Combined Fuzzy Clustering and Chan_Vese Model for Multicolor Gradual Ochotona curzoniae Image Segmentation

Abstract

Precise segmentation of Ochotona curzoniae images collected in a nature scene is the basis of Ochotona curzoniae recognition and behavior analysis. Ochotona curzoniae images have the characteristics of diversity and graduality of target colors and complex background. The method of combined Chan_Vese model and k-means clustering algorithm is used to segment the multicolor images, but when k-means clustering algorithm is used to cluster the color of multicolor images, the manner of hard classification is adopted, without considering the color-gradient feature. As a resolution to this problem, a new approach of the Chan_Vese model in combination with fuzzy C-means clustering is proposed in the present paper. The proposed model utilises fuzzy C-means clustering to cluster the pixels inside the evolution curve of the Chan_Vese model, classifying the pixels into a certain color cluster with a certain probability to describe the image color gradual characteristics. By fuzzy C-means clustering, several cluster centers can be obtained, and the values of cluster centers can be used to replace internal fitting values of the Chan_Vese model. In this way, the problem that the Chan_Vese model cannot segment images with intensity inhomogeneity is overcome. Furthermore, the global Heaviside function is replaced by the local Heaviside function to suppress the influence of the background on image segmentation. The experimental results of Ochotona curzoniae images segmentation demonstrate that the proposed model can more accurately locate the target contour and has a higher Dice similarity coefficient, Jaccard Similarity, and segmentation accuracy.