accepted 21 December 2009

Academic Editor: Yue Joseph Wang

Copyright © 2009 A. Meyer-Baese et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An application of an unsupervised neural network-based computer-aided diagnosis (CAD) system is reported for the detection and characterization of small indeterminate breast lesions, average size 1.1 mm, in dynamic contrast-enhanced MRI. This system enables the extraction of spatial and temporal features of dynamic MRI data and additionally provides a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions. Lesions with an initial contrast enhancement 50% were selected with semiautomatic segmentation. This conventional segmentation analysis is based on the mean initial signal increase and postinitial course of all voxels included in the lesion. In this paper, we compare the conventional segmentation analysis with unsupervised classification for the evaluation of signal intensity time courses for the differential diagnosis of enhancing lesions in breast MRI. The results suggest that the computerized analysis system based on unsupervised clustering has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.