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

Medical Image Analysis
Moo K ChungHouri K Vorperian

Abstract

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.

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Citations

Jul 10, 2016·International Journal of Computer Assisted Radiology and Surgery·Stefan RaithHorst Fischer
Feb 6, 2017·Archives of Oral Biology·Michael P KellyLindell R Gentry
Jul 25, 2018·Sensors·Ibrahim Salim, A Ben Hamza
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Jun 23, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Shih-Gu HuangUNKNOWN Alzheimer’s Disease Neuroimaging Initiative
Sep 24, 2021·Scientific Reports·C Klop, UNKNOWN MAGIC Amsterdam

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