Fuzzy energy based active contour model for multi-region image segmentation
Multimedia Tools and Applications
In this article, we present a new multi-phase pseudo 0.5 level set framework on fuzzy energy based active contour model to segment images into more than two regions. The proposed method is a generalization of fuzzy active contour based on 2-phase segmentation (object and background), developed by Krinidis and Chatzis. The proposed method needs only log2n pseudo 0.5 level set functions for n phase piece-wise constant case. In piece-wise smooth case, only two pseudo 0.5 level set functions are sufficient to represent any partition based on ‘the four colo theorem. The proposed fuzzy active contour model can segment images into multiple regions instead of two regions (object and background) based on curve evolution. In this article, instead of solving the Euler-Lagrange equation, a multi-phase pseudo 0.5 level set based optimization is proposed to speed up the convergence. Finally, the proposed method is compared with state-of-the-art techniques on several images. Analysis (both qualitative and quantitative) of the results concludes that the proposed method segments images into multiple regions in a better way as compared to the existing ones.
Mondal, Ajoy, "Fuzzy energy based active contour model for multi-region image segmentation" (2020). Journal Articles. 543.