Gray Matter Segmentation of Brain MRI Using Hybrid Enhanced Independent Component Analysis in Noisy and Noise Free Environment

Abstract:

Medical segmentation is the primary task performed to diagnosis the abnormalities in the human body. The brain is the complex organ and anatomical segmentation of brain tissues is a challenging task. In this paper, we used Enhanced Independent component analysis to perform the segmentation of gray matter. We used modified K means, Expected Maximization and Hidden Markov random field to provide better spatial correlation that overcomes in-homogeneity, noise and low contrast. Our objective is achieved in two steps (i) initially unwanted tissues are clipped from the MRI image using skull stripped Algorithm (ii) Enhanced Independent Component analysis is used to perform the segmentation of gray matter. We apply the proposed method on both T1w and T2w MRI to perform segmentation of gray matter at different noisy environments. We evaluate the the performance of our proposed system with Jaccard Index, Dice Coefficient and Accuracy. We further compared the proposed system performance with the existing frameworks. Our proposed method gives better segmentation of gray matter useful for diagnosis neurodegenerative disorders.

• Periodical: Journal of Biomimetics, Biomaterials and Biomedical Engineering (Volume 47)
• Pages: 75-103
• DOI: https://doi.org/10.4028/www.scientific.net/JBBBE.47.75
• Online since: November 2020
• Authors: Shaik Basheera*, M. Satya Sai Ram
• Keywords: Gaussian Mixture Model, Gibbs Density Function, Image Segmentation, K-Means, MRI, Skull Stripping

References

[1] T Liu, G. Young, NK Chen, L. Huang, STC Wong, 76-space analysis of gray matter diffusivity: methods and application,, Neuroimage. 2006 May 15;31(1):51-65.

DOI: 10.1016/j.neuroimage.2005.11.041

[2] A. Kharrat, N. Benamrane, M. B. Messaoud and M. Abid, Detection of brain tumor in medical images,, 2009 3rd International Conference on Signals, Circuits and Systems (SCS), Medenine, 2009, pp.1-6,.

DOI: 10.1109/icscs.2009.5412577

[3] Iraky khalifa, Aliaa Youssif, Howida Youssry, Tissue segmentation Techniques of brain MR Images: A Review", International Conference on Intelligent Computational Systems (ICICS,2012) Jan. 7-8, 2012 Dubai.

[4] Hayit Greenspan, Amit Ruf and Jacob Goldberger, Constrained Gaussian Mixture Model Framework for Automatic Segmentation of MR Brain Images,, IEEE Trans. on Medical Imaging, Vol. 25, no. 9, pp.1233-1245, 2006.

DOI: 10.1109/tmi.2006.880668

[5] Mortazavi D, Kouzani AZ, Soltanian-Zadeh H, Segmentation of multiple sclerosis lesions in MR images: a review,, Neuroradiology. 2012; 54(4): 299‐320.

DOI: 10.1007/s00234-011-0886-7

[6] Park JG, Lee C, Skull stripping based on region growing for magnetic resonance brain images,, Neuroimage, 2009; 47(4): 1394‐1407.

DOI: 10.1016/j.neuroimage.2009.04.047

[7] N. F. Ishak, R. Logeswaran, and W.-H. Tan, Artifact and noise stripping on low-field brain mri,, Int. J. Biology Biomed. Eng, vol. 2, no. 2, pp.59-68, (2008).

[8] Shan Shen, W. Sandham, M. Granat and A. Sterr, MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization,, in IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 3, pp.459-467, Sept. 2005,.

DOI: 10.1109/titb.2005.847500

[9] E.A. Zanaty & Ashraf Afifi, A watershed approach for improving medical image segmentation, Computer Methods in Biomechanics and Biomedical Engineering,, 16:12, 1262-1272, 2013,.

DOI: 10.1080/10255842.2012.666794

[10] L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger and J. C. Bezdek, A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain,, in IEEE Transactions on Neural Networks, vol. 3, no. 5, pp.672-682, Sept. 1992.

DOI: 10.1109/72.159057

[11] Zanaty, E. A., An Approach Based on Fusion Concepts for Improving Brain Magnetic Resonance Images (MRIs) Segmentation,, Journal of Medical Imaging and Health Informatics, Volume 3, Number 1, March 2013, pp.30-37(8), https://doi.org/10.1166/jmihi.2013.1122.

DOI: 10.1166/jmihi.2013.1122

[12] Ayush Goyal, Sunayana Tirumalasetty, Gahangir Hossain ,Rajab Challoo, Manish Arya, Rajeev Agrawal, and Deepak Agrawal, Development of a Stand-Alone Independent Graphical User Interface for Neurological Disease Prediction with Automated Extraction and Segmentation of Gray and White Matter in Brain MRI Images,, Journal of Healthcare Engineering, Volume 2019 |Article ID 9610212 |21 pages| https://doi.org/10.1155/2019/ 9610212.

DOI: 10.1155/2019/9610212

[13] A. Lundervold and G. Storvik, Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images,, in IEEE Transactions on Medical Imaging, vol. 14, no. 2, pp.339-349, June 1995.

DOI: 10.1109/42.387715

[14] W. M. Wells, W. E. L. Grimson, R. Kikinis and F. A. Jolesz, Adaptive segmentation of MRI data,, in IEEE Transactions on Medical Imaging, vol. 15, no. 4, pp.429-442, Aug. 1996.

DOI: 10.1109/42.511747

[15] Xiaohong Li, S. Bhide and M. R. Kabuka, Labeling of MR brain images using Boolean neural network,, in IEEE Transactions on Medical Imaging, vol. 15, no. 5, pp.628-638, Oct. 1996.

DOI: 10.1109/42.538940

[16] Lakshmi, G. Geethu and A. Suruliandi. Anatomical structure segmentation in MRI brain images., 2011 International Conference on Emerging Trends in Electrical and Computer Technology (2011): 786-791.

DOI: 10.1109/icetect.2011.5760225

[17] H. Chen et al., A Supervised Hybrid Classifier for Brain Tissues and White Matter Lesions on Multispectral MRI,, 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC), Exeter, 2017, pp.375-379,.

DOI: 10.1109/ispan-fcst-iscc.2017.54

[18] H. Chen et al., A Supervised Hybrid Classifier for Brain Tissues and White Matter Lesions on Multispectral MRI,, 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC), Exeter, 2017, pp.375-379,.

DOI: 10.1109/ispan-fcst-iscc.2017.54

[19] R. Fang, Y. J. Chen, R. Zabih and T. Chen, Tree-metrics graph cuts for brain MRI segmentation with tree cutting,, 2010 Western New York Image Processing Workshop, Rochester, NY, 2010, pp.10-13,.

DOI: 10.1109/wnyipw.2010.5649772

[20] P. Kalavathi, Brain tissue segmentation in MR brain images using multiple Otsu's thresholding technique,, 2013 8th International Conference on Computer Science & Education, Colombo, 2013, pp.639-642,.

DOI: 10.1109/iccse.2013.6553987

[21] T. M. Nguyen, Q.M.J. Wu, D. Mukherjee and H. Zhang, A Bayesian Bounded Asymmetric Mixture Model with Segmentation Application,, in IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 1, pp.109-119, Jan. 2014.

DOI: 10.1109/jbhi.2013.2264749

[22] Z. Ji, Y. Xia, Q. Sun, Q. Chen, D. Xia and D. D. Feng, Fuzzy Local Gaussian Mixture Model for Brain MR Image Segmentation,, in IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp.339-347, May 2012.

DOI: 10.1109/titb.2012.2185852

[23] Meena Prakash R.,1 Shantha Selva Kumari R.2, Fuzzy C Means Integrated With Spatial Information and Contrast Enhancement for Segmentation of MR Brain Images,, C 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 116–123, 2016; Published online in Wiley Online Library (wileyonlinelibrary.com).

DOI: 10.1002/ima.22166

[24] Y. Song, Z. Ji and Q. Sun, An extension Gaussian mixture model for brain MRI segmentation,, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, 2014, pp.4711-4714,.

DOI: 10.1109/embc.2014.6944676

[25] Saritha Saladi, Amutha Prabha N, MRI brain segmentation in combination of clustering methods with Markov random field,, Int J Imaging Syst Technol, 2018;1–10,.

DOI: 10.1002/ima.22271

[26] A. Demirhan, M. Törü and İ. Güler, Segmentation of Tumor and Edema Along With Healthy Tissues of Brain Using Wavelets and Neural Networks,, in IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 4, pp.1451-1458, July 2015.

DOI: 10.1109/jbhi.2014.2360515

[27] P. Moeskops, M. A. Viergever, A. M. Mendrik, L. S. de Vries, M. J. N. L. Benders and I. Išgum, Automatic Segmentation of MR Brain Images With a Convolutional Neural Network,, in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp.1252-1261, May 2016.

DOI: 10.1109/tmi.2016.2548501

[28] J. Zhang, Y. Gao, Y. Gao, B. C. Munsell and D. Shen, Detecting Anatomical Landmarks for Fast Alzheimer's Disease Diagnosis,, in IEEE Transactions on Medical Imaging, vol. 35, no. 12, pp.2524-2533, Dec. 2016.

DOI: 10.1109/tmi.2016.2582386

[29] M. Dadar et al., Validation of a Regression Technique for Segmentation of White Matter Hyperintensities in Alzheimer's Disease,, in IEEE Transactions on Medical Imaging, vol. 36, no. 8, pp.1758-1768, Aug. 2017.

DOI: 10.1109/tmi.2017.2693978

[30] Jaccard P, The distribution of the flora in the alpine zone,, New Phytologist. 1912;11(2):37–50.

DOI: 10.1111/j.1469-8137.1912.tb05611.x

[31] Zou KH, Warfield SK, Baharatha A, Tempany C, Kaus MR, Haker SJ, et al. Statistical validation of image segmentation quality based on a spatial overlap index,, Academic Radiology. 2004; 11:178–89.

DOI: 10.1016/s1076-6332(03)00671-8

[32] Meila, Marina, Comparing Clusterings by the Variation of Information,, Learning Theory and Kernel Machines: 2003, 173–187.

[33] Monika Xess, et al. Analysis of Image Segmentation Methods Based on Performance Evaluation Parameters,. International Journal of Computational Engineering Research,Vol, 04, No.3. (2014).