Title :
Brain image segmentation using fuzzy classifiers
Author :
Zhu, Y. ; Chi, Z. ; Yan, H.
Author_Institution :
Dept. of Electr. Eng., Sydney Univ., NSW, Australia
fDate :
29 Nov-2 Dec 1994
Abstract :
A rule-based approach is proposed here for brain tissue segmentation in magnetic resonance images (MRI). By combining a thresholding method, which is fast and easy to implement, and fuzzy rules, which can deal with uncertain or ambiguous data, the proposed segmentation method outperforms the existing conventional methods. The results of the proposed method have been compared to that obtained with the well-known fuzzy c-means algorithm on a typical MRI brain dataset
Keywords :
biomedical NMR; brain; fuzzy logic; image segmentation; medical expert systems; medical image processing; uncertainty handling; MRI brain dataset; ambiguous data; brain image segmentation; brain tissue segmentation; fuzzy c-means algorithm; fuzzy classifiers; fuzzy rules; magnetic resonance images; rule-based approach; thresholding method; uncertain data; Australia; Brain; Degenerative diseases; Histograms; Humans; Image segmentation; Magnetic resonance imaging; Neoplasms; Protons; Surgery;
Conference_Titel :
Intelligent Information Systems,1994. Proceedings of the 1994 Second Australian and New Zealand Conference on
Conference_Location :
Brisbane, Qld.
Print_ISBN :
0-7803-2404-8
DOI :
10.1109/ANZIIS.1994.396912
