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Augmented Statistical Shape Modeling for Orthopedic Surgery and Rehabilitation
Published in de Azevedo-Marques Paulo Mazzoncini, Mencattini Arianna, Salmeri Marcello, Rangayyan Rangaraj M., Medical Image Analysis and Informatics: Computer-Aided Diagnosis and Therapy, 2018
Bhushan Borotikar, Tinashe Mutsvangwa, Valérie Burdin, Enjie Ghorbel, Mathieu Lempereur, Sylvain Brochard, Eric Stindel, Christian Roux
The CPD software toolbox was developed by Myronenko [50]. The MATLAB-based toolbox is for rigid, affine and non-rigid point set registration and matching and allows aligning two N-D point sets and recovering the correspondences. It is freely available for academic use. Several parameters have to be set depending on the size of the dataset. Recommendations are provided in the help file on how to choose the appropriate parameters. The regularization parameter λ and another value β (explained below), however, have to be found empirically. The parameters in the help file are listed below in Table 17.2 with the recommended values and those used by the authors together with the justification of the choices.
3D coronary artery elastic registration based on differential invariant signatures
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
In the field of computer vision and medical imaging, point set registration (or point matching) is one of the most important research issues, which serves as a significant step in registering datasets into a more accurate model, and determines a spatial transformation to align two point sets. Point set registration is now applied in many aspects such as medical image analysis (Rasoulian et al. 2012; Wu et al. 2014; Fang et al. 2019; Zhu et al. 2020), shape matching (Lindner et al. 2014), character recognition (Zhang et al. 2016; Xu et al. 2017), and feature detection, etc. Hierarchical attribute matching mechanism for elastic registration (HAMMER) was introduced in Shen and Davatzikos (2002) which used geometric moment invariants (GMIs) to establish anatomical correspondences in the deformation and was successfully applied to magnetic resonance images of the brain. Belongie et al. (2002) created shape context as a shape descriptor, and then object recognition can be achieved by matching this feature with a priori knowledge of the shape context of the boundary points of the object. Fitzgibbon (2003) introduced a method of registering point sets using general-purpose non-linear optimisation to minimise registration error directly. 2D/3D registration method is established by a point-of-interest tracking network, then the 3D pose of pre-intervention data is estimated through a triangulation layer (Liao et al. 2019).
Point set registration for reduced geometry mismatch during estimation of mass transfer properties in osmotic dehydration of complex-shaped foods
Published in Drying Technology, 2020
K. H. Estévez-Sánchez, J. E. González-Pérez, C. E. Ochoa-Velasco, M. A. García-Alvarado, D. Cruz-González, A. Sampieri, I. I. Ruiz-López
Point set registration (PSR) or point matching is the process of computing a spatial transformation that optimally aligns two pairs of point sets.[20,21] The PSR method serves as an important step to merge multiple datasets into a more accurate (reference) model and has several applications such as optical character recognition, pose estimation, medical image analysis (align data from magnetic resonance and computed tomography images), shape modeling, and feature detection.[21] The objective of this study is to evaluate PSR as a method to reduce geometry mismatch during the osmotic dehydration of complex-shaped foods such as white mushroom pilei. To fully achieve this purpose, the following topics are covered: (i) the introduction of a novel algorithm to use PSR and oblate spheroidal coordinate system to extract optimum shape descriptors of product, (ii) analyze the PSR data to create a representative image of product for mass transfer modeling and description of shrinkage data, (iii) estimate diffusion coefficients with the PSR-generated geometry, (iv) explore roundness as a shape index to quantify geometry mismatch between the real and model shapes, and (v) develop and validate a simple method based on the straight-line fit approach to estimate diffusion coefficients corrected for shrinkage in the proposed geometry.