scholarly journals A Novel Information-Theoretic Point-Set Measure Based on the Jensen-Havrda-Charvat-Tsallis Divergence

2008 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Suyash Awate ◽  
James Gee
Keyword(s):  
Matrix Decomposition ◽  
Fine Tuning ◽  
Information Theoretic ◽  
Point Set Registration ◽  
Registration Algorithm ◽  
Divergence Point ◽  
Theoretic Point ◽  
Point Set ◽  
Jensen Shannon Divergence ◽  
Decomposition Schemes

A novel point-set registration algorithm was proposed in [6] based on minimization of the Jensen-Shannon divergence. In this contribution, we generalize this Jensen-Shannon divergence point-set measure framework to the Jensen-Havrda-Charvat-Tsallis divergence. This generalization permits a fine-tuning of the actual divergence measure between robustness and specificity. The principle contribution of this submission is theitk::JensenHavrdaCharvatTsallisPointSetMetric class which is derived from the existing itk::PointSetToPointSetMetric. In addition, we provide other classes with utility that would extend beyond the point-set measure framework that we provide in this paper. This includes a point-set analogue of the itk::ImageFunction, i.e. itk::PointSetFunction. From this class we derive the class itk::ManifoldParzenWindowsPointSetFunction which provides a Parzen windowing scheme for learning the local structure of point-sets. Finally, we include the itk::DecomposeTensorFunction class which wraps the different vnl matrix decomposition schemes for easy use within ITK.

scholarly journals Information-Theoretic Directly Manipulated Free-Form Deformation Labeled Point-Set Registration

2010 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Suyash Awate ◽  
James Gee
Keyword(s):  
Spline Approximation ◽  
Transformation Model ◽  
Free Form ◽  
Theoretic Approach ◽  
Cardinality Constraints ◽  
Information Theoretic ◽  
Point Set Registration ◽  
Local Point ◽  
Free Form Deformation ◽  
Point Set

Our previous contributions to the ITK community include a generalized B-spline approximation scheme as well as a generalized information-theoretic measure for assessing point-set correspondence known as the Jensen-Havrda-Charvat-Tsallis (JHCT) divergence. In this submission, we combine these two contributions for the registration of labeled point-sets. The transformation model which uses the former contribution is denoted as directly manipulated free-form deformation (DMFFD) and has been used for image registration. The information-theoretic approach described not only eliminates exact cardinality constraints which plague exact landmark matching algorithms, but it also incorporates the local point-set structure into the similarity measure calculation. Although theoretical discussion of these two components is deferred to other venues, the implementation details given in this submission should be adequate for those wishing to use our algorithm. Visualization of results is aided by another of our previous contributions. Additionally, we provide the rudimentary command line parsing classes used in our testing routines which were written in the ITK style and also available to use consistent with the open-source paradigm.

An Improved Non-Rigid Point Set Registration Algorithm by Preserving Local Topology

2021 ◽  
Vol 31 (4) ◽  
pp. 646-655
Author(s):  
Qiang Sang ◽  
Tao Huang ◽  
Huihuang Tang ◽  
Ping Jiang
Keyword(s):  
Point Set Registration ◽  
Registration Algorithm ◽  
Point Set ◽  
Local Topology

A robust point set registration algorithm based on information geometry

2014 ◽  
Author(s):  
Xiaoqiang Hua ◽  
Ping Wang ◽  
Kefeng Ji ◽  
Yinghui Gao ◽  
Ruigang Fu
Keyword(s):  
Information Geometry ◽  
Point Set Registration ◽  
Registration Algorithm ◽  
Point Set
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