Tetrahedral Meshing of Volumetric Medical Images Respecting Image Edges

1. Spanel, M., Krsek, P., Stancl, V.: Vector Segmentation of Volumetric Image Data: Tetrahedral Meshing Constrained by Image Edges. In: Proceedings of the 3rd International Joint Converence on Computer Vision, Imaging and Computer Graphics Theaory and Applications, pp. 134–138 (2010)

   Google Scholar 

2. George, P.-L., Borouchaki, H.: Delaunay Triangulation and Meshing: Application to Finite Elements, 413 pages, (1998)

   Google Scholar 

3. Labelle, F., Shewchuk, J.R.: Isosurface stuffing: fast tetrahedral meshes with good dihedral angles. ACM Trans. Graph. 26(3), 57 (2007)

   Article  Google Scholar 

4. Zhang, Y., Bajaj, C., Sohn, B.-S.: Adaptive and quality 3D meshing from imaging data. In: Proceedings of the Eighth ACM Symposium on Solid Modeling and Applications, pp. 286–291 (2003)

   Google Scholar 

5. Alliez, P., Cohen-Steiner, D., Yvinec, M., Desbrun, M.: Variational tetrahedral meshing. ACM Trans. Graph. 24(3), 617–625 (2005)

   Article  Google Scholar 

6. Fabbri, R., Costa, L., da, F., Torelli, J.C., Bruno, O.M.: 2D Euclidean Distance Transform Algorithms: A Comparative Survey. ACM Computing Surveys 40(1), 1–44 (2008)

   Article  Google Scholar 

7. Du, Q., Emelianenko, M., Ju, L.: Convergence of the lloyd algorithm for computing centroidal voronoi tessellations. SIAM J. Numer. Anal. 44(1), 102–119 (2006)

   Article  MathSciNet  MATH  Google Scholar 

8. Fehr, J., Burkhardt, H.: 3d rotation invariant local binary patterns. Pattern Recognition 29, 1–4 (2008)

   Article  Google Scholar 

9. Pham, D.L., Prince, J.L.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Transactions on Medical Imaging 18 (1999)

   Google Scholar 

10. Ng, S.K., McLachlan, G.J.: On some variants of the em algorithm for fitting mixture models. Austrian Journal of Statistics 23, 143–161 (2003)

    Google Scholar 

11. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1124–1137 (2004)

    Article  MATH  Google Scholar 

12. Kurita, T.: An efficient agglomerative clustering algorithm for region growing. In: Proc. of IAPR Workshop on Machine Vision Applications, pp. 210–213 (1991)

    Google Scholar 

13. Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3d surface construction algorithm. SIGGRAPH Comput. Graph. 21(4), 163–169 (1987)

    Article  Google Scholar 

14. Botsch, M., Pauly, M., Rossl, C., Bischoff, S., Kobbelt, L.: Geometric modeling based on triangle meshes. In: SIGGRAPH Course Notes (2006)

    Google Scholar 

15. Taubin, G.: Geometric signal processing on polygonal meshes (2000)

    Google Scholar 

16. Vollmer, J., Mencl, R., Mller, H.: Improved laplacian smoothing of noisy surface meshes. In: Computer Graphics Forum, vol. 18, pp. 131–138. Blackwell Publishing, Malden (1999)

    Google Scholar 

17. Garland, M., Heckbert, P.S.: Surface simplification using quadric error metrics. In: SIGGRAPH 1997: Proceedings of the 24th annual conference on Computer graphics and interactive techniques, pp. 209–216 (1997)

    Google Scholar 

18. Hildebrandt, K., Polthier, K.: Constraint-based fairing of surface meshes. In: Proc. of the Fifth Eurographics Symposium on Geometry Processing, pp. 203–212 (2007)

    Google Scholar 

19. Cignoni, P., Rocchini, C., Scopigno, R.: Metro: measuring error on simplified surfaces. Computer Graphics Forum 17, 167–174 (1998)

    Article  Google Scholar 

20. Tournois, J., Srinivasan, R., Alliez, P.: Perturbing slivers in 3d delaunay meshes. In: Proceedings of the 18th International Meshing Roundtable, pp. 157–173 (2009)

    Google Scholar 

21. Veksler, O.: Gcmex – matlab wrapper for graph cuts multi-label energy minimization (2010)

    Google Scholar 

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