Active Contour Method based on Region-Scalable Fitting and Hausdorff Distance for Medical Image Segmentation

In this study, a new local distance-based active contour model is proposed. An energy function based on the regional-scalable fitting term and local hausdorff distance term is formulated. The RSF term is dominant near object boundary and responsible for attracting the level set contour towards object boundaries, and local hausdorff distance term including the local region similarity information improves the robustness of the proposed method. Compared to the active contour method driven by region-scalable fitting and local Bhattacharyya distance energies (RSFB), the proposed method has the relatively fast convergence and improves the robustness to the parameter selection. Our method can overcome the weak boundaries and noise problem in images. A level set function is used to define the partition of image domain into two disjoined regions. Experiment results demonstrate the desirable performance of the proposed method with relativity less iterations and time.