Adaptive Probabilistic Thresholding Method for Accurate Breast Region Segmentation in Mammograms

Abstract

Segmentation of the breast region is usually the first step in the analysis of mammograms. Due to the non-uniformity of the background, breast segmentation presents several difficulties especially for film based mammograms. Our experimental results show that 50% of digitized film based mammograms in the mini-MIAS database do not have uniform intensity in the background. For this reason, applying a global thresholding method produces inaccurate results. In addition, finding the optimal global threshold value by only using histogram information requires a reliable objective function that characterizes the statistics of the background and the mammogram regions in the digitized mammograms. A second way to find the boundary of the breast consists in fitting a deformable model, such as snakes, on the mammogram. However, this method has three main shortcomings. First, the model must be initialized near the boundary. Second, using gradient information in the objective function can push the boundary toward the tissues inside the breast rather than the actual boundary. Third, in some mammograms the breast region is occluded by artifacts, such as labels, that have high gradient values on their boundary and cause the deformable model to be fitted on the artifact. To address these problems we propose a probabilistic adaptive thresholding method that uses texture information and its probability to find the most probable threshold values for specific parts of the mammogram. The experimental results on mini-MIAS database show that our proposed method outperforms the state-of-art methods and improves the accuracy at least 37% in comparison with the best results obtained by contour growing methods.