Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy Sets and Systems
Pattern Recognition Letters
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
Accelerating EM for Large Databases
Machine Learning
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Segmentation of 3D Brain Structures Using Level Sets and Dense Registration
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An Efficient Automatic Framework for Segmentation of MRI Brain Image
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
MRI Image Segmentation Using Unsupervised Clustering Techniques
ICCIMA '05 Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications
Gradual Model Generator for Single-Pass Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Scalable frameworks and algorithms for cluster ensembles and clustering data streams
Scalable frameworks and algorithms for cluster ensembles and clustering data streams
Extending fuzzy and probabilistic clustering to very large data sets
Computational Statistics & Data Analysis
Complexity reduction for "large image" processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reducing the time complexity of the fuzzy c-means algorithm
IEEE Transactions on Fuzzy Systems
Fast accurate fuzzy clustering through data reduction
IEEE Transactions on Fuzzy Systems
Optimization of clustering criteria by reformulation
IEEE Transactions on Fuzzy Systems
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Artificial Intelligence in Medicine
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Objective function-based clustering
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
Editor's Choice Article: Human activity recognition in videos using a single example
Image and Vision Computing
Fuzzy and crisp clustering methods based on the neighborhood concept: A comprehensive review
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS'2011: 2nd International Fuzzy Systems Symposium
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A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data.