A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Centroid of a type-2 fuzzy set
Information Sciences: an International Journal
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A novel fuzzy compensation multi-class support vector machine
Applied Intelligence
Robust fuzzy clustering-based image segmentation
Applied Soft Computing
A robust segmentation method for the AFCM-MRF model in noisy image
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Review of brain MRI image segmentation methods
Artificial Intelligence Review
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
Effective fuzzy c-means based kernel function in segmenting medical images
Computers in Biology and Medicine
Type-2 fuzzy sets and systems: an overview
IEEE Computational Intelligence Magazine
IEEE Transactions on Information Technology in Biomedicine
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
IEEE Transactions on Fuzzy Systems
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to C-Means
IEEE Transactions on Fuzzy Systems
Hi-index | 0.10 |
The fuzzy C-means (FCM) algorithm has significant importance compared to other methods in Medical image segmentation. Conventional FCM algorithm is sensitive to noise especially in the presence of intensity inhomogeneity in MRI. Main reason is that a single fuzzifier in FCM cannot properly represent pattern memberships for all clusters. In this paper, we present a novel algorithm for fuzzy segmentation of MRI data. The algorithm utilizes two fuzzifiers used in interval type-2 FCM and a spatial constraint on the membership functions. Also, in our investigation, validity functions are extended to generalized form for interval type-2 fuzzy clustering. The experimental results on both synthetic and MR images show that the proposed algorithm has better performance on image segmentation than conventional FCM based algorithms.