Breast Mass Segmentation Using Iterated Graph Cuts Based on Multi-Scale Smoothing

Abstract

A novel scheme for mass segmentation in mammography is proposed,which is based on Graph Cuts algorithm and multi-scale analysis.Mammogram is segmented by statistical region merging firstly,and the obtained rough contour is used as the initial contour for Graph Cuts segmentation.In iterative optimization stage of the algorithm,multi-scale analysis method is introduced to estimate the Gaussian Mixture Model(GMM)parameters with pyramidal decomposing serial images instead of fix-scale original image.The algorithm estimates GMM parameters rapidly with fewer samples by utilizing the complementarities between segmentation accuracy of fine scale and segmentation easiness of coarse scale.In order to improve efficiency of the proposed approach,watershed algorithm is utilized to produce a region adjacency graph,replacing pixel adjacency graph with fewer samples.The proposed method,interactive Graph Cuts and GrabCut were simultaneously applied for mass segmentation on 110 mammographic ROIs,and the achieved average ratios of misclassification error are 1.57,3.46 and 5.01 respectively.The results demonstrate that the proposed method achieves a better performance in accuracy and robustness.