DocumentCode :
1710573
Title :
A modified Fuzzy C-means algorithm with symmetry information for MR brain image segmentation
Author :
Jayasuriya, Surani Anuradha ; Liew, Alan Wee-Chung
Author_Institution :
Sch. of Inf. & Commun. Technol., Griffith Univ., Gold Coast, QLD, Australia
fYear :
2013
Firstpage :
1
Lastpage :
5
Abstract :
In this paper, we present a novel modified Fuzzy C-means algorithm with symmetry information to reduce the effect of noise in brain tissue segmentation in magnetic resonance image (MRI). We integrate brain´s bilateral symmetry into the conventional Fuzzy C-means (FCM) as an additional term. In experiments, some synthetic images, and both simulated and real brain images were used to investigate the robustness of the method against noise. Finally, the method was compared with the conventional FCM algorithm. Results show the viability of the approach and the preliminary investigation appears promising.
Keywords :
biological tissues; biomedical MRI; brain; fuzzy set theory; image denoising; image segmentation; medical image processing; pattern clustering; FCM algorithm; MR brain image segmentation; brain bilateral symmetry; brain tissue segmentation; magnetic resonance image; modified fuzzy c-means algorithm; noise effect reduction; symmetry information; synthetic images; Brain; Clustering algorithms; Image segmentation; Linear programming; Magnetic resonance imaging; Noise; Standards; Brain image segmentation; Brain symmetry; Fuzzy c-means; MRI; Mid-sagittal plane;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on
Conference_Location :
Tainan
Print_ISBN :
978-1-4799-0433-4
Type :
conf
DOI :
10.1109/ICICS.2013.6782786
Filename :
6782786
Link To Document :
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