A Fuzzy Geometric Active Contour Method for Image Segmentation

Abstract

SUMMARYIn this paper, we propose a hybrid fuzzy geometric active contour method, which embeds the spatial fuzzy clustering into the evolution of geometric active contour. In every iteration, the evolving curve works as a spatial constraint on the fuzzy clustering, and the clustering result is utilized to construct the fuzzy region force. On one hand, the fuzzy region force provides a powerful capability to avoid the leakages at weak boundaries and enhances the robustness to various noises. On the other hand, the local information obtained from the gradient feature map contributes to locating the object boundaries accurately and improves the performance on the images with heterogeneous foreground or background. Experimental results on synthetic and real images have shown that our model can precisely extract object boundaries and perform better than the existing representative hybrid active contour approaches. key words: geometric active contour, spatial fuzzy clustering, level set method, image segmentation

3D Flow Entropy Contour Fitting Segmentation Algorithm Based on Multi-Scale Transform Contour Constraint

“…Classic segmentation methods include the Otsu method, maximum entropy method [1,2], watershed algorithm [3], active contour method [4][5][6] and so on. Among them, the Otsu method uses selection of the global optimal threshold to segment an image.…”

“…When the scale changes, the separable scale image can be extracted. Based on the theory of variable scale theory, an improved formula of top-hat and bottom-hat structure (5)…”