Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images.
In this paper we propose two brain extraction algorithms (BEA) for T2-weighted magnetic resonance imaging (MRI) scans. The T2-weighted image is first filtered with a low pass filter (LPF) to remove or subdue the background noise. Then the image is diffused to enhance the brain boundaries. Using Ridler's method a threshold value for intensity is obtained. Using the threshold value a rough binary brain image is obtained. By performing morphological operations and using the largest connected component (LCC) analysis, a brain mask is obtained from which the brain is extracted. This method uses only 2D information of slices and is named as 2D-BEA. The concept of LCC failed in few slices. To overcome this problem, 3D information available in adjacent slices is used which resulted in 3D-BEA. Experimental results on 20 MRI data sets show that the proposed 3D-BEA gave excellent results. The performance of this 3D-BEA is better than 2D-BEA and other popular methods, brain extraction tool (BET) and brain surface extractor (BSE).
Somasundaram
K
K
Department of Computer Science and Applications, Gandhigram Rural Institute, Gandhigram, Tamilnadu 624302, India. somasundaramk@yahoo.com
Kalaiselvi
T
T
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D016208:Databases, Factual; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged