Paper Info
Title
Enhancing accuracy of symmetric random walker image registration via a novel data-consistency measure
Abstract
Random walker image registration has recently been extended in [1] to enforce inverse consistency in the solution by registering the input images towards a common space that resides midway between the two images. In this paper, we propose a novel extension to [1] to further improve its accuracy. We do so by proposing a voxel selection criterion that examines consistency of the data-likelihood estimates computed between the forward and backward directions. In particular, poor agreement occurs at locations where the top candidate displacement labels preferred by the forward direction conflict with those preferred by the backward direction. Once data-consistency is measured at every voxel location, the data-likelihood estimates of locations with low data-consistency are adjusted so that these nodes will contribute minimally to the similarity calculation. Experiments using different image modalities and image similarity measures show that this scheme can improve registration accuracy significantly per statistical analyses.
Year
DOI
Venue
2016
10.1109/ISBI.2016.7493203
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
Field
DocType
ISSN
Voxel,Computer vision,Inverse,Pattern recognition,Computer science,Selection criterion,Artificial intelligence,Random walker algorithm,Image registration,Data consistency
Conference
1945-7928
Citations 
PageRank 
References 
0
0.34
11
Authors
3
Name
Order
Citations
PageRank
Lisa Y. W. Tang11107.05
Roger C. Tam224416.61
Ghassan Hamarneh31353110.14