Pattern Recognition Letters
Expert Systems with Applications: An International Journal
A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI
Expert Systems with Applications: An International Journal
A new segmentation system for brain MR images based on fuzzy techniques
Applied Soft Computing
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
Fuzzy c-means approach to tissue classification in multimodal medical imaging
Information Sciences: an International Journal
Partially supervised clustering for image segmentation
Pattern Recognition
An investigation of mountain method clustering for large data sets
Pattern Recognition
Non-uniform self-selective coder for fuzzy rules and its application
Expert Systems with Applications: An International Journal
An automatic region-based image segmentation algorithm for remote sensing applications
Environmental Modelling & Software
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Computers in Biology and Medicine
Mathematical and Computer Modelling: An International Journal
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Artificial Intelligence in Medicine
A novel fuzzy Dempster-Shafer inference system for brain MRI segmentation
Information Sciences: an International Journal
A novel fuzzy clustering algorithm with between-cluster information for categorical data
Fuzzy Sets and Systems
Fuzzy rule-based segmentation of CT brain images of hemorrhage for compression
International Journal of Advanced Intelligence Paradigms
Fuzzy C-mean based brain MRI segmentation algorithms
Artificial Intelligence Review
Two novel fuzzy clustering methods for solving data clustering problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared