Pattern Recognition Letters
Possibilistic approach to kernel-based fuzzy c-means clustering with entropy regularization
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Robust kernel fuzzy clustering
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
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
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
An improved FCM algorithm for image segmentation
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
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
In search of optimal centroids on data clustering using a binary search algorithm
Pattern Recognition Letters
Local gaussian distribution fitting based FCM algorithm for brain MR image segmentation
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Lessons to learn from a mistaken optimization
Pattern Recognition Letters
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A fast spatially constrained kernel clustering algorithm is proposed for segmenting medical magnetic resonance imaging (MRI) brain images and correcting intensity inhomogeneities known as bias field in MRI data. The algorithm using kernel technique implicitly maps image data to a higher dimensional kernel space in order to improve the separability of data and provide more potential for effectively segmenting MRI data. Based on the technique, a speed-up scheme for kernel clustering and an approach for correcting spurious intensity variation of MRI images have been implemented. The fast kernel clustering and bias field correcting benefit each other in an iterative matter and have dramatically reduced the time complexity of kernel clustering. The experiments on simulated brain phantoms and real clinical MRI data have shown that the proposed algorithm generally outperforms the corresponding traditional algorithms when segmenting MRI data corrupted by high noise and gray bias field.