Fuzzy Clustering With Spatial Information For Image Segmentation Using Kernel Metric

In this project the image segmentation using Fuzzy Cmeans algorithm and kernel metric. In FCM algorithm by introducing a trade-off weighted fuzzy factor and kernel metric. This factor depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. So we propose generalised rough set FCM algorithm in order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its objective function. This algorithm determines the kernel parameter by using all data points in the collection. So the segmentation accuracy is high. Furthermore, the trade-off weighted fuzzy factor and the kernel distance measure are both parameter free. The performance of the proposed method is compared with FCM results on synthetic and real images are more effective and efficient.