A spatial clustering method with edge weighting for image segmentation

As one of the best image clustering methods, fuzzy local information C-means is often used for image segmentation. The effects of noise are avoided by utilizing the spatial relationship among pixels, but it often generates boundary zones for the mix pixels around the edges. This letter presents an image spatial clustering method, called fuzzy C-means with edge and local information (FELICM), which reduces the edge degradation by introducing the weights of pixels within local neighbor windows. The edges are extracted at first by Canny edge detection. During detection, two adaptive thresholds obtained by multi-Otsu method are used. Then, different weights are set according to whether the window center and the local neighbors are separated by an edge or not. Pixels, together with different weighted local neighbors, are clustered iteratively, until the final clustering result is obtained. The method can be directly applied to the image without any filter preprocessing, and the experimental results over remote sensing images show that FELICM not only effectively solves the problem of isolated and random distribution of pixels inside regions but also obtains high edge accuracies.