Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Inherent Wave Estimation on Ultrasonic Non-destructive Testing Using Fuzzy Inference
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
A Novel Algorithm for Automatic Brain Structure Segmentation from MRI
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Ultrasonography System Aided by Fuzzy Logic for Identifying Implant Position in Bone
IEICE - Transactions on Information and Systems
Rough representation of a region of interest in medical images
International Journal of Approximate Reasoning
Fuzzy thick rubber model for cerebral surface extraction in neonatal brain MR images
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI
Computers in Biology and Medicine
Automated segmentation of human brain MR images using a multi-agent approach
Artificial Intelligence in Medicine
A novel fuzzy Dempster-Shafer inference system for brain MRI segmentation
Information Sciences: an International Journal
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This paper proposes an automated procedure for segmenting an magnetic resonance (MR) image of a human brain based on fuzzy logic. An MR volumetric image composed of many slice images consists of several parts: gray matter, white matter, cerebrospinal fluid, and others. Generally, the histogram shapes of MR volumetric images are different from person to person. Fuzzy information granulation of the histograms can lead to a series of histogram peaks. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram obtained from the MR images. After these thresholds are evaluated by a procedure called region growing, the whole brain can be identified. A segmentation experiment was done on 50 human brain MR volumes. A statistical analysis showed that the automated segmented volumes were similar to the volumes manually segmented by a physician. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Fuzzy if-then rules can represent information on the anatomical locations, segmentation boundaries as well as intensities. Evaluation of the inferred result using the region growing method can then lead to the decomposition of the whole brain. We applied this method to 44 MR volumes. The decomposed portions were statistically compared with those manually decomposed by a physician. Consequently, our method can identify the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem with high accuracy and therefore can provide the three dimensional shapes of these regions.