TY - GEN
T1 - A novel material-aware feature descriptor for volumetric image registration in diffusion tensor space
AU - Li, Shuai
AU - Zhao, Qinping
AU - Wang, Shengfa
AU - Hou, Tingbo
AU - Hao, Aimin
AU - Qin, Hong
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84867871962
U2 - 10.1007/978-3-642-33765-9_36
DO - 10.1007/978-3-642-33765-9_36
M3 - Conference contribution
AN - SCOPUS:84867871962
SN - 9783642337642
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 502
EP - 515
BT - Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
T2 - 12th European Conference on Computer Vision, ECCV 2012
Y2 - 7 October 2012 through 13 October 2012
ER -