Adaptive Double Suppressed Fuzzy C-means Clustering Image Segmentation Algorithm Based on Regional Information Entropy and Fuzzy Partition

Aiming at the pixel misclassification and poor convergence performance of the suppressed fuzzy C-means clustering algorithm in image segmentation, an adaptive double suppressed fuzzy C-means clustering image segmentation algorithm based on regional information entropy and fuzzy partition is proposed. Firstly, the regional information entropy of each region is calculated by dividing and judging the region to which the pixel belongs, and the number of initial clusters is redetermined according to the actual needs; Secondly, the regional information entropy is used to construct the correction factor, and the membership matrix of different regions is first suppressed by the correction factor; Finally, the fuzzy partition matrix is combined with the distribution characteristics of the data itself to construct the adaptive selection formula of the suppression factor, and realize the second suppression of the membership matrix. The experimental results show that the proposed algorithm, compared with the related algorithms, can improve the misclassification of pixel and the convergence performance.