Abstract
A three-dimensional surface-based progressive alignment algorithm is proposed to recover nonlinear deformation field. The deformation field is represented with a multi-resolution wavelet expansion and is modeled by the partial differential equations of linear elasticity. We report of its use in spatial normalization of mouse brains reconstructed from sectional material. The wavelet alignment algorithm produced more than threefold improvement in accuracy over an affine (linear) alignment. Its susceptibility to sparse sampling, a problem when the data is derived from tissue sections, was evaluated. Registration accuracy was reduced by only two fold as sampling decreased six- fold.
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Gefen, S., Tretiak, O., Bertrand, L., Nissanov, J. (2003). Mouse Brain Spatial Normalization: The Challenge of Sparse Data. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds) Biomedical Image Registration. WBIR 2003. Lecture Notes in Computer Science, vol 2717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39701-4_37
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DOI: https://doi.org/10.1007/978-3-540-39701-4_37
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