Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Skull Stripping Problem in MRI Solved by a Single 3D Watershed Transform
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Computers in Biology and Medicine
Binarization in Magnetic Resonance Images (MRI) of human head scans with intensity inhomogeneity
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
MARGA: Multispectral Adaptive Region Growing Algorithm for brain extraction on axial MRI
Computer Methods and Programs in Biomedicine
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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 20MRI 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).