MSPSF: A Multi-Scale Local Intensity Measurement Function for Segmentation of Breast Thermogram

Abstract

Accurate segmentation of region(s) of abnormality (RoAs) in a grayscale breast thermogram is an essential step in the automated identification of cancer in the breast. However, low contrast, intensity nonuniformity, noise, and complex background pose challenges to this segmentation task. To overcome those challenges, a novel segmentation method that amalgamates the breast blood perfusion (BBP) model, an adaptive triangular histogram-based thresholding (ATHT) method, and a new energy functional-based level set method (LSM) is proposed here. The BBP model improves the contrast between the RoA and normal tissue region in grayscale breast thermogram. The ATHT method is used for robust initialization of the proposed LSM. To deal with the complexity caused by intensity nonuniformity and noise, we have formulated a new multiscale local intensity measurement function, called multiscale spatially weighted pixel-contribution and shape-feature-embedded force (MSPSF), which is then incorporated into a variational level set formulation and minimized using interleaved operations of the level set function to segment the RoAs accurately. Extensive experimentation on two different databases justifies the superiority of our segmentation method over the other widely used state-of-the-art segmentation methods on the basis of different qualitative and quantitative measures. We have also proposed a novel two-stage breast abnormality prediction framework based on the segmented RoA.