title: Initialising groupwise non-rigid registration using multiple parts+geometry models.
abstract: Groupwise non-rigid registration is an important technique in medical image analysis. Recent studies show that its accuracy can be greatly improved by explicitly providing good initialisation. This is achieved by seeking a sparse correspondence using a parts+geometry model. In this paper we show that a single parts+geometry model is unlikely to establish consistent sparse correspondence for complex objects, and that better initialisation can be achieved using a set of models. We describe how to combine the strengths of multiple models, and demonstrate that the method gives state-of-the-art performance on three datasets, with the most significant improvement on the most challenging.
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, USA. peizhang@email.unc.edu
authors.forename: Pei
authors.initials: P
authors.lastname: Zhang
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.forename: Timothy F
authors.initials: TF
authors.lastname: Cootes
mesh_terms: D000465:Algorithms; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique