Feature-based fuzzy classification for interpretation of mammograms
Fuzzy Sets and Systems
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
Fuzzy c-Means Classifier for Incomplete Data Sets with Outliers and Missing Values
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
A robust deterministic annealing algorithm for data clustering
Data & Knowledge Engineering
Investigating diversity of clustering methods: An empirical comparison
Data & Knowledge Engineering
On some entropy functionals derived from Rényi information divergence
Information Sciences: an International Journal
A new segmentation system for brain MR images based on fuzzy techniques
Applied Soft Computing
Extending fuzzy and probabilistic clustering to very large data sets
Computational Statistics & Data Analysis
Density-weighted fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
Complexity reduction for "large image" processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms
IEEE Transactions on Fuzzy Systems
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Artificial Intelligence in Medicine
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods
Computer Methods and Programs in Biomedicine
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This paper presents a robust fuzzy c-means (FCM) for an automatic effective segmentation of breast and brain magnetic resonance images (MRI). This paper obtains novel objective functions for proposed robust fuzzy c-means by replacing original Euclidean distance with properties of kernel function on feature space and using Tsallis entropy. By minimizing the proposed effective objective functions, this paper gets membership partition matrices and equations for successive prototypes. In order to reduce the computational complexity and running time, center initialization algorithm is introduced for initializing the initial cluster center. The initial experimental works have done on synthetic image and benchmark dataset to investigate the effectiveness of proposed, and then the proposed method has been implemented to differentiate the different region of real breast and brain magnetic resonance images. In order to identify the validity of proposed fuzzy c-means methods, segmentation accuracy is computed by using silhouette method. The experimental results show that the proposed method is more capable in segmentation of medical images than existed methods.