Density Based Breast Segmentation for Mammograms Using Graph Cut and Seed Based Region Growing Techniques

Abstract

In this work we explore the application of graph cuts and seed based region growing (SBRG) techniques to segment and detect the boundary of different breast tissue regions in mammograms. The graph cut (GC) is applied with multiselection of seed labels to provide the hard constraint, whereas the seeds labels are selected based on user defined. The region growing is applied with multi-selection of threshold and the threshold values are selected based upon histogram. To enhance the representation of each tissue type, pseudocolouring is used. The main goal of this study is to evaluate the graph cut techniques in the segmentation of different breast tissue regions, which correspond to the density in mammograms. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and quantitative evaluation of density changes. Our proposed methodology has been tested on MIAS database.