Abstract
This paper examines the effect of bilateral anatomical asymmetry of spatial priors on the final tissue classification based on maximum-likelihood (ML) estimates of model parameters, in a model-based intensity driven brain tissue segmentation algorithm from (possibly multispectral) MR images. The asymmetry inherent in the spatial priors is enforced on the segmentation routine by laterally flipping the priors during the initialization stage. The influence of asymmetry on the final classification is examined by making the priors subject-specific using non-rigid warping, by reducing the strength of the prior information, and by a combination of both. Our results, both qualitative and quantitative, indicate that reducing the prior strength alone does not have any significant impact on the segmentation performance, but when used in conjunction with the subject-specific priors, helps to remove the misclassifications due to the influence of the asymmetric priors.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Cocosco, C.A., Kollokian, V., Kwan, R.K.-S., Evans, A.C.: Brainweb: Online interface to a 3d mri simulated brain database. NeuroImage part 2/4 5(4), S425 (1997)
Evans, A.C., Collins, D.L., Mills, S.R., Brown, E.D., Kelly, R.L., Peters, T.M.: 3d statistical neuroanatomical models from 305 mri volumes. In: Proc. IEEE Nuclear Science Symposium and Medical Imaging Conference, pp. 1813–1817 (1993)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging 18(10), 897–908 (1999)
Maes, F., Collignon, A., Vandermeulen, D., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16(2), 187–198 (1997)
Marroquin, J.L., Vemuri, B.C., Botello, S., Calderon, F., Fernandez-Bouzas, A.: An accurate and efficient bayesian method for automatic segmentation of brain MRI. IEEE Transactions on Medical Imaging 21(8), 934–945 (2002)
Wellcome Department of Cognitive Neurology. Statistical Parametric Mapping (SPM), http://www.fil.ion.ucl.ac.uk/spm/
Pohl, K.M., Wells, W.M., Guimond, A., Kasai, K., Shenton, M.E., Kikinis, R., Grimson, W.E.L., Warfield, S.K.: Incorporating non-rigid registration into expectation maximization algorithm to segment mr images. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 564–571. Springer, Heidelberg (2002)
Prima, S., Ourselin, S., Ayache, N.: Computation of the mid-saggital plane in 3d brain images. IEEE Transactions on Medical Imaging 21(2), 122–138 (2002)
Zijdenbos, A.P., Dawant, B.M., Margolin, R.A., Palmer, A.C.: Morphometric analysis of white matter lesions in MR images: Methods and validation. IEEE Transactions on Medical Imaging 13(4), 716–724 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Srivastava, S., Maes, F., Vandermeulen, D., Van Paesschen, W., Dupont, P., Suetens, P. (2004). Effects of Anatomical Asymmetry in Spatial Priors on Model-Based Segmentation of the Brain MRI: A Validation Study. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30135-6_40
Download citation
DOI: https://doi.org/10.1007/978-3-540-30135-6_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22976-6
Online ISBN: 978-3-540-30135-6
eBook Packages: Springer Book Archive