title: Hierarchical multimodal image registration based on adaptive local mutual information.
abstract: We propose a new, adaptive local measure based on gradient orientation similarity for the purposes of multimodal image registration. We embed this metric into a hierarchical registration framework, where we show that registration robustness and accuracy can be improved by adapting both the similarity metric and the pixel selection strategy to the Gaussian blurring scale and to the modalities being registered. A computationally efficient estimation of gradient orientations is proposed based on patch-wise rigidity. We have applied our method to both rigid and non-rigid multimodal registration tasks with different modalities. Our approach outperforms mutual information (MI) and previously proposed local approximations of MI for multimodal (e.g. CT/MRI) brain image registration tasks. Furthermore, it shows significant improvements in terms of mTRE over standard methods in the highly challenging clinical context of registering pre-operative brain MRI to intra-operative US images.
authors.affiliation: McGill University, Centre for Intelligent Machines. dante@cim.mcgill.ca
authors.forename: Dante
authors.initials: D
authors.lastname: De Nigris
authors.forename: Laurence
authors.initials: L
authors.lastname: Mercier
authors.forename: Rolando
authors.initials: R
authors.lastname: Del Maestro
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
authors.forename: Tal
authors.initials: T
authors.lastname: Arbel
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D004453:Echoencephalography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique