Fast detection of masses in digitized mammograms

A novel method for fast detection of regions of suspicion (ROS) that contain circumscribed lesions in mammograms is presented. The position and the size of ROS are first recognized with the aid of a radial-basis-function neural network (RBFNN) by performing windowing analysis. Then a set of criteria is employed to these regions to make the final decision concerning the abnormal ones. Accelerated estimation of the high-order statistical features decreases the computational complexity 55 times in multiplication operations. The proposed method detects the exact location of the circumscribed lesions with accuracy of 72.7% (overlap between groundtruthed and detected regions greater than 50%) for mammograms containing masses, while the recognition rate for the normal ones reaches 77.7% in the MIAS database