Real-time intensity-based rigid 2d-3d medical image registration using RapidMind Multi-core Development Platform

2008 ◽  
Author(s):  
Lin Xu ◽  
Justin W.L. Wan
Keyword(s):  
Image Registration ◽  
Real Time ◽  
Medical Image ◽  
Medical Image Registration ◽  
Development Platform

3D Medical Images Registration Based on GPU Parallel Computing

2012 ◽  
Vol 241-244 ◽  
pp. 3010-3013 ◽  
Author(s):  
Lu Meng
Keyword(s):  
Parallel Computing ◽  
Image Registration ◽  
Real Time ◽  
Clinical Application ◽  
Medical Image ◽  
Three Dimensional ◽  
Medical Image Registration ◽  
Time Requirement ◽  
The Real ◽  
Gpu Parallel Computing

Real time 3D medical image registration method is key technology of medical image processing, especially in surgical operation navigation. However, current 3D medical image registration methods are time-consuming, which can’t meet the real time requirement of clinical application. To solve this problem, this paper presented a high performance computational method based on CUDA ( Compute Unified Device Architecture), which took full advantage of GPU parallel computing under CUDA architecture combined with image multiple scale and maximum mutual information to make fast registration of three dimensional medical image. Experiments showed that this algorithm can greatly accelerate the computational speed of registration of three dimensional medical image, and meet the real time requirement of clinical application.

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

Medical image registration using sparse coding of image patches

2016 ◽  
Vol 73 ◽  
pp. 56-70 ◽  
Author(s):  
Maryam Afzali ◽  
Aboozar Ghaffari ◽  
Emad Fatemizadeh ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Image Registration ◽  
Sparse Coding ◽  
Medical Image ◽  
Medical Image Registration ◽  
Image Patches
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