Paper Info
Title
Registration of contours of brain structures through a heat-kernel representation of shape
Abstract
We develop an algorithm for the registration of surfaces representing the contours of various subcortical structures of the human brain. We employ a scale-space representation of shape based on the heat kernel, which only depends on the intrinsic geometry of the surfaces. The multi-scale representation is used in conjunction with the non-linear Iterative Closest Point algorithm based on thin-plate-spline warps to establish point correspondences between shapes. The method is applied to the registration of the contours of four subcortical structures: the hippocampus, caudate nucleus, putamen, and third ventricle.
Year
DOI
Venue
2009
10.1109/ISBI.2009.5193209
ISBI
Keywords
Field
DocType
point correspondence,non-linear iterative closest point,intrinsic geometry,various subcortical structure,multi-scale representation,human brain,heat-kernel representation,scale-space representation,caudate nucleus,heat kernel,brain structure,subcortical structure,shape,kernel,scale space,thin plate spline,heating,iterative methods,hippocampus,image registration
Kernel (linear algebra),Computer vision,Thin plate spline,Pattern recognition,Iterative method,Computer science,Heat kernel,Artificial intelligence,Intrinsic geometry,Spectral representation,Image registration,Iterative closest point
Conference
Citations 
PageRank 
References 
4
0.61
6
Authors
4
Name
Order
Citations
PageRank
Jonathan Bates1172.49
Ying Wang261.32
Xiuwen Liu374480.44
Washington Mio454440.82