A computer aided detection system for digital mammograms based on radial basis functions and feature extraction techniques.
An intelligent Computer-Aided Detection system (CAD) can be very helpful in detecting and diagnosing breast abnormalities earlier and faster than typical screening programs. In this paper, a system based on Radial Basis neural networks coupled with feature extraction techniques for detecting breast abnormalities in digital mammograms is presented. Suspicious regions are identified following a run of the trained neural network. Within this work, 322 breast images from the MIAS database are considered. Five co-occurrence matrices are constructed at different distances for each suspicious region. A number of statistical features are used to train and test the Radial Basis neural network presented. An average recognition rate of 87% was achieved. Using Receiver Operating Characteristic (ROC) analysis, the overall sensitivity of the technique measured by Az was found to be 0.91.
Jirari
Mohammed
M
Department of Computer Science, Kent State University, Kent, Ohio 44242 USA, mjirari@cs.kent.edu.