Paper Info
Title
Comparison of optimization strategy and similarity metric in atlas-to-subject registration using statistical deformation model
Abstract
A robust atlas-to-subject registration using a statistical deformation model (SDM) is presented. The SDM uses statistics of voxel-wise displacement learned from pre-computed deformation vectors of a training dataset This allows an atlas instance to be directly translated into an intensity volume and compared with a patient's intensity volume. Rigid and nonrigid transformation parameters were simultaneously optimized via the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), with image similarity used as the objective function. The algorithm was tested on CT volumes of the pelvis from 55 female subjects. A performance comparison of the CMA-ES and Nelder-Mead downhill simplex optimization algorithms with the mutual information and normalized cross correlation similarity metrics was conducted. Simulation studies using synthetic subjects were performed, as well as leave-one-out cross validation studies. Both studies suggested that mutual information and CMA-ES achieved the best performance. The leave-one-out test demonstrated 4.13 mm error with respect to the true displacement field, and 26,102 function evaluations in 180 seconds, on average.
Year
DOI
Venue
2015
10.1117/12.2081754
Proceedings of SPIE
Keywords
Field
DocType
Statistical deformation model,atlas-to-subject registration,evolutionary optimization
Cross-correlation,Computer vision,Displacement field,Simplex algorithm,Computer science,Matrix (mathematics),Evolution strategy,Mutual information,CMA-ES,Artificial intelligence,Cross-validation
Conference
Volume
ISSN
Citations 
9415
0277-786X
0
PageRank 
References 
Authors
0.34
9
6
Name
Order
Citations
PageRank
Yoshito Otake114428.20
Ryan J. Murphy27411.63
r b grupp333.47
yoichi sato400.34
Russell H. Taylor51970438.00
M Armand616927.17