Tetrahedral mesh segmentation based on quality criteria

被引:0
|
作者
Neves, Leandro Alves [1 ]
Pavarino, Eduardo [1 ]
Cintra, Andre Felipe [1 ]
Zafalon, Geraldo Francisco D. [1 ]
do Nascimento, Marcelo Zanchetta [2 ]
Valencio, Carlos Roberto [1 ]
机构
[1] Sao Paulo State Univ UNESP, Dept Comp Sci & Stat DCCE, Sao Jose Do Rio Preto, SP, Brazil
[2] Fed Univ Uberlandia UFU, Fac Comp FACOM, Uberlandia, MG, Brazil
来源
2016 17TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS AND TECHNOLOGIES (PDCAT) | 2016年
关键词
segmentation; aspect ratio; tetrahedral mesh quality; finite element meshes;
D O I
10.1109/PDCAT.2016.81
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Simulations based on the Finite Element Method are widely applied in different contexts. The convergence and reliability of results depends directly on the quality of the tetrahedrons that compose FEM meshes. In this context, this work aimed to obtain tetrahedral meshes of real structures from tomographic images using open source software and split those tetrahedrons that do not contribute for convergence of such simulations through the aspect ratio criteria. As a result, tetrahedral meshes from real structures were obtained together with indications of the regions that could impair FEM simulations. Moreover, this approach makes it possible to study specific methods for local refinements for improving numerical simulations. These findings are of great interest to users generally, and particularly to researchers working on geometric and topological methods for shape and solid modeling, with extensive applications in the fields of Computational Fluid Dynamics, Heat Transfer and others.
引用
收藏
页码:358 / 361
页数:4
相关论文
共 50 条
  • [41] A novel 3D mesh compression using mesh segmentation with multiple principal plane analysis
    Cheng, Shyi-Chyi
    Kuo, Chen-Tsung
    Wu, Da-Chun
    PATTERN RECOGNITION, 2010, 43 (01) : 267 - 279
  • [42] Sparse Non-Negative Matrix Factorization for Mesh Segmentation
    McGraw, Tim
    Kang, Jisun
    Herring, Donald
    INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS, 2016, 16 (01)
  • [43] Liver Segmentation on CT and MR Using Laplacian Mesh Optimization
    Chartrand, Gabriel
    Cresson, Thierry
    Chav, Ramnada
    Gotra, Akshat
    Tang, An
    De Guise, Jacques A.
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2017, 64 (09) : 2110 - 2121
  • [44] Learning Boundary Edges for 3D-Mesh Segmentation
    Benhabiles, Halim
    Lavoue, Guillaume
    Vandeborre, Jean-Philippe
    Daoudi, Mohamed
    COMPUTER GRAPHICS FORUM, 2011, 30 (08) : 2170 - 2182
  • [45] Right ventricular segmentation in cardiac MRI with moving mesh correspondences
    Punithakumar, Kumaradevan
    Noga, Michelle
    Ben Ayed, Ismail
    Boulanger, Pierre
    COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2015, 43 : 15 - 25
  • [46] Quality mesh generation in higher dimensions
    Mitchell, SA
    Vavasis, SA
    SIAM JOURNAL ON COMPUTING, 2000, 29 (04) : 1334 - 1370
  • [47] Three-dimensional thrombus segmentation in abdominal aortic aneurysms using graph search based on a triangular mesh
    Lee, Kyungmoo
    Johnson, Ryan K.
    Yin, Yin
    Wahle, Andreas
    Olszewski, Mark E.
    Scholz, Thomas D.
    Sonka, Milan
    COMPUTERS IN BIOLOGY AND MEDICINE, 2010, 40 (03) : 271 - 278
  • [48] Segmentation Quality Evaluation based on Multi-Scale Convolutional Neural Networks
    Shi, Wen
    Meng, Fanman
    Wu, Qingbo
    2017 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), 2017,
  • [49] Segmentation of paratransit users based on service quality and travel behaviour in Bandung, Indonesia
    Tarigan, Ari K. M.
    Susilo, Yusak O.
    Joewono, Tri B.
    TRANSPORTATION PLANNING AND TECHNOLOGY, 2014, 37 (02) : 200 - 218
  • [50] Segmentation of High-Speed/Hypervelocity Penetrators: Criteria of Effectiveness Based on Approximate Analytical Models#
    Ben-Dor, G.
    Dubinsky, A.
    Elperin, T.
    MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES, 2010, 38 (03) : 372 - 387