Mahalanobis Fuzzy C-Means Clustering with Spatial Information for Image Segmentation

A fuzzy C-Means segmentation algorithm can be implemented in an image segmentation based on the Mahalanobis distance; However, this method only needs to consider the color space situation, not the neighborhood system of the image. It was an effective edge detection process unwell performed and generated less accuracy in segmentation results. In this article, we propose a new method for image segmentation with Mahalanobis fuzzy C-means Spatial information (MFCMS). The proposed method combines feature space and images of the information of the neighborhood (spatial information) to improve the accuracy of the result of segmentation on the image. The MFCMS consists of two steps, the histogram threshold module for the first step and the MFCMS module for the second step. The Histogram Threshold module is used to get the MFCMS initialization conditions for the cluster centroid and the number of centroids. Test results show that this method provides better segmentation performance than classification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48, respectively.