A New Divergence Measure Based on Arimoto Entropy for Medical Image Registration - Université de Rennes Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

A New Divergence Measure Based on Arimoto Entropy for Medical Image Registration

Résumé

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
Fichier non déposé

Dates et versions

hal-01260609 , version 1 (22-01-2016)

Identifiants

Citer

Bicao Li, Guanyu Yang, Huazhong Shu, J.L Coatrieux. A New Divergence Measure Based on Arimoto Entropy for Medical Image Registration. 2014 22nd International Conference on Pattern Recognition (ICPR), 2014, Stockholm Sweden. pp.3197--3202, ⟨10.1109/ICPR.2014.551⟩. ⟨hal-01260609⟩
62 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More