title: A Robust and Accurate Two-Step Auto-Labeling Conditional Iterative Closest Points (TACICP) Algorithm for Three-Dimensional Multi-Modal Carotid Image Registration.
abstract: Atherosclerosis is among the leading causes of death and disability. Combining information from multi-modal vascular images is an effective and efficient way to diagnose and monitor atherosclerosis, in which image registration is a key technique. In this paper a feature-based registration algorithm, Two-step Auto-labeling Conditional Iterative Closed Points (TACICP) algorithm, is proposed to align three-dimensional carotid image datasets from ultrasound (US) and magnetic resonance (MR). Based on 2D segmented contours, a coarse-to-fine strategy is employed with two steps: rigid initialization step and non-rigid refinement step. Conditional Iterative Closest Points (CICP) algorithm is given in rigid initialization step to obtain the robust rigid transformation and label configurations. Then the labels and CICP algorithm with non-rigid thin-plate-spline (TPS) transformation model is introduced to solve non-rigid carotid deformation between different body positions. The results demonstrate that proposed TACICP algorithm has achieved an average registration error of less than 0.2mm with no failure case, which is superior to the state-of-the-art feature-based methods.
authors.affiliation: Research Institute of Image and Information, Department of Electrical Engineering, Tsinghua University, Beijing, China.
authors.forename: Hengkai
authors.initials: H
authors.lastname: Guo
authors.affiliation: Research Institute of Image and Information, Department of Electrical Engineering, Tsinghua University, Beijing, China.
authors.forename: Guijin
authors.initials: G
authors.lastname: Wang
authors.affiliation: Healthcare Department, Philips Research China, Shanghai, China.
authors.forename: Lingyun
authors.initials: L
authors.lastname: Huang
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China.
authors.forename: Yuxin
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China.|Department of Radiology, University of Washington, 850 Republican St, Seattle, WA, United States of America.
authors.forename: Chun
authors.initials: C
authors.lastname: Yuan
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Li
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China.
authors.forename: Xihai
authors.initials: X
authors.lastname: Zhao
mesh_terms: D000368:Aged; D000465:Algorithms; D050197:Atherosclerosis / Q000000981:diagnostic imaging* / Q000473:pathology; D002339:Carotid Arteries / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011859:Radiography; D014463:Ultrasonography / Q000379:methods