3D Brain Tumor Segmentation Using Level-Sets Method and Meshes Simplification from Volumetric MR Images
The main objective of this paper is to provide an efficient tool for delineating brain tumors in three-dimensional magnetic resonance images. To achieve this goal, we use basically a level-sets approach to delineating three-dimensional brain tumors. Then we introduce a compression plan of 3D brain structures based for the meshes simplification, adapted for time to the specific needs of the telemedicine and to the capacities restricted by network communication. We present here the main stages of our system, and preliminary results which are very encouraging for clinical practice.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081727Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1764
 T. McInerney and D. Terzopoulos, "Deformable models in medical image analysis: A survey," Medical Image Analysis, vol. 1, pp. 91-108, 1996.
 M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision, vol. 1, pp. 321-331, 1987.
 C. Xu, D. Pham, and J. Prince, "Image segmentation using deformable models.," in Handbook of Medical Imaging, vol. 2: SPIE, 2000, pp. 129- 174.
 T. McInerney and D. Terzopoulos, "Topologically adaptable snakes," in the Proceedings of 5th International Conference on Computer Vision, pp. 840-845, 1995.
 S. Osher and J. A. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys. 79, 12 (1988).
 J. A. Sethian, Numerical methods for propagating fronts, in Variational Methods for Free Surface Interfaces,
 J. A. Sethian, Curvature and the evolution of fronts, Commun. Math. Phys. 101, 487 (1985).A. Alpher. Frobnication. Journal of Foo, 12(1):234-778, 2002.
 R. Malladi, J. A. Sethian, and B. C. Vemuri, "Shape modeling with front propagation: A level set approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17,no. 2, pp. 158-175, 1995.
 El. Angelini and al, Segmentation of Real-Time Three-Dimensional Ultrasound for Quantification of Ventricular Function: A Clinical Study on Right and Left Ventricles, Ultrasound in Med. & Biol., Vol. 31, No. 9, pp. 1143-1158, 2005
 Medical Database for the Evaluation of Image and Signal processing (MeDEISA),http://www.istia.univangers.fr/LISA_MeDEISA/IEEE_FR ANCE_EMB/
 D.Saupe, J.Kuska. Compression of Isosurfaces for Structured Volumes. VMV 2001 : 333-340 Stuttgart, Germany 2001.
 O. Devillers et P.-M. Gandoin. Geometric compression for interactive transmission. Dans IEEE Visualization 2000 Conference Proc., 2000.
 P.-M. Gandoin et O. Devillers. Progressive lossless compression of arbitrary simplicial complexes. ACM Transactions on Graphics, 21 :372-379, 2002. SIGGRAPH -2002 Conference Proceedings.
 C. Touma et C. Gotsman. Triangle mesh compression. Dans Graphics Interface 98 Conference Proc., pages 26-34, 1998.
 D. Cohen-Or, D. Levin, et O. Remez. Progressive compression of arbitrary triangular meshes. Dans IEEE Visualization 99 Conference Proc., pages 67-72, 1999.
 P. Alliez et M. Desbrun. Progressive compression for lossless transmission of triangle meshes. Dans SIGGRAPH 2001 Conference Proc., 2001.
 H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, et W. Stuetzle. Mesh optimization. Dans SIGGRAPH 93 Conference Proc., 1993.
 H. Hoppe, "Progressive Meshes", SIGGRAPH '96 Proc., pp. 99-108, August 1996.
 P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Machine Intell., 12 :629.639, 1990.