Abstract
Despite being routinely required in medical applications, deformable surface registration is notoriously difficult due to large intersubject variability and complex geometry of most medical datasets. We present a general and flexible deformable matching framework based on generalized surface flows that efficiently tackles these issues through tailored deformation priors and multiresolution computations. The value of our approach over existing methods is demonstrated for automatic and user-guided cortical registration. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Eckstein, I., Joshi, A. A., Kuo, C. C. J., Leahy, R., & Desbrun, M. (2007). Generalized surface flows for deformable registration and cortical matching. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4791 LNCS, pp. 692–700). Springer Verlag. https://doi.org/10.1007/978-3-540-75757-3_84
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