Split Bregman method for minimization of region-scalable fitting energy for image segmentation

Authors:
Yunyun Yang;Chunming Li;Chiu-Yen Kao;Stanley Osher
Affiliations:
Department of Mathematics, The Ohio State University, OH and Department of Mathematics, Harbin Institute of Technology, Harbin, China;Department of Radiology, University of Pennsylvania, PA;Department of Mathematics, The Ohio State University, OH and Mathematical Biosciences Institute, The Ohio State University, OH;Department of Mathematics, University of California, Los Angeles, CA
Venue:
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Year:
2010

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Abstract

In this paper, we incorporate the global convex segmentation method and the split Bregman technique into the region-scalable fitting energy model. The new proposed method based on the region-scalable model can draw upon intensity information in local regions at a controllable scale, so that it can segment images with intensity inhomogeneity. Furthermore, with the application of the global convex segmentation method and the split Bregman technique, the method is very robust and efficient. By using a non-negative edge detector function to the proposed method, the algorithm can detect the boundaries more easily and achieve results that are very similar to those obtained through the classical geodesic active contour model. Experimental results for synthetic and real images have shown the robustness and efficiency of our method and also demonstrated the desirable advantages of the proposed method.