A Hybrid Medical Image Segmentation Approach Based on Dual-Front Evolution Model
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In this paper, a hybrid medical image segmentation approach is proposed based on a dual front evolution and fast sweeping evolution. This approach is composed of two stages. In the first stage, a fast sweeping evolution with a stopping criterion based upon gradient information is adopted to give a fast and rough initial boundary estimate close to (or overlapping) the actual boundary. Next, a morphological dilation is used to expand this boundary to a narrow region large enough to contain the actual boundary. In the second stage, a dual front evolution model is used to refine the final segmentation result. In this step, the evolution speeds consider the gradient information together with less local image statistics to improve the veracity and compatibility of the algorithm. The experimental results show that this two-stage algorithm can provide close, smooth and accurate final contours with low computational complexity O(N).