An Efficient Computational Approach for the Detection of MR Brain Tissues in the Presence of Noise and Intensity Inhomogeneity

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Abstract:

The automatic detection of brain tissues such as White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) from the MR images of the brain using segmentation is of immense interest for the early detection and diagnosing various brain-related diseases. MR imaging technology is one of the best and most reliable ways of studying the brain. Segmentation of MR images is a challenging task due to various artifacts such as noise, intensity inhomogeneity, partial volume effects and elemental texture of the image. This work proposes a region based, efficient and modern energy minimization process called as Anisotropic Multiplicative Intrinsic Component Optimization (AMICO) for the brain image segmentation in the presence of noise and intensity inhomogeneity to separate different tissues. This algorithm uses an efficient Anisotropic diffusion filter to decrease the noise. The denoised image gets segmented after the correction of intensity inhomogeneity by the MICO algorithm. The algorithm decomposes the MR brain image as two multiplicative intrinsic components, called as the component of the true image which represents the physical properties of the brain tissue and the component of bias field that is related to intensity inhomogeneity. By optimizing the values of these two components using an efficient energy minimization technique, correction of intensity inhomogeneity and segmentation of the tissues can be achieved simultaneously. Performance evaluation and the comparison with some existing methods have validated the remarkable performance of AMICO in terms of efficiency of segmentation of brain images in the presence of noise and intensity inhomogeneity.

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65-79

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July 2017

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[1] Y. Zhou, W. -R. Shi, W. Chen, Y. -l. Chen, Y. Li, L. -W. Tan, D. -Q. Chen, Active contours driven by localizing region and edge-based intensity fitting energy with application to segmentation of the left ventricle in cardiac CT images, Neurocomputing 156 (2015).

DOI: 10.1016/j.neucom.2014.12.061

Google Scholar

[2] N. Sharma and L. M. Aggarwal, Automated medical image segmentation techniques, Journal ofMedical Physics, vol. 35, no. 1, p.3–14, (2010).

Google Scholar

[3] C. Feng, D. Zhao, M. Huang, Segmentation of longitudinal brain MR images using bias correction embedded fuzzy c-means with non-locally spatio-temporal regularization, Journal of Visual Communication and Image Representation 38 (2016) 517–529.

DOI: 10.1016/j.jvcir.2016.03.027

Google Scholar

[4] C. Feng, D. Zhao, M. Huang, Segmentation of Ischemic Stroke Lesions in Multi-spectral MR Images Using Weighting Suppressed FCM and Three Phase Level Set, in: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, 2015, p.233.

DOI: 10.1007/978-3-319-30858-6_20

Google Scholar

[5] Y. Xia, Z. Ji, Y. Zhang, Brain MRI image segmentation based on learning local variational Gaussian mixture models, Neurocomputing 204 (2016) 189–197.

DOI: 10.1016/j.neucom.2015.08.125

Google Scholar

[6] Z. Ji, Y. Xia, Q. Sun, Q. Chen, D. Feng, Adaptive scale fuzzy local gaussian mixture model for brain mr image segmentation, Neurocomputing 134 (2014) 60–69.

DOI: 10.1016/j.neucom.2012.12.067

Google Scholar

[7] X. Chen, Y. Zhu, F. Li, Z. -Y. Zheng, E. C. Chang, J. Ma, Accurate segmentation of touching cells in multi-channel microscopy images with geodesic distance based clustering, Neurocomputing 149 (2015) 39–47.

DOI: 10.1016/j.neucom.2014.01.061

Google Scholar

[8] F. Zhao, Fuzzy clustering algorithms with self-tuning non-local spatial information 628 for image segmentation, Neurocomputing 106 (2013) 115–125.

DOI: 10.1016/j.neucom.2012.10.022

Google Scholar

[9] B. Ayerdi, I. Marqu´es, M. Gran˜a, Spatially regularized semisupervised ensembles of extreme learning machines for hyperspectral image segmentation, Neurocomputing 149 (2015) 373–386.

DOI: 10.1016/j.neucom.2014.01.068

Google Scholar

[10] H. Chang, Z. Chen, Q. Huang, J. Shi, X. Li, Graph-based learning for segmentation of 3D ultrasound images, Neurocomputing 151 (2015) 632–644.

DOI: 10.1016/j.neucom.2014.05.092

Google Scholar

[11] S. Dai, K. Lu, J. Dong, Y. Zhang, Y. Chen, A novel approach of lung segmentation on chest CT images using graph cuts, Neurocomputing 168 (2015) 799–807.

DOI: 10.1016/j.neucom.2015.05.044

Google Scholar

[12] X. Wang, J. Shan, Y. Niu, L. Tan, S. -X. Zhang, Enhanced distance regularization for re-initialization free level set evolution with application to image segmentation, Neurocomputing 141 (2014) 223–235.

DOI: 10.1016/j.neucom.2014.03.011

Google Scholar

[13] S. Zhou, J. Wang, S. Zhang, Y. Liang, Y. Gong, Active contour model based on local and global intensity information for medical image segmentation, Neurocomputing 186 (2016) 107–118.

DOI: 10.1016/j.neucom.2015.12.073

Google Scholar

[14] X. -F. Wang, H. Min, Y. -G. Zhang, Multi-scale local region based level set method for image segmentation in the presence of intensity inhomogeneity, Neurocomputing 151 (2015) 1086–1098.

DOI: 10.1016/j.neucom.2014.01.079

Google Scholar

[15] M. A. Balafar, A. R. Ramli, M. I. Saripan, S. Mashohor, Review of brain MRI image segmentation methods, Artificial Intelligence Review 33 (3) (2010) 261–274.

DOI: 10.1007/s10462-010-9155-0

Google Scholar

[16] D. L. Pham, C. Xu, J. L. Prince, Current methods in medical image segmentation, Annual Review of Biomedical Engineering 2 (1) (2000) 315–337.

DOI: 10.1146/annurev.bioeng.2.1.315

Google Scholar

[17] O. V. Senyukova, Segmentation of blurred objects by classification of isolabel contours, Pattern Recognition 47 (12) (2014)651 3881–3889.

DOI: 10.1016/j.patcog.2014.06.007

Google Scholar

[18] P Perona , J Malik, Scale Space and Edge Detection Using Anisotropic Diffusion, IEEE Trans on Pattern Analysis and Machine Intelligence, vol 12, no. 7 , pp.629-639, (1990).

DOI: 10.1109/34.56205

Google Scholar

[19] Gerig, R. Kikinis, O. Kubler and F.A. Jolesz, Nonlinear anisotropic filtering of MRI data, IEEE Trans on Medical Imaging, vol 11, no. 2 , pp.221-232, (1992).

DOI: 10.1109/42.141646

Google Scholar

[20] N. Malmurugan, A. Nagappan, Z C alex, V. Krishnaveni and Topny Gladwin George, Mammographic Enhancement Algorithm using Balanced Multiwavelet Transform for deetction of Microcalcification, Proceedings of ICIS 2005, University of Petronas, Malysia, Dec (2005).

Google Scholar

[21] Wells W, Grimson E, Kikinis R, Jolesz F. Adaptive segmentation of MRI data,. IEEE Trans Med Imaging 1996; 15(4): 429–42.

DOI: 10.1109/42.511747

Google Scholar

[22] Styner M, Brechbuhler C, Szekely G, Gerig G. Parametric estimate of intensity inhomogeneities applied to MRI,. IEEE Trans Med Imaging 2000; 19(3): 153–65.

DOI: 10.1109/42.845174

Google Scholar

[23] Leemput V, Maes K, Vandermeulen D, Suetens P. Automated model-based bias field correction of MR images of the brain,. IEEE Trans Med Imaging 1999; 18 (10): 885–96.

DOI: 10.1109/42.811268

Google Scholar

[24] Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI,. IEEE Trans Med Imaging 2007; 26(3): 405–21.

DOI: 10.1109/tmi.2006.891486

Google Scholar

[25] Pham D, Prince J. Adaptive fuzzy segmentation of magnetic resonance images,. IEEE Trans Med Imaging 1999; 18(9): 737–52.

DOI: 10.1109/42.802752

Google Scholar

[26] Pham D. Spatial models for fuzzy clustering,. Comput Vis Image Underst 2001; 84 (2): 285–97.

Google Scholar

[27] Condon BR, Patterson J, Wyper D. Image nonuniformity in magnetic resonance imaging: its magnitude and methods for its correction,. Br J Radiol 1987; 60 (1): 83–7.

DOI: 10.1259/0007-1285-60-709-83

Google Scholar

[28] Simmons A, Tofts PS, Barker GJ, Arrdige SR. Sources of intensity nonuniformity in spin echo images at 1. 5 t,. Magn Reson Med 1991; 32(1): 121–8.

DOI: 10.1002/mrm.1910320117

Google Scholar

[29] Wicks DAG, Barker GJ, Tofts PS. Correction of intensity nonuniformity in MR images of any orientation,. Magn Reson Imaging 1993; 11(2): 183–96.

DOI: 10.1016/0730-725x(93)90023-7

Google Scholar

[30] Narayana PA, Brey WW, Kulkarni MV, Sivenpiper CL. Compensation for surface coil sensitivity variation in magnetic resonance imaging,. Magn Reson Imaging 1988; 6(3): 271–4.

DOI: 10.1016/0730-725x(88)90401-8

Google Scholar

[31] Johnston B, Atkins MS, Mackiewich B, Anderson M. Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI,. IEEE Trans Med Imaging 1996; 15(2): 154–69.

DOI: 10.1109/42.491417

Google Scholar

[32] Dawant B, Zijdenbos A, Margolin R. Correction of intensity variations in MR images for computer-aided tissues classification,. IEEE Trans Med Imaging 1993; 12(4): 770–81.

DOI: 10.1109/42.251128

Google Scholar

[33] Sled J, Zijdenbos A, Evans A. A nonparametric method for automatic correction of intensity nonuniformity in MRI data,. IEEE Trans Med Imaging 1998; 17 (1): 87–97.

DOI: 10.1109/42.668698

Google Scholar

[34] Ahmed M, Yamany S, Mohamed N, Farag A, Moriarty T. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data,. IEEE Trans Med Imaging 2002; 21(3): 193–9.

DOI: 10.1109/42.996338

Google Scholar

[35] Salvado O, Hillenbrand C, Wilson D. Correction of intensity inhomogeneity in MR images of vascular disease". EMBS, 05. Shanghai: IEEE; 2005. p.4302–5.

DOI: 10.1109/iembs.2005.1615416

Google Scholar

[36] Li C, Huang R, Ding Z, Gatenby C, Metaxas D, Gore J. A variational level set approach to segmentation and bias correction of medical images with intensity inhomogeneity,. Proc. Medical Image Computing and Computer Aided Intervention (MICCAI), Vol. LNCS 5242, Part II; 2008. p.1083.

DOI: 10.1007/978-3-540-85990-1_130

Google Scholar

[37] Likar B, Viergever M, Pernus F. Retrospective correction of MR intensity inhomogeneity by information Minimization,. IEEE Trans Med Imaging 2001; 20 (12): 1398–410.

DOI: 10.1109/42.974934

Google Scholar

[38] . Chunming Li, John C. Gore and Christos Davatzikos Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation, MR Imaging 32 (2014) 913–923.

DOI: 10.1016/j.mri.2014.03.010

Google Scholar

[39] . A. Demirhan, İ. Güler, Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation, Eng. Applicat. of Artificial Intell., vol. 24, p.358–367, (2011).

DOI: 10.1016/j.engappai.2010.09.008

Google Scholar

[40] Meenu Bhatia, Dr. Saylee Gharge, Segmentation of Brain MR Image using Fuzzy Local Gaussian Mixture Model, 2014 International Conference on Advances in Communication and Computing Technologies.

DOI: 10.1109/eic.2015.7230720

Google Scholar

[41] Chaolu Feng, Dazhe Zhao, Min Huang, Image Segmentation and Bias Correction Using Local Inhomogeneous iNtensity Clustering (LINC): A Region-based Level Set Method, To appear in: Neurocomputing.

DOI: 10.1016/j.neucom.2016.09.008

Google Scholar

[42] Huang C, Zeng L (2015) An Active Contour Model for the Segmentation of Images with Intensity Inhomogeneities and Bias Field Estimation,. PLoS ONE 10(4): e0120399. doi: 10. 1371/journal. pone. 0120399.

DOI: 10.1371/journal.pone.0120399

Google Scholar

[43] Leemput V, Maes K, Vandermeulen D, Suetens P. Automated model-based bias field correction of MR images of the brain,. IEEE Trans Med Imaging 1999; 18 (10): 885–96.

DOI: 10.1109/42.811268

Google Scholar

[44] C. Li, R. Huang, Z. Ding, J. Gatenby, D. N. Metaxas, J. C. Gore, A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI, IEEE Transactions on Image Processing 20 (7) (2011) 2007–(2016).

DOI: 10.1109/tip.2011.2146190

Google Scholar

[45] http: /brainweb. bic. mni. mcgill. ca/brainweb.

Google Scholar