Computing the Medical Image Registration Using Meta-Heuristics

2014 ◽  
Vol 643 ◽  
pp. 237-242 ◽  
Author(s):  
Tahari Abdou El Karim ◽  
Bendakmousse Abdeslam ◽  
Ait Aoudia Samy
Keyword(s):  
Image Processing ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Image Registration ◽  
Cost Function ◽  
Population Genetic ◽  
Medical Image ◽  
Medical Image Registration ◽  
Anatomical Structures

The image registration is a very important task in image processing. In the field of medical imaging, it is used to compare the anatomical structures of two or more images taken at different time to track for example the evolution of a disease. Intensity-based techniques are widely used in the multi-modal registration. To have the best registration, a cost function expressing the similarity between these images is maximized. The registration problem is reduced to the optimization of a cost function. We propose to use neighborhood meta-heuristics (tabu search, simulated annealing) and a meta-heuristic population (genetic algorithms). An evaluation step is necessary to estimate the quality of registration obtained. In this paper we present some results of medical image registration

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

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 189 ◽  
Author(s):  
Bicao Li ◽  
Huazhong Shu ◽  
Zhoufeng Liu ◽  
Zhuhong Shao ◽  
Chunlei Li ◽  
...  
Keyword(s):  
Image Registration ◽  
Cost Function ◽  
Medical Image ◽  
Spatial Information ◽  
Brain Magnetic Resonance Imaging ◽  
Dissimilarity Measure ◽  
Medical Image Registration ◽  
Three Dimension ◽  
Information Theoretic ◽  
Regularization Term

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.

scholarly journals A Tutorial on Combining Nonlinear Optimization with CUDA

2015 ◽  
Author(s):  
Charles Hatt
Keyword(s):  
Image Registration ◽  
Cost Function ◽  
Nonlinear Optimization ◽  
Medical Image ◽  
Gpu Computing ◽  
Optimization Problems ◽  
Parallel Implementation ◽  
Medical Image Registration ◽  
Computing Framework

Nonlinear optimization is a key component of many image registration algorithms. Improving registration speed is almost always desirable. One way to do this is to accelerate the optimization cost function using a parallel implementation. The purpose of this document is to provide a tutorial on how to combine the CUDA GPU computing framework with standard nonlinear optimization libraries (VNL) using CMake. The provided code can be used as a starting template for programmers looking for a relatively painless introduction to CUDA-accelerated medical image registration and other nonlinear optimization problems.

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