title: Convex hull matching and hierarchical decomposition for multimodality medical image registration.
abstract: This study proposes a novel hierarchical pyramid strategy for 3D registration of multimodality medical images. The surfaces of the source and target volume data are first extracted, and the surface point clouds are then aligned roughly using convex hull matching. The convex hull matching registration procedure could align images with large-scale transformations. The original images are divided into blocks and the corresponding blocks in the two images are registered by affine and non-rigid registration procedures. The sub-blocks are iteratively smoothed by the Gaussian kernel with different sizes during the registration procedure. The registration result of the large kernel is taken as the input of the small kernel registration. The fine registration of the two volume data sets is achieved by iteratively increasing the number of blocks, in which increase in similarity measure is taken as a criterion for acceptation of each iteration level. Results demonstrate the effectiveness and robustness of the proposed method in registering the multiple modalities of medical images.
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China.
authors.forename: Jingfan
authors.initials: J
authors.lastname: Fan
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing, China.
authors.forename: Tianyu
authors.initials: T
authors.lastname: Fu
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China.
authors.forename: Danni
authors.initials: D
authors.lastname: Ai
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China.
authors.forename: Jianjun
authors.initials: J
authors.lastname: Zhu
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing, China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Li
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China.
authors.forename: Yongtian
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging; D064847:Multimodal Imaging / Q000379:methods*; D014057:Tomography, X-Ray Computed
keywords: Convex hull; multimodality image; registration