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).