Breast cancer detection in gadolinium-enhanced MR images by static region descriptors and neural networks.

Abstract

To automate the diagnosis of malignancy by classifying breast tissues as negative or positive for malignancy in gadolinium-enhanced dynamic magnetic resonance (MR) images, using static region descriptors and a neural network classifier. We propose a novel approach whereby the classifier evaluates a number of parameters that identify important tumor characteristics, as obtained by digital image processing techniques. These parameters include static signal intensity (SI) after contrast enhancement, mass margin descriptors, evaluation of mass shape by calculation of eccentricity, mass size, and mass granularity by texture analysis. Datasets for 14 patients were obtained by use of the 1.5T PMRTOW Clinical Imager. Statistical performance evaluation of the neural networks indicated 90%-100% sensitivity, 91%-100% specificity, and 91%-100% accuracy. Although this work is preliminary, it may reduce overall health-care time and costs, and enable higher accuracy in automated breast cancer detection systems.