Abstract
Registration of Diffusion Weighted Images (DWI) is challenging as the data, in contrast to scalar-valued images, is a composition of both directional and intensity information. The DWI signal is known to be influenced by noise and a wide range of artifacts. Therefore, it is attractive to use similarity measures with invariance properties, such as Mutual Information. However, density estimation from DWI is complicated by directional information. We address this problem by extending Locally Orderless Registration (LOR), a density estimation framework for image similarity, to include directional information. We construct a spatio-directional scale-space formulation of marginal and joint density distributions between two DWI, that takes the projective nature of the directional information into account. This accounts for orientation and magnitude and enables us to use a wide range of similarity measures from the LOR framework. Using Mutual Information, we examine the properties of the scale-space induced by the choice of kernels and illustrate the approach by affine registration.
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Keywords
- Mutual Information
- Image Registration
- Joint Histogram
- Human Connectome Project
- Generalize Similarity Measure
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References
Darkner, S., Sporring, J.: Generalized partial volume: An inferior density estimator to parzen windows for normalized mutual information. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 436–447. Springer, Heidelberg (2011)
Darkner, S., Sporring, J.: Locally Orderless Registration. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(6), 1437–1450 (2013)
Duarte-Carvajalino, J.M., Sapiro, G., Harel, N., Lenglet, C.: A framework for linear and non-linear registration of diffusion-weighted mris using angular interpolation. Frontiers in Neuroscience 7 (2013)
Hermosillo, G., Chefd’Hotel, C., Faugeras, O.: Variational methods for multimodal image matching. International Journal of Computer Vision 50(3), 329–343 (2002)
Jupp, P.E., Mardia, K.: A unified view of the theory of directional statistics, 1975-1988. International Statistical Review, 261–294 (1989)
Koenderink, J., Van Doorn, A.: The structure of locally orderless images. International Journal of Computer Vision 31(2), 159–168 (1999)
Raffelt, D., Tournier, J., Fripp, J., Crozier, S., Connelly, A., Salvado, O., et al.: Symmetric diffeomorphic registration of fibre orientation distributions. NeuroImage 56(3), 1171–1180 (2011)
Rathi, Y., Michailovich, O., Shenton, M.E., Bouix, S.: Directional functions for orientation distribution estimation. Medical Image Analysis 13(3), 432–444 (2009)
Rohde, G., Barnett, A., Basser, P., Marenco, S., Pierpaoli, C.: Comprehensive approach for correction of motion and distortion in diffusion-weighted mri. Magnetic Resonance in Medicine 51(1), 103–114 (2004)
Tao, X., Miller, J.V.: A method for registering diffusion weighted magnetic resonance images. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 594–602. Springer, Heidelberg (2006)
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K.: The wu-minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)
Van Hecke, W., Leemans, A., D’Agostino, E., De Backer, S., Vandervliet, E., Parizel, P.M., Sijbers, J.: Nonrigid coregistration of diffusion tensor images using a viscous fluid model and mutual information. IEEE Transactions on Medical Imaging 26(11), 1598–1612 (2007)
Wells, W.M., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Medical Image Analysis 1(1), 35–51 (1996)
Yap, P.T., Chen, Y., An, H., Yang, Y., Gilmore, J.H., Lin, W., Shen, D.: Sphere: Spherical harmonic elastic registration of hardi data. NeuroImage 55(2), 545–556 (2011)
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Jensen, H.G., Lauze, F., Nielsen, M., Darkner, S. (2015). Locally Orderless Registration for Diffusion Weighted Images. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham. https://doi.org/10.1007/978-3-319-24571-3_37
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DOI: https://doi.org/10.1007/978-3-319-24571-3_37
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