An Adaptive Fuzzy C-Means Noise Image Segmentation Algorithm Combining Local and Regional Information

Noisy image segmentation using fuzzy c-means clustering (FCM) algorithm is an important direction in the field of image segmentation, among which FCM using local information or nonlocal information constraints has made some progress. However, as the noise density increases, the local information cannot restore the real image. The nonlocal information-based segmentation method overly retains the noise and thus produces false edges resulting in the degradation of image segmentation accuracy (SA). To address these problems, this article uses regional and local information to develop an adaptive FCM image segmentation algorithm. First, the regional information and local information are obtained by the regional information filtering method and the local information filtering method. Then, the weighted image is constructed in a certain way. Second, to make the objective function adaptively adjust the constraint ratio of the original image and the weighted image pixel by pixel, the degree of constraint is adaptively changed by using the difference between them as the coefficients of both through certain transformation. Finally, to speed up the convergence of the objective function, we add the number of clustering samples to the denominator of the objective function by certain transformations to reduce the number of iterations of the objective function. The proposed algorithm achieves more than 90% in image SA and mean Intersection-over-Union through synthetic and real-world image tests and acquires good performance in other indexes.