Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
Brain State Recognition Using Fuzzy C-Means (FCM) Clustering with Near Infrared Spectroscopy (NIRS)
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Using Fuzzy Information in Knowledge Guided Segmentation of Brain Tumors
IJCAI '95 Selected papers from the Workshop on Fuzzy Logic in Artificial Intelligence, Towards Intelligent Systems
IEEE Transactions on Neural Networks
Video sequence motion tracking by fuzzification techniques
Applied Soft Computing
Effective fuzzy c-means based kernel function in segmenting medical images
Computers in Biology and Medicine
Pattern Recognition Letters
Novel segmentation algorithm in segmenting medical images
Journal of Systems and Software
Modified fuzzy c-means algorithm for segmentation of T1-T2-weighted brain MRI
Journal of Computational and Applied Mathematics
Robust kernel FCM in segmentation of breast medical images
Expert Systems with Applications: An International Journal
Modified bias field fuzzy C-means for effective segmentation of brain MRI
Transactions on computational science VIII
Modified bias field fuzzy C-means for effective segmentation of brain MRI
Transactions on computational science VIII
Improved Fuzzy Clustering Algorithms in Segmentation of DC-enhanced breast MRI
Journal of Medical Systems
Strong fuzzy c-means in medical image data analysis
Journal of Systems and Software
Fuzzy spectral clustering with robust spatial information for image segmentation
Applied Soft Computing
Effective FCM noise clustering algorithms in medical images
Computers in Biology and Medicine
An efficient neural network based method for medical image segmentation
Computers in Biology and Medicine
Hi-index | 0.00 |
This work concerns a new method called fuzzy membership C-means (FMCMs) for segmentation of magnetic resonance images (MRI), and an efficient program implementation of it to the segmentation of MRI. Classical unsupervised clustering methods including the FCM by Bezdek, suffer many problems that can be partially treated with a proper rule to construct the initial membership matrix to clusters. This work develops a specific method to construct the initial membership matrix to clusters in order to improve the strength of the clusters. The new FMCM is tested on a set of benchmarks and then the application to the segmentation of MR images is presented and compared with the results obtained using FCM.