Adaptive Mesh Generation of MRI Images for 3D Reconstruction of Human Trunk

Courchesne, Olivier; Guibault, François; Dompierre, Julien; Cheriet, Foued

doi:10.1007/978-3-540-74260-9_92

Cited by 7 publications

(11 citation statements)

References 8 publications

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“…Furthermore, these commercial software are typically shipped in the form of stand-alone packages, and it is difficult to incorporate and automate in a image-based modeling work-flow. A number of publications have reported algorithms or utilities for similar purposes [2,3], however, these software are either not accessible or not scalable for general use.…”

Section: Introductionmentioning

confidence: 99%

Tetrahedral mesh generation from volumetric binary and grayscale images

Fang

Boas

2009

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

315

135

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We report a general purpose mesh generator for creating finite-element surface or volumetric mesh from 3D binary or gray-scale medical images. This toolbox incorporates a number of existing free mesh processing utilities and enables researchers to perform a range of mesh processing tasks for image-based mesh generation, including raw image processing, surface mesh extraction, surface re-sampling, and multi-scale/adaptive tetrahedral mesh generation. We also implemented robust algorithms for meshing opensurfaces and sub-region labeling. Atomic meshing utilities for each processing step can be accessed with simple interfaces, which can be streamlined or executed independently. The toolbox is compatible with Matlab or GNU Octave. We demonstrate the applications of this toolbox for meshing a range of challenging geometries including complex vessel network, human brain and breast.

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Section: Introductionmentioning

confidence: 99%

Tetrahedral mesh generation from volumetric binary and grayscale images

Fang

Boas

2009

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

315

135

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“…Triangular meshes have recently received considerable interest in adaptive sampling for image representation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. One common approach is to find proper sample points then connect the points to form a mesh.…”

Section: Introductionmentioning

confidence: 99%

“…Isotropic triangles are used in their adaptive meshes. Courchesne et al [13] use the Hessian matrix based on the gray level of MRI images as a metric tensor to adapt the triangular mesh for 3D reconstruction of human trunk. The Hessian matrix is reconstructed by linear or quadratic fitting.…”

Section: Introductionmentioning

confidence: 99%

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Anisotropic mesh adaptation for image representation

2016

J Image Video Proc.

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Triangular meshes have gained much interest in image representation and have been widely used in image processing. This paper introduces a framework of anisotropic mesh adaptation (AMA) methods to image representation and proposes a GPRAMA method that is based on AMA and greedy-point removal (GPR) scheme. Different than many other methods that triangulate sample points to form the mesh, the AMA methods start directly with a triangular mesh and then adapt the mesh based on a user-defined metric tensor to represent the image. The AMA methods have clear mathematical framework and provide flexibility for both image representation and image reconstruction. A mesh patching technique is developed for the implementation of the GPRAMA method, which leads to an improved version of the popular GPRFS-ED method. The GPRAMA method can achieve better quality than the GPRFS-ED method but with lower computational cost.

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“…This representation will be a patient specific high quality 3D geometric model, based on tetrahedral elements and using MR data. A preliminary study of the proposed method, performed only on 2D images, has been presented in [33] [34]. With the goal of constructing meshes targeted toward the simulation of physical deformations of the trunk, we propose to use 3D MR acquisition sequences for the reconstruction of the discrete 3D trunk model.…”

Section: Introductionmentioning

confidence: 99%

Patient‐specific anisotropic model of human trunk based on MR data

Courchesne

Guibault

Parent

et al. 2015

Numer Methods Biomed Eng

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SUMMARYThere are many ways to generate geometrical models for numerical simulation and most of them start with a segmentation step to extract the boundaries of the regions of interest. This paper presents an algorithm to generate a patient specific 3D geometric model, based on a tetrahedral mesh, without an initial extraction of contours from the volumetric data. Using the information directly available in the data, such as grey levels, a metric is built to drive a mesh adaptation process. The metric is used to specify the size and orientation of the tetrahedral elements everywhere in the mesh. Our method, which produces anisotropic meshes, gives good results with synthetic and real MRI data. The resulting model quality has been evaluated qualitatively and quantitatively by comparing it with an analytical solution and with a segmentation made by an expert. Results show that our method gives, in 90% of the cases, as good or better meshes as a similar isotropic method, based on the accuracy of the volume reconstruction for a given mesh size. Moreover, a comparison of Hausdorff distances between adapted meshes of both methods and ground-truth volumes shows that our method decreases reconstruction errors faster.

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Adaptive Mesh Generation of MRI Images for 3D Reconstruction of Human Trunk

Cited by 7 publications

References 8 publications

Tetrahedral mesh generation from volumetric binary and grayscale images

Tetrahedral mesh generation from volumetric binary and grayscale images

Anisotropic mesh adaptation for image representation

Patient‐specific anisotropic model of human trunk based on MR data

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