Abstract
Brain tissue segmentation is important in studying markers in human brain Magnetic Resonance Images (MRI) of patients with diseases such as Multiple Sclerosis (MS). Parametric segmentation approaches typically assume unimodal Gaussian distributions on MRI intensities of individual tissue classes, even in applications on multi-spectral images. However, this assumption has not been rigorously verified especially in the context of MS. In this work, we evaluate the local MRI intensities of both healthy and diseased brain tissues of 21 multi-spectral MRIs (63 volumes in total) of MS patients for adherence to this assumption. We show that the tissue intensities are not uniform across the brain and vary across (anatomical) regions of the brain. Consequently, we show that Gaussian mixtures can better model the multi-spectral intensities. We utilize an Expectation Maximization (EM) based approach to learn the models along with a symmetric Jeffreys divergence criterion to study differences in intensity distributions. The effects of these findings are also empirically verified on automatic segmentation of brains with MS.
This work was supported by NSERC Strategic Grant (350547-07).
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References
Butz, T., et al.: A new brain segmentation framework. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 586–593. Springer, Heidelberg (2003)
Cuadra, M.B., et al.: Comparison and validation of tissue modelization and sta- tistical classification methods in T1-weighted MR brain images. IEEE TMI 24, 1548–1565 (2005)
Datta, S., et al.: Improved cerebellar tissue classification on magnetic resonance images of brain. J. Magn. Reson. Imaging 29(5), 1035–1042 (2009)
Derakhshan, M., et al.: Evaluation of Automated Techniques for the Quantification of Grey Matter Atrophy in Patients with Multiple Sclerosis. In: Neuroimage 2010 (2010) [Epub ahead of print]
Madabhushi, A., Udupa, J.K.: Interplay between intensity standardization and inhomogeneity correction in MR image processing. IEEE TMI 24(5), 561–576 (2005)
Nyul, L.G., Udupa, J.K.: On standardizing the MR Image Intensity Scale. Mag. Res. in Med. 42, 1072–1081 (1999)
Nyul, L.G., et al.: New Variants of a Method of MRI Scale Standardization. IEEE TMI 19(2), 143–150 (2000)
Pham, D.L., et al.: Current Methods in Medical Image Segmentation, vol. 2, 315–338 (2000)
Puzicha, J., et al.: Empirical evaluation of dissimilarity measures for color and texture. In: ICCV 1999, vol. 2, pp. 1165–1173 (1999)
Ruf, A., et al.: Tissue classification of noisy mr brain images using constrained gmm. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 790–797. Springer, Heidelberg (2005)
Sled, J.G., et al.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)
Van Leemput, K., et al.: Automated model-based tissue classification of MR images of the brain. IEEE TMI 18(10), 897–908 (1999)
Zhang, Y., et al.: Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE TMI 20, 45–57 (2005)
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Xiao, Y., Shah, M., Francis, S., Arnold, D.L., Arbel, T., Collins, D.L. (2010). Optimal Gaussian Mixture Models of Tissue Intensities in Brain MRI of Patients with Multiple-Sclerosis. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds) Machine Learning in Medical Imaging. MLMI 2010. Lecture Notes in Computer Science, vol 6357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15948-0_21
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DOI: https://doi.org/10.1007/978-3-642-15948-0_21
Publisher Name: Springer, Berlin, Heidelberg
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