Comparison of myocardial motion estimation methods based on simulated echocardiographic B-mode and RF data

2010 ◽  
Author(s):  
Vahid Tavakoli ◽  
Jamie Kemp ◽  
Buddha Dawn ◽  
Marcus Stoddard ◽  
Amir A. Amini
Keyword(s):  
Motion Estimation ◽  
Estimation Methods ◽  
Myocardial Motion

scholarly journals Myocardial Motion Estimation Combining Tissue Doppler and B-mode Echocardiographic Images

2013 ◽  
pp. 484-491 ◽  
Author(s):  
Antonio R. Porras ◽  
Mathieu De Craene ◽  
Nicolas Duchateau ◽  
Marta Sitges ◽  
Bart H. Bijnens ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Tissue Doppler ◽  
Myocardial Motion ◽  
Echocardiographic Images

scholarly journals Single-Dimension Perturbation Glowworm Swarm Optimization Algorithm for Block Motion Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiangpin Liu ◽  
Shibin Xuan ◽  
Feng Liu
Keyword(s):  
Motion Estimation ◽  
Optimization Algorithm ◽  
Estimation Method ◽  
Estimation Methods ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Single Dimension ◽  
Glowworm Swarm Optimization ◽  
Block Motion ◽  
Block Motion Estimation

In view of the fact that the classical fast motion estimation methods are easy to fall into local optimum and suffer the high computational cost, the convergence of the motion estimation method based on the swarm intelligence algorithm is very slow. A new block motion estimation method based on single-dimension perturbation glowworm swarm optimization algorithm is proposed. Single-dimension perturbation is a local search strategy which can improve the ability of local optimization. The proposed method not only has overcome the defect of falling into local optimum easily by taking use of both the global search ability of glowworm swarm optimization algorithm and the local optimization ability of single-dimension perturbation but also has reduced the computation complexity by using motion vector predictor and terminating strategies in view of the characteristic of video images. The experimental results show that the performance of the proposed method is better than that of other motion estimation methods for most video sequences, specifically for those video sequences with violent motion, and the searching precision has been improved obviously. Although the computational complexity of the proposed method is slightly higher than that of the classical methods, it is still far lower than that of full search method.

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