Browsing by Author "Tamayo, OA"

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    Hybrid non-rigid registration method for registering rat skeletons from micro-CT images
    (Society of Nuclear Medicine and Molecular Imaging, 2009-05) Xiao, D; Zahra, D; Bourgeat, P; Tamayo, OA; Berghofer, PJ; Fripp, J; Grégoire, MC; Salvado, O
    Objectives: A hybrid non-rigid registration method for automatic rat spine detection and registration and further rat limbs registration was proposed. Methods: We first developed an automatic algorithm to extract the rat spine from its whole-body skeleton and then detect top point on the spinous process of each vertebra starting from the first vertebra at thoracic vertebrae towards the tail. The extracted points were matched to the predefined corresponding points of the spine in a rat atlas. Their correspondences field was used to perform skeletons registration by thin-plate-spline (TPS). Further, we extend 3D shape context algorithm for landmarks matching for rat limbs between the atlas and the newly extracted skeletons. The correspondences found were used to perform the rat limb skeletons registration by TPS. Results: Experiments were described using phantoms and actual rat skeletons. Mean square errors decrease was observed during registration process. Visually, the skeletons were successfully registered. Conclusions: The method can improve the robustness of rat skeleton registration, even in the case of large variation in some postures and this first step work can be extended to further improve rat organs registration guided by the correspondences found from the skeletons.© 2009 by Society of Nuclear Medicine
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    An improved 3D shape context based non-rigid registration method and its application to small animal skeletons registration
    (Pergamon-Elsevier Science Ltd, 2010-06-01) Xiao, D; Zahra, D; Bourgeat, P; Berghofer, PJ; Tamayo, OA; Wimberley, CA; Grégoire, MC; Salvado, O
    3D shape context is a method to define matching points between similar shapes. It can be used as a preprocessing step in a non-rigid registration. The main limitation of the method is point mismatching, which includes long geodesic distance mismatch causing wrong topological structure, and neighbors crossing mismatch between two adjacent points. In this paper, we propose a topological structure verification method to correct the long geodesic distance mismatch and a correspondence field smoothing method to correct the neighbors crossing mismatch. A robust 3D shape context model is generated and further combined with thin-plate spline model for non-rigid registration. The method was tested on phantoms and applied to rat hind limb and mouse hind limb skeletons registration from micro-CT images. Errors between the registered surfaces were reduced by using the proposed method. The robustness of the method is demonstrated. © 2010, Elsevier Ltd.