SIGN-IN

Publication: High Performance Medical Image Registration Using New Optimization Techniques

All || By Area || By Year

Title High Performance Medical Image Registration Using New Optimization Techniques
Authors/Editors* M. P. Wachowiak, T. M. Peters
Where published* IEEE Transactions on Information Technology in Biomedicine
How published* Journal
Year* 2006
Volume 10
Number 2
Pages 344-353
Publisher
Keywords DIviding RECTangles (DIRECT), medical image registration, multidirectional search (MDS), optimization, parallel computing
Link
Abstract
Optimization of a similarity metric is an essential component in intensity-based medical image registration. The increasing availability of parallel computers makes parallelizing some registration tasks an attractive option to increase speed. In this paper, two new deterministic, derivative-free, and intrinsically parallel optimization methods are adapted for image registration. DIviding RECTangles (DIRECT) is a global technique for linearly bounded problems, and multidirectional search (MDS) is a recent local method. The performance of DIRECT, MDS, and hybrid methods using a parallel implementation of Powell’s method for local refinement, are compared. Experimental results demonstrate that DIRECT and MDS are robust, accurate, and substantially reduce computation time in parallel implementations.
Go to Data science, machine learning, geometric deep learning, graph signal processing, biomedical data exploration
Back to page 73 of list