On the use of fuzzy sets in histogram equalization
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Digital Image Processing
Advanced algorithmic approaches to medical image segmentation: state-of-the-art application in cardiology, neurology, mammography and pathology
Fuzzy Mathematics in Medicine
Clustering validity checking methods: part II
ACM SIGMOD Record
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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This paper describes a way of medical image segmentation using an appropriately defined fuzzy clustering method based on a fuzzy similarity relation. The considered relation is defined in terms of the Minkowski metric. A fuzzy similarity relation based image segmentation algorithm is also introduced. Next, a method for formal comparison between different data clusterings, based on a new distance model is proposed. To illustrate the obtained segmentation process some examples of computed tomography imaging are considered. For comparison purpose and also to access the clustering quality, corresponding results with using the classical fuzzy c means algorithm are also presented.