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Globally optimal deformable registration on a minimum spanning tree using dense displacement sampling.

TitleGlobally optimal deformable registration on a minimum spanning tree using dense displacement sampling.
Publication TypeJournal Article
Year of Publication2012
AuthorsHeinrich M.P., Jenkinson M., Jenkinson M., Schnabel J.A.
JournalMedical image computing and computer-assisted intervention : MICCAI 2012 International Conference on Medical Image Computing and Computer-Assisted Intervention
IssuePt 3
Date Published2012
Publication Languageeng
KeywordsAlgorithms, Humans, Imaging, Three-Dimensional, Lung, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Radiography, Thoracic, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, Tomography, X-Ray Computed

Deformable image registration poses a highly non-convex optimisation problem. Conventionally, medical image registration techniques rely on continuous optimisation, which is prone to local minima. Recent advances in the mathematics and new programming methods enable these disadvantages to be overcome using discrete optimisation. In this paper, we present a new technique deeds, which employs a discrete dense displacement sampling for the deformable registration of high resolution CT volumes. The image grid is represented as a minimum spanning tree. Given these constraints a global optimum of the cost function can be found efficiently using dynamic programming, which enforces the smoothness of the deformations. Experimental results demonstrate the advantages of deeds: the registration error for the challenging registration of inhale and exhale pulmonary CT scans is significantly lower than for two state-of-the-art registration techniques, especially in the presence of large deformations and sliding motion at lung surfaces.

PubMed Link


Alternate JournalMed Image Comput Comput Assist Interv
Created at October 26, 2015 - 10:24am.


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