A hybrid medical image segmentation approach based on dual-front evolution model

© 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Presented at the 2005 IEEE International Conference on Image Processing (ICIP 2005), 11-14 September 2005, Genoa, Italy.DOI: 10.1109/ICIP.2005.1530179In 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)