A new contour detection approach in mammogram using rational wavelet filtering and MRF smoothing

This paper presents a new approach to detect breast contour in mammogram. Formed from a class of rational orthogonal wavelets (ROWs), a 2-D image filter is constructed for prefiltering of the mammogram. The filtered image facilitates a simple binarisation of the mammogram. An initial breast contour is then extracted from the binarised image with a simple boundary scan technique. Based on Markov Random Field (MRF) modelling and iterated conditional modes (ICM) relaxation, a smoothing algorithm is developed to further smooth the initial contour. The proposed smoothing algorithm has a unique advantage of smoothing the breast contour while the nipple is preserved with high fidelity. In comparison with contour detection techniques relying on the calculation of varying thresholds based on histgram analysis, a single fixed ROW image filter is sufficient for all mammograms being analysed. The ROW filter is adaptive to varying statistics of mammograms regarding the pixel intensity. Results prove the robustness of the proposed detection algorithm for 82 mammograms from the Mini-MIAS database.