Three-dimensional segmentation of brain tissues using Markov random fields and genetic algorithms
Proceedings of the 2007 ACM symposium on Applied computing
A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Fuzzy clustering to detect tuberculous meningitis-associated hyperdensity in CT images
Computers in Biology and Medicine
Masseter segmentation using an improved watershed algorithm with unsupervised classification
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Centroid Neural Network with Spatial Constraints
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Applied Soft Computing
Robust Motion Detection via the Fuzzy Fusion of 6D Feature Space Decompositions
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Video sequence motion tracking by fuzzification techniques
Applied Soft Computing
A modified-FCM segmentation algorithm for brain MR images
Proceedings of the 2009 International Conference on Hybrid Information Technology
Risk evaluation of bidding decision based on multi-factor clustering analysis
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A generalized spatial fuzzy C-means algorithm for medical image segmentation
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Engineering Applications of Artificial Intelligence
A survey on use of soft computing methods in medicine
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Fuzzy spatial growing for glioblastoma multiforme segmentation on brain magnetic resonance imaging
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III
Multi-seed segmentation of tomographic volumes based on fuzzy connectedness
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Proceedings of the 15th WSEAS international conference on Computers
Evaluation method for MRI brain tissue abnormalities segmentation study
Proceedings of the 15th WSEAS international conference on Computers
A method for MRI segmentation of brain tissue
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
A new method based on the CLM of the LV RNN for brain MR image segmentation
Digital Signal Processing
Fuzzy data mining: a literature survey and classification framework
International Journal of Networking and Virtual Organisations
Simplified labeling process for medical image segmentation
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Image segmentation of noisy digital images using extended fuzzy C-means clustering algorithm
International Journal of Computer Applications in Technology
A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation
Pattern Recognition Letters
Fuzzy C-mean based brain MRI segmentation algorithms
Artificial Intelligence Review
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Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. Unfortunately, MR images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies with segmentation. A robust segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels, is shown to improve the segmentation performance dramatically. The degree of attraction is optimized by a neural-network model. Simulated and real brain MR images with different noise levels are segmented to demonstrate the superiority of the proposed technique compared to other FCM-based methods. This segmentation method is a key component of an MR image-based classification system for brain tumors, currently being developed.