A segmentation of brain MRI images utilizing intensity and contextual information by Markov random field

References

• Bauer S, Wiest R, Nolte L, et al. A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol. 2013;58:R97–R129.
   PubMed Web of Science ®Google Scholar
• Zhuang X. Challenges and methodologies of fully automatic whole heart segmentation: a review. J Healthc Eng. 2013;4:371–407.
   PubMed Web of Science ®Google Scholar
• Gordillo N, Montseny E, Sobrevilla P. State of the art survey on MRI brain tumor segmentation. J Magn Reson Imaging. 2013;31:1426–1438.
   Web of Science ®Google Scholar
• Balafar MA, Ramli AR, Saripan MI, et al. Review of brain MRI image segmentation methods. Artif Intell Rev. 2010;33:261–274.
   Web of Science ®Google Scholar
• West J, Blystad I, Engstrom M, et al. Application of quantitative MRI for brain tissue segmentation at 1.5 T and 3.0 T field strengths. PloS One. 2013;8:e74795.
   PubMed Web of Science ®Google Scholar
• West J, Warntjes JBM, Lundberg P. Novel whole brain segmentation and volume estimation using quantitative MRI. Eur Radiol. 2012;22:998–1007.
   PubMed Web of Science ®Google Scholar
• Flórez N, Bouzerar R, Moratal D, et al. Quantitative analysis of cerebrospinal fluid flow in complex regions by using phase contrast magnetic resonance imaging. Int J Imaging Syst Technol. 2011;21:290–297.
   Web of Science ®Google Scholar
• Sled J, Nossin-Manor R. Quantitative MRI for studying neonatal brain development. Neuroradiology. 2013;55:97–104.
   PubMed Web of Science ®Google Scholar
• Balafar MA. Gaussian mixture model based segmentation methods for brain MRI images. Artif Intell Rev. 2014;41:429–439.
   Web of Science ®Google Scholar
• Wang L, Shi F, Li G, et al. Segmentation of neonatal brain MR images using patch-driven level sets. Neuroimage. 2014;84:141–158.
   PubMed Web of Science ®Google Scholar
• Cabezas M, Oliver A, Lladó X, et al. A review of atlas-based segmentation for magnetic resonance brain images. Comput Methods Programs Biomed. 2011;104:e158–e177.
   PubMed Web of Science ®Google Scholar
• JC. Bezdek Pattern recognition with fuzzy objective function algorithms. New York (NY): Springer US, 1981.
   Google Scholar
• Tolias YA, Panas SM. Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions. IEEE Trans Syst, Man, Cybern A. 1998;28:359–369.
   Web of Science ®Google Scholar
• Ahmed MN, Yamany SM, Mohamed N, et al. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging. 2002;21:193–199.
   PubMed Web of Science ®Google Scholar
• Cai W, Chen S, Zhang D. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn. 2007;40:825–838.
   Web of Science ®Google Scholar
• Krinidis S, Chatzis V. A Robust Fuzzy Local Information C-Means Clustering Algorithm. IEEE Trans Image Process. 2010;19:1328–1337.
   PubMed Web of Science ®Google Scholar
• Wang Z, Song Q, Soh YC, et al. An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation. Comput Vis Image Underst. 2013;117:1412–1420.
   Web of Science ®Google Scholar
• Scherrer B, Forbes F, Garbay C, et al. Distributed local MRF models for tissue and structure brain segmentation. IEEE Trans Med Imaging. 2009;28:1278–1295.
   PubMed Web of Science ®Google Scholar
• Ashraf AB, Gavenonis SC. A multichannel markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk,”. IEEE Trans Med Imaging. 2013;32:637–648.
   PubMed Web of Science ®Google Scholar
• Yousefi S, Azmi R, Zahedi M. Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms. Medical Image Analysis. 2012;16:840–848.
   PubMed Web of Science ®Google Scholar
• Tohka J, Dinov ID, Shattuck DW, et al. Brain MRI tissue classification based on local Markov random fields. J Magn Reson Imaging. 2010;28:557–573.
   Web of Science ®Google Scholar
• Chatzis SP, Varvarigou TA. A fuzzy clustering approach toward Hidden Markov random field models for enhanced spatially constrained image segmentation. IEEE Trans Fuzzy Syst. 2008;16:1351.
   Web of Science ®Google Scholar
• Gong M, Su L, Jia M, et al. Fuzzy Clustering With a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images. IEEE Trans Fuzzy Syst. 2014;22:98–109.
   Web of Science ®Google Scholar
• Liu G, Li P, Zhang Y. A color texture image segmentation method based on fuzzy c-means clustering and region-level markov random field model. Math Probl Eng. 2015;2015:1.
   Google Scholar
• Liu J, Lei Y, Xing Y, et al. Multispectral and panchromatic images fusion using the Markov-random-field-based FCM. Remote Sens Lett. 2015;6:992–1001.
   Web of Science ®Google Scholar
• Li SZ. Markov random field modeling in image analysis. New York: Springer-Verlag, 2009.
   Google Scholar
• Geman S, Geman D. Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell. 1984;6:721–741.
   PubMed Web of Science ®Google Scholar
• Besag J. Spatial interaction and the statistical analysis of lattice systems. J R Stat Soc Series B. 1974;36:192–236.
   Google Scholar
• Zijdenbos AP, Dawant BM. Brain segmentation and white matter lesion detection in MRI images. Crit Rev Biomed Eng. 1994;22:401–465.
   PubMedGoogle Scholar
• Sijbers J, den Dekker AJ, Scheunders P, et al. Maximum-likelihood estimation of Rician distribution parameters. IEEE Trans Med Imaging. 1998;17:357–361.
   PubMed Web of Science ®Google Scholar
• Kwan RKS, Evans A, Pike GB. MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans Med Imaging. 1999;18:1085–1097.
   PubMed Web of Science ®Google Scholar
• Rohlfing T. Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable. IEEE Trans Med Imaging. 2012;31:153–163.
   PubMed Web of Science ®Google Scholar