Abstract
The human brain may be considered as a genus-0 shape, topologically equivalent to a sphere. Various methods have been used in the past to transform the brain surface to that of a sphere using harmonic energy minimization methods used for cortical surface matching. However, very few methods have studied volumetric parameterization of the brain using a spherical embedding. Volumetric parameterization is typically used for complicated geometric problems like shape matching, morphing and isogeometric analysis. Using conformal mapping techniques, we can establish a bijective mapping between the brain and the topologically equivalent sphere. Our hypothesis is that shape analysis problems are simplified when the shape is defined in an intrinsic coordinate system. Our goal is to establish such a coordinate system for the brain. The efficacy of the method is demonstrated with a white matter clustering problem. Initial results show promise for future investigation in these parameterization technique and its application to other problems related to computational anatomy like registration and segmentation.
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Acknowledgements
This study was supported in part by a Consortium grant (U54 EB020403) from the NIH Institutes contributing to the Big Data to Knowledge (BD2K) Initiative, including the NIBIB. The authors are also thankful to Dr. Ratnesh Kumar from Teradeep Inc., Sunnyvale (California) for valuable insights and suggestions.
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Gupta, V., Prasad, G., Thompson, P. (2016). A Volumetric Conformal Mapping Approach for Clustering White Matter Fibers in the Brain. In: Reuter, M., Wachinger, C., Lombaert, H. (eds) Spectral and Shape Analysis in Medical Imaging. SeSAMI 2016. Lecture Notes in Computer Science(), vol 10126. Springer, Cham. https://doi.org/10.1007/978-3-319-51237-2_1
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DOI: https://doi.org/10.1007/978-3-319-51237-2_1
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