Improving fuzzy algorithms for automatic image segmentation

This paper seeks an answer to the question: Can the fuzzy k-means (FKM), c-means (FCM), kernelized FCM (KFCM), and spatial constrained (SKFCM) work automatically without pre-define number of clusters. We present automatic fuzzy algorithms with considering some spatial constraints on the objective function. The algorithms incorporate spatial information into the membership function and the validity procedure for clustering. We use the intra-cluster distance measure, which is simply the median distance between a point and its cluster centre. The number of the cluster increases automatically according the value of intra-cluster, for example when a cluster is obtained, it uses this cluster to evaluate intra-cluster of the next cluster as input to the fuzzy method and so on, stop only when intra-cluster is smaller than a prescribe value. The most important aspect of the proposed algorithms is actually to work automatically. Alternative is to improve automatic image segmentation The proposed methods are evaluated and compared with the established methods by applying them on various test images, including synthetic images corrupted with noise of varying levels and simulated volumetric Magnetic Resonance Image (MRI) datasets.