Segmentation of rat brain MR images using a hybrid fuzzy system

We have developed a magnetic resonance (MR) image segmentation system which consists of a fuzzy ruled-based system and a fuzzy c-means algorithm (FCM). The first stage of the system is the fuzzy ruled-based system which classifies most pixels of MR images into several known classes and one "unc...

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Bibliographic Details
Published inNAFIPS/IFIS/NASA '94. Proceedings of the First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intellige pp. 55 - 59
Main Authors Chih-Wei Chang, Hillman, G.R., Hao Ying, Kent, T.A., Yen, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1994
Subjects
Biomedical imaging
Computer science
Fuzzy logic
Fuzzy set theory
Fuzzy systems
Image segmentation
Knowledge based systems
Lesions
Magnetic resonance
Pixel
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Summary:We have developed a magnetic resonance (MR) image segmentation system which consists of a fuzzy ruled-based system and a fuzzy c-means algorithm (FCM). The first stage of the system is the fuzzy ruled-based system which classifies most pixels of MR images into several known classes and one "unclassified" class. In the second stage, the classified result of the first stage is used to find the initial prototypes for FCM and the "unclassified" pixels are classified by FCM. The result of this combination is a very robust classification system. Rat brain MR images with stroke lesions are segmented. This system successfully identified the penumbra area of the rat brain.< >
ISBN:9780780321250
0780321251
DOI:10.1109/IJCF.1994.375151