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

Abstract

A fuzzy C-Means segmentation algorithm can be implemented in an image segmentationbased on the Mahalanobis distance; However, this method only needs to consider the colorspace situation, not the neighborhood system of the image. It was an effective edge detectionprocess 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 Spatialinformation (MFCMS). The proposed method combines feature space and images of theinformation of the neighborhood (spatial information) to improve the accuracy of the result ofsegmentation on the image. The MFCMS consists of two steps, the histogram threshold modulefor the first step and the MFCMS module for the second step. The Histogram Threshold moduleis used to get the MFCMS initialization conditions for the cluster centroid and the number ofcentroids. Test results show that this method provides better segmentation performance thanclassification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48,respectively.