A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence of an EM-type algorithm for spatial clustering
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Robust fuzzy clustering-based image segmentation
Applied Soft Computing
Novel modified fuzzy c-means algorithm with applications
Digital Signal Processing
IEEE Transactions on Information Technology in Biomedicine
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Scale space classification using area morphology
IEEE Transactions on Image Processing
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Fuzzy C-Means algorithm fails to segment the noisy image properly. In this paper, we present an algorithm called Extended Fuzzy C means EFCM, which pre-processes the image to reduce the noise effect and then apply FCM algorithm for image segmentation. Pre-processing of image is influenced by the direct eight neighbourhood pixels of every pixel of an image under consideration. Proposed algorithm has least execution time and it yields regions more homogeneous than those of other techniques. It removes noisy spots and is less sensitive to noise. The proposed technique is a powerful method for noisy image segmentation compared to other image segmentation techniques.