Abstract
The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects. The spatial information is incorporated by convolving the posterior probability during E-Step of the EM algorithm with mean filter. Also, a method of pixel re-labeling is included to improve the segmentation accuracy. The proposed method is validated by extensive experiments on both simulated and real brain images from standard database. Quantitative and qualitative results depict that the method is superior to the conventional methods by around 25% and over the state-of-the art method by 8%.
Similar content being viewed by others
References
Yazdani, S., Yusof, R., Karimian, A., Pashna, M., and Hematian, A., Image segmentation methods and applications in MRI brain images. IETE Tech. Rev. 32:413–427, 2015.
Pham, D.L., Xu, C., and Prince, J.L., Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2:315–337, 2000.
Liew, A. W.-C., Yan, H., Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images. Curr. Med. Imaging Rev. 2006.
Van Leemput, K., Maes, F., Vandermeulen, D., and Suetens, P., A unifying framework for partial volume segmentation of brain MR images. IEEE Trans. Med. Imaging. 22(1):105–119, 2003.
Nguyen, T.M., and Wu, Q.M.J., Gaussian-mixture-model-based spatial neighborhood relationships for pixel labeling problem. IEEE Trans. Syst. Man Cybern. 42(1):193–202, 2012.
Balafar, M.A., Spatial based expectation maximizing (EM). Diagn. Pathol. 6:103, 2011.
Balafar, M.A., Gaussian mixture model based segmentation methods for brain MRI images. Artif. Intell. Rev. 41(3):429–439, 2012.
Xie, M., Gao, J., Zhu, C., and Zhou, Y., A modified method for MRF segmentation and bias correction of MR image with intensity inhomogeneity. Med. Biol. Eng. Comput. 53(1):23–35, 2015.
Greenspan, H., Ruf, A., and Goldberger, J., Constrained gaussian mixture model framework for auotmatic segmentation of MR brain images. IEEE Trans. Med. Imaging. 25(9):1233–1245, 2006.
Lee, J.-D., Su, H.-R., Cheng, P.E., Liou, M., Aston, J.A.D., Tsai, A.C., and Chen, C.-Y., MR image segmentation using a power transformation approach. IEEE Trans. Med. Imaging. 28(6):894–905, 2009.
Siyal, M.Y., and Yu, L., An intelligent modified fuzzy, c-means based algorithm for bias estimation and segmentation of brain MRI. Pattern Recogn. Lett. 26:2052–2062, 2005.
Mekhmoukh, A., and Mokrani, K., Improved fuzzy C-means based particle swarm optimization (PSO) initialization and outlierrejection with level set methods for MR brainimage segmentation. Comput. Methods Prog. Biomed. 122(2):266–281, 2015.
Zhang, X., Wang, G., Su, Q., Guo, Q., Zhang, C., and Chen, B., An improved fuzzy algorithm for image segmentation using peak detection, spatial information and reallocation. Soft Computing:1–9, 2015.
Zhao, F., Fan, J., and Liu, H., Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation. Expert Systems with Applications. 41:4083–4093, 2014.
Ji, Z., Liu, J., Cao, G., Sun, Q., and Chen, Q., Robust spatially constrained fuzzy c-means algorithm for brain MR image segmentation. Pattern Recogn. 47:2454–2466, 2014.
Madhukumar, S., and Santhiyakumari, N., Evaluation of k-means and fuzzy C-means segmentation of MR images of brain. Egypt. J. Radiol. Nucl. Med. 46:475–479, 2015.
Zhang, J., Jiang, W., Wang, R., Wang, L., and Brain, M.R., Image segmentation with spatial constrained K-mean algorithm and dual-tree complex wavelet transform. J. Med. Syst. 38:93, 2014.
Ali, H., Elmogy, M., El-Daydamony, E., Atwan, A., and Multi-resolution, M.R.I., Brain image segmentation based on morphological pyramid and fuzzy C-mean clustering. Arab. J. Sci. Eng. 40(11):3173–3185, 2015.
Chen, Z., Wang, J., Kong, D., and Dong, F., A nonlocal energy minimization approach to brain image segmentation with simultaneous bias field estimation and denoising. Mach. Vis. Appl. 25:529–544, 2014.
Taherdangkoo, M., Bagheri, M.H., Yazdi, M., and Andriole, K.P., An effective method for segmentation of MR brain images using the ant colony optimization algorithm. J. Digit. Imaging. 26:1116–1123, 2013.
Huang, C., and Zeng, L., An active contour model for the segmentation of images with intensity inhomogeneities and bias field estimation. PLoS One. 10(4):e0120399, 2015.
Li, X., Jiang, D., Shi, Y., and Li, W., Segmentation of MR image using local and global region based geodesic model. BioMed. Eng. OnLine. 14:8, 2015.
Prakash, R. M., Kumari, R. S. S., Gaussian mixture model with the inclusion of spatial factor and pixel re-labelling: Application to MR brain image segmentation. Arab. J. Sci. Eng. 2016.
Prakash, R.M., and Kumari, R.S.S., Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images. Int. J. Imaging Syst. Technol. 26(2):116–123, 2016.
Bishop, C.M., Pattern recognition and machine learning. Springer, New York, 2006.
Dempster, A.P., Laird, N.M., and Rubin, D.B., Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39:1–38, 1977.
Carson, C., Belongie, S., Greenspan, H., and Malik, J., Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Anal. Mach. Intell. 24(8):1026–1038.
Bezdek, J., Pattern recognition with fuzzy objective function algorithms. Plenum, New York, 1981.
Dunn, J., A fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clusters. J. Cybern. 3:32–57, 1974.
Author information
Authors and Affiliations
Corresponding author
Additional information
This article is part of the Topical Collection on Image & Signal Processing
Rights and permissions
About this article
Cite this article
Meena Prakash, R., Shantha Selva Kumari, R. Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images. J Med Syst 41, 15 (2017). https://doi.org/10.1007/s10916-016-0662-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-016-0662-7