Mutual information-based 3D surface matching with applications to face recognition and brain mapping

Yalin Wang, Ming Chang Chiang, Paul M. Thompson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Scopus citations

Abstract

Face recognition and many medical imaging applications require the computation of dense correspondence vector fields that match one surface with another. In brain imaging, surface-based registration is useful for tracking brain change, and for creating statistical shape models of anatomy. Based on surface correspondences, metrics can also be designed to measure differences in facial geometry and expressions. To avoid the need for a large set of manually-defined landmarks to constrain these surface correspondences, we developed an algorithm to automate the matching of surface features. It extends the mutual information method to automatically match general 3D surfaces (including surfaces with a branching topology). We use diffeomorphic flows to optimally align the Riemann surface structures of two surfaces. First, we use holomorphic 1-forms to induce consistent conformal grids on both surfaces. High genus surfaces are mapped to a set of rectangles in the Euclidean plane, and closed genus-zero surfaces are mapped to the sphere. Next, we compute stable geometric features (mean curvature and conformai factor) and pull them back as scalar fields onto the 2D parameter domains. Mutual information is used as a cost functional to drive a fluid flow in the parameter domain that optimally aligns these surface features. A diffeomorphic surface-to-surface mapping is then recovered that matches surfaces in 3D. Lastly, we present a spectral method that ensures that the grids induced on the target surface remain conformal when pulled through the correspondence field. Using the chain rule, we express the gradient of the mutual information between surfaces in the conformal basis of the source surface. This finite-dimensional linear space generates all conformal reparameterizations of the surface. Illustrative experiments apply the method to face recognition and to the registration of brain structures, such as the hippocampus in 3D MRI scans, a key step in understanding brain shape alterations in Alzheimer's disease and schizophrenia.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Pages527-534
Number of pages8
DOIs
StatePublished - Dec 1 2005
Externally publishedYes
EventProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 - Beijing, China
Duration: Oct 17 2005Oct 20 2005

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
VolumeI

Other

OtherProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
CountryChina
CityBeijing
Period10/17/0510/20/05

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Wang, Y., Chiang, M. C., & Thompson, P. M. (2005). Mutual information-based 3D surface matching with applications to face recognition and brain mapping. In Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 (pp. 527-534). [1541299] (Proceedings of the IEEE International Conference on Computer Vision; Vol. I). https://doi.org/10.1109/ICCV.2005.165