Generalized Spatial Kernel Based Fuzzy C-Means Clustering Algorithm for Image Segmentation

Image segmentation plays an important role in image analysis. It is one of the first and most important tasks in image analysis and computer vision. This proposed system presents a variation of fuzzy c- means algorithm that provides image clustering. Based on the Mercer kernel, the kernel fuzzy c-means clustering algorithm (KFCM) is derived from the fuzzy c-means clustering algorithm (FCM). The KFCM algorithm that provides image clustering and improves accuracy significantly compared with classical fuzzy C-Means algorithms. This proposed system makes the use of the advantages of KFCM and also incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called Generalized Spatial Kernel based Fuzzy C-Means (GSKFCM) algorithm. The major characteristic of GSKFCM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation as well as it is parameter independent. The purpose of designing this system is to produce better segmentation results for images corrupted by noise, so that it can be useful in various fields like medical image analysis, such as tumor detection, study of anatomical structure, and treatment planning.