title: Automatic learning sparse correspondences for initialising groupwise registration.
abstract: We seek to automatically establish dense correspondences across groups of images. Existing non-rigid registration methods usually involve local optimisation and thus require accurate initialisation. It is difficult to obtain such initialisation for images of complex structures, especially those with many self-similar parts. In this paper we show that satisfactory initialisation for such images can be found by a parts+geometry model. We use a population based optimisation strategy to select the best parts from a large pool of candidates. The best matches of the optimal model are used to initialise a groupwise registration algorithm, leading to dense, accurate results. We demonstrate the efficacy of the approach on two challenging datasets, and report on a detailed quantitative evaluation of its performance.
authors.affiliation: Imaging Science and Biomedical Engineering, The University of Manchester, UK. Pei.Zhang-2@postgrad.manchester.ac.uk
authors.forename: Pei
authors.initials: P
authors.lastname: Zhang
authors.forename: Steve A
authors.initials: SA
authors.lastname: Adeshina
authors.forename: Timothy F
authors.initials: TF
authors.lastname: Cootes
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique