Initialized Iterative Closest Point for bone recognition in ultrasound volumes

Abstract : Ultrasound (US) probes have been used as guiding tools for Computer Assisted Orthopedic Surgeries (CAOS) [1]. Because of the US data uncertainty, the process of recognition-the localization of regions of interest in the image-requires a registration to a more precise, but invasive, imaging modality such as Computed Tomography (CT). A millimetric precision and a real-time processing are intraoperative requirements. Iterative Closest Point (ICP) [2] is a simple and non symmetric rigid registration algorithm that is sensitive to the initial position of the point sets. The aim of this study is to show the contribution of initializing ICP in rigid US-CT registration and to illustrate it on data of a proximal femur. First, an iterative initialization of the model (CT) to the partial view (US) is performed using ICP with annealed filtering. The first obtained local minimum is then used to initialize a refinement step that maps the partial view to the model. One femur phantom was imaged both in a water bath using a calibrated 3D ultrasound probe and by CT. For each of the ten US acquisitions (five in the Anterior neck A, and five in the Posterior neck P), the CT scan is brought by means of fiducials pair-point matching. The initialization step improves ICP successful registrations from (A:25%, P:21%) to (A:76%, to P:52%) and the registration takes about 3s in average whilst ICP takes about 1s.
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International Conference on Pattern Recognition, Dec 2016, Cancun, Mexico. Proceedings of ICPR'2016
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Oussama Haddad, Julien Leboucher, Jocelyne Troccaz, Eric Stindel. Initialized Iterative Closest Point for bone recognition in ultrasound volumes. International Conference on Pattern Recognition, Dec 2016, Cancun, Mexico. Proceedings of ICPR'2016. 〈hal-01455683〉

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