An Automated Approach for Segmentation of Brain MR Images using Gaussian Mixture Model based Hidden Markov Random Field with Expectation Maximization
DOI:
https://doi.org/10.14738/jbemi.24.1411Keywords:
GMM, HMRF, EM, Segmentation, brain tumourAbstract
Manual segmentation of brain tissues from MR images for diagnosis purpose is time consuming and requires much effort even by experts. This has motivated generation of automated segmentation techniques. Moreover, due to presence of noise in an image and its low contrast, it is difficult to correctly delineate tumour from brain MR images. In this paper, a novel hybrid method using Gaussian Mixture Model based Hidden Markov Random Field (HMRF) with Expectation Maximization (EM) has been proposed which segments tissues from MR brain images efficiently and helps to separate out tumour area easily. The proposed method minimizes energy function during each iteration of EM and gives comparable results with ground truth. The results obtained are also compared with the results of fuzzy c-means clustering algorithm for image segmentation.
References
(1) Yousefi Sahar, Reza Azmi, and Morteza Zahedi. "Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms.", Elsevier, Medical image analysis 16, no. 4 (2012): 840-848.
(2) Gordillo Nelly, Eduard Montseny and Pilar Sobrevilla. "State of the art survey on MRI brain tumor segmentation." Elsevier, Magnetic resonance imaging 31.8 (2013): 1426-1438.
(3) Balafar, M. A. "Gaussian mixture model based segmentation methods for brain MRI images." Springer, Artificial Intelligence Review 41, no. 3 (2014): 429-439.
(4) Caldairou Benoít, Nicolas Passat, Piotr A. Habas, Colin Studholme and François Rousseau. "A non-local fuzzy segmentation method: application to brain MRI.” , Elsevier, Pattern Recognition 44, no. 9 (2011): 1916-1927.
(5) Khotanlou Hassan, Olivier Colliot, Jamal Atif and Isabelle Bloch. "3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models." Fuzzy Sets and Systems 160, no. 10 (2009): 1457-1473.
(6) Lladó Xavier, Arnau Oliver, Mariano Cabezas, Jordi Freixenet, Joan C. Vilanova, Ana Quiles, Laia Valls, Lluís Ramió-Torrentà and Àlex Rovira. "Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches.", Elsevier, Information Sciences 186, no. 1 (2012): 164-185.
(7) Jason J. Corso, , Eitan Sharon, Shishir Dube, Suzie El-Saden, Usha Sinha and Alan Yuille, "Efficient multilevel brain tumor segmentation with integrated bayesian model classification." Medical Imaging, IEEE Transactions on 27.5 (2008): 629-640.
(8) Mohamed N. Ahmed, Sameh M. Yamany, Nevin Mohamed, Aly A. Farag and Thomas Moriarty, "A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data." Medical Imaging, IEEE Transactions on 21.3 (2002): 193-199.
(9) Wu T, Bae MH, Zhang M, Pan R and Badea A, "A prior feature SVM-MRF based method for mouse brain segmentation." Elsevier, NeuroImage 59.3 (2012): 2298-2306.
(10) Balafar, M. A., Ramli A. R., Saripan M. I., & Mashohor S., "Review of brain MRI image segmentation methods."Artificial Intelligence Review 33.3 (2010): 261-274.
(11) Wang, Quan. "GMM-Based Hidden Markov Random Field for Color Image and 3D Volume Segmentation." arXiv preprint arXiv:1212.4527 (2012).
(12) Reynolds, Douglas A., Thomas F. Quatieri and Robert B. Dunn. "Speaker verification using adapted Gaussian mixture models." Digital signal processing10.1 (2000): 19-41.
(13) Zhang Yongyue, Michael Brady and Stephen Smith. "Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm." Medical Imaging, IEEE Transactions on 20.1 (2001): 45-57.
(14) MRI Brain Datasets available on BRATS 2012 - http://www2.imm.dtu.dk/projects/BRATS2012/
(15) Greenspan Hayit, Amit Ruf and Jacob Goldberger. "Constrained
Gaussian mixture model framework for automatic segmentation of MR brain images."Medical Imaging, IEEE Transactions on 25.9 (2006): 1233-1245.
(16) Mahapatra Dwarikanath and Ying Sun, "Integrating segmentation information for improved MRF-based elastic image registration." Image Processing, IEEE Transactions on 21.1 (2012): 170-183.
