Detecting microcalcification clusters in digital mammograms: Study for inclusion into computer aided diagnostic prompting system
Detecting Microcalcification Clusters in Digital Mammograms: Study for Inclusion into Computer Aided Diagnostic Prompting System By Gopalkrishna Veni Emma Regentova, Ph.D. Examination Committee Chair Associate Professor of Electrical Engineering University of Nevada, Las Vegas Among signs of breast cancer encountered in digital mammograms radiologists point to microcalcification clusters (MCCs) Their detection is a challenging problem from both medical and image processing point of views. This work presents two concurrent methods for MCC detection, and studies their possible inclusion to a computer aided diagnostic prompting system. One considers Wavelet Domain Hidden Markov Tree (WHMT) for modeling microcalcification edges. The model is used for differentiation between MC and non MC edges based on the weighted maximum likelihood (WML) values. The classification of objects is carried out using spatial filters. The second method employs SUSAN edge detector in the spatial domain for mammogram segmentation. Classification of objects as calcifications is carried out using another set of spatial filters and Feedforward Neural Network (NN) A same distance filter is employed in both methods to find true clusters. The analysis of two methods is performed on 54 image regions from the mammograms selected randomly from DDSM database, including benign and cancerous cases as well as cases which can be classified as "hard cases" from both radiologists and the computer perspectives. WHMT/WML is able to detect 98.15%