DocumentCode :
1906463
Title :
Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation
Author :
Noor, Noorhayati Mohamed ; Khalid, Noor Elaiza Abdul ; Hassan, Rohaida ; Ibrahim, Shafaf ; Yassin, Ihsan Mohd
Author_Institution :
Fac. of Comput. & Math. Sci., Univ. Teknol. Mara, Shah Alam, Malaysia
fYear :
2010
fDate :
22-22 June 2010
Firstpage :
68
Lastpage :
70
Abstract :
This paper studies the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for segmentation of brain abnormality in MRI images. Segmentation of MRI image is an important part of brain imaging research. In this study, 150 MRI images were used as testing data for the system. The data was created by combining the shapes and size of various abnormalities and pasting it onto normal brain image. Several types of backgrounds were tested - low, medium and high grey levels. The experimental results show good segmentation for medium and low background levels value for both light and dark abnormality levels over different backgrounds.
Keywords :
biomedical MRI; brain; fuzzy neural nets; image segmentation; inference mechanisms; ANFIS; MRI image segmentation; abnormality levels; adaptive neuro-fuzzy inference system; brain abnormality segmentation; brain imaging research; Accuracy; Adaptive systems; Brain; Control systems; Image segmentation; Magnetic resonance imaging; Adaptive Neuro-Fuzzy Inference System (ANFIS); Brain Abnormality Segmentation; Magnetic Resonance Imaging (MRI);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and System Graduate Research Colloquium (ICSGRC). 2010 IEEE
Conference_Location :
Shah Alam
Print_ISBN :
978-1-4244-7238-3
Type :
conf
DOI :
10.1109/ICSGRC.2010.5562519
Filename :
5562519
Link To Document :
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