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  3. Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model
 

Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model

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BORIS DOI
10.7892/boris.76881
Publisher DOI
10.1007/978-3-319-24888-2_13
Description
This paper presents a non-rigid free-from 2D-3D registration approach using statistical deformation model (SDM). In our approach the SDM is first constructed from a set of training data using a non-rigid registration algorithm based on b-spline free-form deformation to encode a priori information about the underlying anatomy. A novel intensity-based non-rigid 2D-3D registration algorithm is then presented to iteratively fit the 3D b-spline-based SDM to the 2D X-ray images of an unseen subject, which requires a computationally expensive inversion of the instantiated deformation in each iteration. In this paper, we propose to solve this challenge with a fast B-spline pseudo-inversion algorithm that is implemented on graphics processing unit (GPU). Experiments conducted on C-arm and X-ray images of cadaveric femurs demonstrate the efficacy of the present approach.
Date of Publication
2015
Publication Type
Book Section
Subject(s)
500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
Language(s)
en
Contributor(s)
Zheng, Guoyanorcid-logo
Institut für chirurgische Technologien und Biomechanik (ISTB)
Yu, Weimin
Institut für chirurgische Technologien und Biomechanik (ISTB)
Editor(s)
Zhou, Luping
Wang, Li
Wang, Qian
Shi, Yinghuan
Additional Credits
Institut für chirurgische Technologien und Biomechanik (ISTB)
Publisher
Springer
ISBN
978-3-319-24887-5
Book Title
Machine Learning in Medical Imaging. 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015, Proceedings
Access(Rights)
restricted
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