Investigation on Micro-Calcifications for Breast Cancer Via DWT and BPNN

Abstract

A high-sensitivity computer-aided diagnosis algorithm which can detect and quantify micro- calcifications for early-stage breast cancer. The algorithm can be divided into two phases: image reconstruction and recognition on micro-calcification regions. For Phase ?, the suspicious micro-calcification regions are separated from the normal tissues by wavelet layers and Renyi’s information theory. The Morphology-Dilation and Majority Voting Rule are employed to reconstruct the scattered regions of suspicious micro-calcification. For Phase ?, total 31 descriptors which mainly includes shape inertia, compactness, eccentricity and grey-level co-occurrence matrix are introduced to define the characteristics of the suspicious micro-calcification clusters. In order to reduce the computation load, principal component analysis is used to transform these descriptors to a compact but efficient expression by linear combination method. The efficacy of back-propagation neural network classifier exhibits its superiority in terms of high true positive rate (TP rate) and low false positive (FP rate) rate, in comparison to other classifier.