DIRBoost : an algorithm for boosting deformable image registration

S.E.A. Muenzing, B. Ginneken, van, J.P.W. Pluim

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)


We introduce a novel boosting algorithm to boost - i.e. improve on - existing methods for deformable image registration. The proposed DIRBoost algorithm is inspired by the theory on hypothesis boosting, well-known in the field of machine learning. DIRBoost involves a classifier for landmark-based Registration Error Detection (RED). Based on these RED predictions a Voronoi tessellation is generated to obtain a dense estimate of local image registration quality. All areas predicted as erroneous registration are subjected to boosting, i.e. undergo iterative registrations by employing boosting masks on both the fixed and moving image. We evaluated the DIRBoost algorithm on five CT pulmonary breathhold inspiration and expiration scan pairs, employing the NiftyReg registration algorithm. DIRBoost could boost about 50% of the wrongly registered areas which in turn also improved the average landmark registration error by 24%. © 2012 IEEE.
Original languageEnglish
Title of host publication9th IEEE International Symposium on Biomedical Imaging : from Nano to Macro (ISBI 2012), May 2-5 2012, Barcelona, Spain
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4577-1858-8
Publication statusPublished - 2012


Dive into the research topics of 'DIRBoost : an algorithm for boosting deformable image registration'. Together they form a unique fingerprint.

Cite this