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Conference papers
A New Divergence Measure Based on Arimoto Entropy for Medical Image Registration
Abstract : A new divergence measure for rigid image registration is proposed that uses the properties of the Arimoto entropy. This Jensen-Arimoto divergence allows designing a novel registration method by minimizing a dissimilarity measure through the steepest gradient descent optimization method. Preliminary experiments on simulated magnetic resonance images with partial overlap and different degrees of noise have been carried out and a comparison has been conducted with other relevant information theoretic measures such as the normalized mutual information and the cross cumulative residual entropy. The results show that the proposed registration approach has better robustness to noise and can provide better registration accuracy, i.e. a sub pixel accuracy less than 0.1mm and 0.1 degree for translation and rotation. In addition, the calculation time for a 2D rigid registration is improved by approximately 10-20 % compared to the other two methods
Keywords :
2D rigid registration
arimoto entropy
cross cumulative residual entropy
divergence measure
Entropy
gradient methods
image registration
Jensen-Arimoto divergence
magnetic resonance images
medical image processing
medical image registration
Mutual information
Noise
normalized mutual information
optimisation
Optimization
partial overlap
Probability distribution
Random variables
steepest gradient descent optimization method
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01260609
Contributor : Laurent Jonchère <>
Submitted on : Friday, January 22, 2016 - 1:56:11 PM
Last modification on : Friday, July 5, 2019 - 10:16:02 AM
Contributor : Laurent Jonchère <>
Submitted on : Friday, January 22, 2016 - 1:56:11 PM
Last modification on : Friday, July 5, 2019 - 10:16:02 AM