A Novel Material-Aware Feature Descriptor for Volumetric Image Registration in Diffusion Tensor Space

Abstract

This paper advocates a novel material-aware feature descriptor for volumetric image registration. We rigorously formulate a novel probability density function (PDF) based distance metric to devise a compact local feature descriptor supporting invariance of full 3D orientation and isometric deformation. The central idea is to employ anisotropic heat diffusion to characterize the detected local volumetric features. It is achieved by the elegant unification of diffusion tensor (DT) space construction based on local Hessian eigen-system, multi-scale feature extraction based on DT-weighted dyadic wavelet transform, and local distance definition based on PDF formulated in DT space. The diffusion, intrinsic structure-aware nature makes our volumetric feature descriptor more robust to noise. With volumetric images registration as verifiable application, various experiments on different volumetric images demonstrate the superiority of our descriptor.