Abstract
Breast ultrasound (BUS) image segmentation is a very challenge task because of the poor image quality. In this paper, we proposed a probability model-based method for the accurate and robust segmentation for low quality medical images. It combines the spatial priori knowledge with the frequency constraints under the maximum a posteriori probability with markov random field (MAP-MRF) segmentation frameworks. The spatial constraints model the global location, object pose and the appearance, and the objective boundary is constrained in the frequency domain via modeling the phase feature and the zero crossing feature of the wavelet coefficients. The proposed method is applied to a breast ultrasound database with 131 cases, and its performance is evaluated by area error metrics and boundary error metrics. In comparing with the state of the art, our method is more accurate and robust in segmenting breast ultrasound images.
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