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Advances in Artificial Neural Systems
Volume 2012 (2012), Article ID 919281, 8 pages
http://dx.doi.org/10.1155/2012/919281
Research Article

Selection of Spatiotemporal Features in Breast MRI to Differentiate between Malignant and Benign Small Lesions Using Computer-Aided Diagnosis

1Department of Computer Science, Technical University of Munich, 8574 Garching, Germany
2Department of Scientific Computing, Florida State University, Tallahassee, FL 32306-4120, USA
3Institute for Clinical Radiology, University of Munich, 81377 Munich, Germany
4Department of Electrical and Computer Engineering, FAMU/FSU College of Engineering, Tallahassee, FL 32310-6046, USA

Received 29 February 2012; Accepted 14 May 2012

Academic Editor: Juan Manuel Gorriz Saez

Copyright © 2012 F. Steinbruecker 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.

Abstract

Automated detection and diagnosis of small lesions in breast MRI represents a challenge for the traditional computer-aided diagnosis (CAD) systems. The goal of the present research was to compare and determine the optimal feature sets describing the morphology and the enhancement kinetic features for a set of small lesions and to determine their diagnostic performance. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal feature number and tested different classification techniques. The results suggest that the computerized analysis system based on spatiotemporal features 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.