Comput. Biol. Med. - Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images.

Tópicos

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Resumo

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).

Resumo Limpo

paper propos two brain extract algorithm bea tweight magnet reson imag mri scan tweight imag first filter low pass filter lpf remov subdu background nois imag diffus enhanc brain boundari use ridler method threshold valu intens obtain use threshold valu rough binari brain imag obtain perform morpholog oper use largest connect compon lcc analysi brain mask obtain brain extract method use d inform slice name dbea concept lcc fail slice overcom problem d inform avail adjac slice use result dbea experiment result mri data set show propos dbea gave excel result perform dbea better dbea popular method brain extract tool bet brain surfac extractor bse

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