title: Multiscale deformable registration of noisy medical images.
abstract: Multiscale image registration techniques are presented for the registration of medical images using deformable registration models. The techniques are particularly effective for registration problems in which one or both of the images to be registered contains significant levels of noise. A brief overview of existing deformable registration techniques is presented, and experiments using B-spline free-form deformation registration models demonstrate that ordinary deformable registration techniques fail to produce accurate results in the presence of significant levels of noise. The hierarchical multiscale image decomposition described in E. Tadmor, S. Nezzar, and L. Vese's, "A multiscale image representation using hierarchical (BV;L2) decompositions" (Multiscale Modeling and Simulations, 2 (2004): 4, pp. 554-579) is reviewed, and multiscale image registration algorithms are developed based on the multiscale decomposition. Accurate registration of noisy images is achieved by obtaining a hierarchical multiscale decomposition of the images and iteratively registering the resulting components. This approach enables a successful regstration of images that contain noise levels well beyond the level at which ordinary deformable registration fails. Numerous image registration experiments demonstrate the accuracy and efficiency of the multiscale registration techniques.
authors.affiliation: Department of Mathematics, Kenyon College, Gambier, OH 43022-9623, USA. paquind@kenyon.edu
authors.forename: Dana
authors.initials: D
authors.lastname: Paquin
authors.forename: Doron
authors.initials: D
authors.lastname: Levy
authors.forename: Lei
authors.initials: L
authors.lastname: Xing
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique