Title :
Segmentation of rat brain MR images using a hybrid fuzzy system
Author :
Chang, Chih-Wei ; Hillman, Gilbert R. ; Ying, Hao ; Kent, Thomas A. ; Yen, John
Author_Institution :
Dept. of Comput. Sci., Texas A&M Univ., College Station, TX, USA
Abstract :
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
Keywords :
biomedical NMR; brain; fuzzy logic; image segmentation; knowledge based systems; fuzzy c-means algorithm; fuzzy ruled-based system; hybrid fuzzy system; image segmentation; rat brain MR images; robust classification; stroke lesions; Biomedical imaging; Computer science; Fuzzy logic; Fuzzy set theory; Fuzzy systems; Image segmentation; Knowledge based systems; Lesions; Magnetic resonance; Pixel;
Conference_Titel :
Fuzzy Information Processing Society Biannual Conference, 1994. Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic,
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-2125-1
DOI :
10.1109/IJCF.1994.375151
