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

Purpose: 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.

Materials And Methods: 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.