An Automatic Method to Extract Brain Tissue in CT Data

In this paper, an automatic method is proposed to extract brain tissue in CT brain. Firstly, we use the adaptive threshold algorithm to compute the range of gray value of the brain tissue. Secondly, we use the range of the thresholds to estimate the position of seed point which is defined as the center of gravity of the brain. Finally, we propose a 3D region growing algorithm based on the pre-judgment to extract the brain tissue. The experiments show that the proposed method can extract the brain tissue in CT data automatically and efficiently.

[1]  Jiang Shao-feng Automatic Extraction of Brain from Cerebral MR Image Based on Improved BET Algorithm , 2009 .

[2]  Piotr J. Slomka,et al.  Automatic registration of misaligned CT attenuation correction maps in Rb-82 PET/CT improves detection of angiographically significant coronary artery disease , 2015, Journal of Nuclear Cardiology.

[3]  D. Rueckert,et al.  Medical Image Registration , 2010 .

[4]  Marco Das,et al.  Global left ventricular function in cardiac CT. Evaluation of an automated 3D region-growing segmentation algorithm , 2006, European Radiology.

[5]  Shuang Ma,et al.  A novel method for automated segmentation of airway tree , 2012, 2012 24th Chinese Control and Decision Conference (CCDC).

[6]  Max A. Viergever,et al.  Automatic registration of CT and MR brain images using correlation of geometrical features , 1995, IEEE Trans. Medical Imaging.

[7]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[8]  Li Da,et al.  A Brain CT Image Distrill Algorithm Based on Matlab , 2009 .

[9]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Alexander Fischer,et al.  Evaluation of elastix-based propagated align algorithm for VOI- and voxel-based analysis of longitudinal 18F-FDG PET/CT data from patients with non-small cell lung cancer (NSCLC) , 2015, EJNMMI Research.

[11]  R. Sukanesh A Padma,et al.  Automatic Classification and Segmentation of Brain Tumor in CT Images using Optimal Dominant Gray level Run length Texture Features , 2011 .

[12]  C. Chellamuthu,et al.  Brain tumour segmentation from MRI image using genetic algorithm with fuzzy initialisation and seeded modified region growing (GFSMRG) method , 2016 .

[13]  Alex Rovira,et al.  MARGA: Multispectral Adaptive Region Growing Algorithm for brain extraction on axial MRI , 2014, Comput. Methods Programs Biomed..

[14]  Françoise Peyrin,et al.  Automated 3D region growing algorithm based on an assessment function , 2002, Pattern Recognit. Lett..