Abstract
Atlas construction generally includes first an image registration step to normalize all images into a common space and then an atlas building step to fuse all the aligned images. Although numerous atlas construction studies have been performed to improve the accuracy of image registration step, simple averaging or weighted averaging is often used for the atlas building step. In this paper, we propose a novel patch-based sparse representation method for atlas construction, especially for the atlas building step. By taking advantage of local sparse representation, more distinct anatomical details can be revealed in the built atlas. Also, together with the constraint on group structure of representations and the use of overlapping patches, anatomical consistency between neighboring patches can be ensured. The proposed method has been applied to 73 neonatal MR images with poor spatial resolution and low tissue contrast, for building unbiased neonatal brain atlas. Experimental results demonstrate that the proposed method can enhance the quality of built atlas by discovering more anatomical details especially in cortical regions, and perform better in a neonatal data normalization application, compared to other existing start-of-the-art nonlinear neonatal brain atlases.
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Wu, G., Wang, Q., Jia, H., Shen, D.: Feature-based groupwise registration by hierarchical anatomical correspondence detection. Human Brain Mapping 33, 253–271 (2012)
Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Towards robust and effective shape modeling: sparse shape composition. Med. Image Anal. 16, 265–277 (2012)
Vinje, W.E., Gallant, J.L.: Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287, 1273–1276 (2000)
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Transactions on Image Processing 19, 2861–2873 (2010)
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society 58, 267–288 (1996)
Liu, J., Ji, S., Ye, J.: Multi-task feature learning via efficient l2,1-norm minimization. Uncertainty in Artificial Intelligence (UAI), 339–348 (2009)
Shi, F., Wang, L., Dai, Y., Gilmore, J.H., Lin, W., Shen, D.: LABEL: Pediatric Brain Extraction using Learning-based Meta-algorithm. NeuroImage (in press, 2012)
Wang, L., Shi, F., Lin, W., Gilmore, J.H., Shen, D.: Automatic Segmentation of Neonatal Images Using Convex Optimization and Coupled Level Sets. NeuroImage 58, 805–817 (2011)
Kuklisova-Murgasova, M., Aljabar, P., Srinivasan, L., Counsell, S., Doria, V., Serag, A., Gousias, I., Boardman, J., Rutherford, M., Edwards, A.: A dynamic 4D probabilistic atlas of the developing brain. NeuroImage 54, 2750–2763 (2010)
Oishi, K., Mori, S., Donohue, P.K., Ernst, T., Anderson, L., Buchthal, S., Faria, A., Jiang, H., Li, X., Miller, M.I.: Multi-contrast human neonatal brain atlas: application to normal neonate development analysis. NeuroImage 56, 8–20 (2011)
Serag, A., Aljabar, P., Ball, G., Counsell, S.J., Boardman, J.P., Rutherford, M.A., Edwards, A.D., Hajnal, J.V., Rueckert, D.: Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression. NeuroImage 59, 2255–2265 (2012)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage 45, S61–S72 (2009)
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© 2012 Springer-Verlag Berlin Heidelberg
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Shi, F. et al. (2012). Atlas Construction via Dictionary Learning and Group Sparsity. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_31
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DOI: https://doi.org/10.1007/978-3-642-33415-3_31
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