Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images

[Display omitted] •Proposing a framework that combines kernel regression and diffusion wavelets.•The kernel regression is given in terms of the Laplace–Beltrami eigenfunctions.•Random field theory is incorporated for statistical inference.•The method is used to characterize the mandible growth in CT images. We present a novel kernel regression framework for smoothing scalar surface data using the Laplace–Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template.