Medical Image Segmentation by Level Set Method Incorporating Region and Boundary Statistical Information
Level set methods are powerful numerical techniques for image segmentation and analysis. This method requires the definition of a speed function that governs curve evolution. However, the classical method only used image gradient, edge strength, and region intensity to define the speed function. In this paper, we present a new speed function for level set framework. The new method integrates the image region statistical information and image boundary statistical information instead of the conventional method that uses spatial image gradient information. The new speed function gives the level set method a global view of the boundary information within the image. The method here proposed is particularly well adapted to situations where edges are weak and overlap, and images are noisy. A number of experiments on ultrasound, CT, and X-ray modalities medical images were performed to evaluate the new method. The experimental results demonstrate the reliability and efficiency of this new scheme.
- 2.Malladi, R., Sethian, J., Vemuri, B.: Shape modeling with front propagation: A level set approach. IEEE T-PAMI 17(2), 158–175 (1995)Google Scholar
- 6.Xu, C., Jr. Yezzi, A.: J. Prince. On the relationship between parametric and geometric active contours. Technical Report JHU/ECE 99-14 (December 1999)Google Scholar
- 7.Baillard, C., Barillot, C.: Robust 3D segmentation of anatomical structures with level sets. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 237–245. Springer, Heidelberg (2000)Google Scholar
- 8.Voorhees, H., Poggio, T.: Detecting textons and texture boundaries in natural images. In: Proc.of the International Conference on Computer Vision, pp. 250–258 (1987)Google Scholar