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Fully Automatic Method for 3D T1-Weighted Brain Magnetic Resonance Images Segmentation
Bouchaib Cherradi, Omar Bouattane, Mohamed Youssfi, Abdelhadi Raihani
Pages - 220 - 235     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 5   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
Magnetic Resonance Imaging, MRI Segmentation, Brain Extraction, Intensity Inhomogeneity Correction
In the domain of medical imaging, accurate segmentation of brain MR images is of interest for many brain disorders. However, due to several factors such noise, imaging artefacts, intrinsic tissue variation and partial volume effects, tissue segmentation remains a challenging task. So, in this paper, a full automatic method for segmentation of brain MR images is presented. The method consists of four steps segmentation procedure. First, noise removing by median filtering is done; second segmentation of brain/non-brain tissue is performed by using a Threshold Morphologic Brain Extraction method (TMBE). Then initial centroids estimation by gray level histogram analysis based is executed. Finally, Fuzzy C-means Algorithm is used for MRI tissue segmentation. The efficiency of the proposed method is demonstrated by extensive segmentation experiments using simulated and real MR images.
CITED BY (5)  
1 Sara, s., ahmed, h., yassine, s. t., achraf, b., & bouchaib, c. (2015). new brain extraction method using expectation maximization and mathematical morphology. Journal of Theoretical & Applied Information Technology, 73(3).
2 Parida, S., & Dehuri, S. (2014). Review of fMRI Data Analysis: A Special Focus on Classification. International Journal of E-Health and Medical Communications (IJEHMC), 5(2), 1-26.
3 Sheejakumari, V., & Sankara Gomathi, B. (2015). MRI brain images healthy and pathological tissues classification with the aid of improved particle swarm optimization and neural network. Computational and mathematical methods in medicine, 2015.
4 Sara, S., Samir, B., Ahmed, H., & Bouchaib, C. (2014, November). A robust comparative study of five brain extraction algorithms (BET; BSE; McStrip; SPM2; TMBE). In Complex Systems (WCCS), 2014 Second World Conference on (pp. 632-636). IEEE.
5 Sheejakumari, V., & Gomathi, A. (2012). Healthy and pathological tissues classification in MRI brain images using hybrid genetic algorithm-neural network (HGANN) approach. European Journal of Scientific Research, 87(2), 212-226.
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Dr. Bouchaib Cherradi
FST - Morocco
Mr. Omar Bouattane
- Morocco
Mr. Mohamed Youssfi
- Morocco
Mr. Abdelhadi Raihani
- Morocco