A cost-sensitive extension of AdaBoost with markov random field priors for automated segmentation of breast tumors in ultrasonic images

Abstract

A cost-sensitive extension of AdaBoost based on Markov random field (MRF) priors was developed to train an ensemble segmentation process which can avoid irregular shape, isolated points and holes, leading to lower error rate. The method was applied to breast tumor segmentation in ultrasonic images. A cost function was introduced into the AdaBoost algorithm that penalizes dissimilar adjacent labels in MRF regularization. The extended AdaBoost algorithm generates a series of weak segmentation processes by sequentially selecting a process whose error rate weighted by the cost is minimum. The method was tested by generation of an ensemble segmentation process for breast tumors in ultrasonic images. This was followed by a active contour to refine the extracted tumor boundary. The segmentation performance was evaluated by tenfold cross validation test, where 300 carcinomas, 50 fibroadenomas, and 50 cysts were used. The experimental results revealed that the error rate of the proposed ensemble segmentation was two-thirds the error rate of the segmentation trained by AdaBoost without MRF. By combining the ensemble segmentation with a geodesic active contour, the average Jaccard index between the extracted tumors and the manually segmented true regions was 93.41%, significantly higher than the conventional segmentation process. A cost-sensitive extension of AdaBoost based on MRF priors provides an efficient and accurate means for the segmentation of tumors in breast ultrasound images.