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Entropy 2019, 21(2), 189; https://doi.org/10.3390/e21020189

Nonrigid Medical Image Registration Using an Information Theoretic Measure Based on Arimoto Entropy with Gradient Distributions

1
School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
2
Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
3
College of Information Engineering, Capital Normal University, Beijing 100048, China
4
School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
*
Author to whom correspondence should be addressed.
Received: 12 December 2018 / Revised: 2 February 2019 / Accepted: 14 February 2019 / Published: 18 February 2019
(This article belongs to the Special Issue Entropy in Image Analysis)
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Abstract

This paper introduces a new nonrigid registration approach for medical images applying an information theoretic measure based on Arimoto entropy with gradient distributions. A normalized dissimilarity measure based on Arimoto entropy is presented, which is employed to measure the independence between two images. In addition, a regularization term is integrated into the cost function to obtain the smooth elastic deformation. To take the spatial information between voxels into account, the distance of gradient distributions is constructed. The goal of nonrigid alignment is to find the optimal solution of a cost function including a dissimilarity measure, a regularization term, and a distance term between the gradient distributions of two images to be registered, which would achieve a minimum value when two misaligned images are perfectly registered using limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization scheme. To evaluate the test results of our presented algorithm in non-rigid medical image registration, experiments on simulated three-dimension (3D) brain magnetic resonance imaging (MR) images, real 3D thoracic computed tomography (CT) volumes and 3D cardiac CT volumes were carried out on elastix package. Comparison studies including mutual information (MI) and the approach without considering spatial information were conducted. These results demonstrate a slight improvement in accuracy of non-rigid registration. View Full-Text
Keywords: Arimoto entropy; free-form deformations; normalized divergence measure; gradient distributions; nonextensive entropy; non-rigid registration Arimoto entropy; free-form deformations; normalized divergence measure; gradient distributions; nonextensive entropy; non-rigid registration
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, B.; Shu, H.; Liu, Z.; Shao, Z.; Li, C.; Huang, M.; Huang, J. Nonrigid Medical Image Registration Using an Information Theoretic Measure Based on Arimoto Entropy with Gradient Distributions. Entropy 2019, 21, 189.

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