Omnidirectional displacements for deformable surfaces

Medical Image Analysis

Volumn 17, Issue 4, 2013, Pages 429-441

  Kainmueller, Dagmar ^a   Lamecker, Hans ^a   Heller, Markus O ^b   Weber, Britta ^a   Hege, Hans Christian ^a   Zachow, Stefan ^a  

^a ZUSE INSTITUTE BERLIN   (Germany)

^b CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

Author keywords

Acetabulum; Deformable surfaces; Mandible; Markov random field; Segmentation

Indexed keywords

ACETABULUM; DEFORMABLE SURFACE MODELS; DEFORMABLE SURFACES; GLOBAL SEGMENTATION; LOCAL REGULARIZATION; MANDIBLE; MARKOV RANDOM FIELDS; REDUCED MEMORY REQUIREMENTS;

BONE; IMAGE SEGMENTATION; MARKOV PROCESSES; TRACKING (POSITION);

DEFORMATION;

ACCURACY; ACETABULUM; ARTICLE; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE QUALITY; IMAGE SEGMENTATION; MANDIBLE; PRIORITY JOURNAL; RANGE OF MOTION; SURFACE PROPERTY;

ALGORITHMS; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84875838486     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2012.11.006     Document Type: Article

References (36)

1. Bucki M., Lobos C., Payan Y. A fast and robust patient specific finite element mesh registration technique: application to 60 clinical cases. Med. Image Anal. 2010, 14:303-317.
2. Cazals F., Chazal F., Lewiner T. ******Molecular shape analysis based upon the Morse-Smale complex and the Connolly function, in: SCG '03: Proceedings of the nineteenth Annual Symposium on Computational Geometry 2003, ACM, New York, NY, USA, pp. 351-360.
3. Chambers J. Graphical Methods for Data Analysis 1983, Chapman & Hall Statistics Series Wadsworth International Group.
4. Conway J., Sloane N., Bannai E. Sphere packings, lattices, and groups. A Series of Comprehensive Studies in Mathematics 1999, Springer.
5. Cootes T.F., Taylor C.J., Cooper D.H., Graham J. Active shape models - their training and application. Comput. Vis. Image Underst. 1995, 61:38-59.
6. Dijkstra E.W. A note on two problems in connexion with graphs. Numer. Math. 1959, 1:269-271.
7. Forman R. Morse theory for cell complexes. Adv. Math. 1998, 134:90-145.
8. Glocker B., Komodakis N., Tziritas G., Navab N., Paragios N. Dense image registration through MRFs and efficient linear programming. Med. Image Anal. 2008, 12:731-741.
9. He L., Peng Z., Everding B., Wang X., Han C.Y., Weiss K.L., Wee W.G. A comparative study of deformable contour methods on medical image segmentation. Image Vis. Comput. 2008, 26:141-163.
10. Heimann T., Delingette H. Model-based segmentation. Biomedical Image Processing. Biological and Medical Physics, Biomedical Engineering 2011, 279-303. Springer Berlin, Heidelberg. T.M. Deserno (Ed.).
11. Heimann T., Meinzer H.P. Statistical shape models for 3d medical image segmentation: a review. Med. Image Anal. 2009, 13:543-563.
12. Hollander M., Wolfe D. Nonparametric statistical methods. Wiley Series in Probability and Statistics 1999, Wiley.
13. Kainmueller D., Lamecker H., Seim H., Zachow S., Hege H.C. Improving deformable surface meshes through omni-directional displacements and MRFs. MICCAI. Lecture Notes in Computer Science 2010, vol. 6361:227-234. Springer.
14. Kainmueller D., Lamecker H., Seim H., Zinser M., Zachow S. Automatic extraction of mandibular nerve and bone from cone-beam CT data. MICCAI. Lecture Notes in Computer Science 2009, vol. 5762:76-83. Springer.
15. Kainmueller D., Lamecker H., Zachow S. Multi-object segmentation with coupled deformable models. Ann. Br. Mach. Vis. Assoc. (BMVA) 2009, 5:1-10.
16. Kainmueller, D., Lamecker, H., Zachow, S., et al., 2009c. An articulated statistical shape model for accurate hip joint segmentation. In: Int. Conf. of the IEEE Eng. in Med. and Biol. Society (EMBC), Minneapolis, USA, pp. 6345-6351.
17. Kainmueller, D., Lange, T., Lamecker, H., 2007. Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: 3D Segmentation in the Clinic: A Grand, Challenge, pp. 109-116.
18. Koenderink J., van Doorn A. Local features of smooth shapes: ridges and courses. SPIE 2031 Geometric Methods in Computer Vision 1993, II:2-13.
19. Komodakis N., Tziritas G., Paragios N. Performance vs computational efficiency for optimizing single and dynamic MRFs: setting the state of the art with primal-dual strategies. Comput. Vis. Image Underst. 2008, 112:14-29.
20. Lee K., Johnson R.K., Yin Y., Wahle A., Olszewski M.E., Scholz T.D., Sonka M. Three-dimensional thrombus segmentation in abdominal aortic aneurysms using graph search based on a triangular mesh. Comput. Biol. Med. 2010, 40:271-278.
21. Li K., Wu X., Chen D.Z., Sonka M. Optimal surface segmentation in volumetric images - a graph-theoretic approach. IEEE TPAMI 2006, 28:119-134.
22. Ma J., Lu L., Zhan Y., Zhou X., Salganicoff M., Krishnan A. Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. Medical Image Computing and Computer-Assisted Intervention MICCAI 2010. Lecture Notes in Computer Science 2010, vol. 6361:19-27. Springer, Berlin/Heidelberg.
23. Montagnat J., Delingette H., Ayache N. A review of deformable surfaces: topology, geometry and deformation. Image Vis. Comput. 2001, 19:1023-1040.
24. Okada T., Shimada R., Sato Y., Hori M., Yokota K., Nakamoto M., Chen Y.W., Nakamura H., Tamura S. Automated segmentation of the liver from 3d CT images using probabilistic atlas and multi-level statistical shape model. Proceedings of the 10th International Conference on Medical Image Computing and Computer-Assisted Intervention 2007, vol. Part I:86-93. Springer-Verlag, Berlin, Heidelberg.
25. Park J.Y., McInerney T., Terzopoulos D., Kim M.H. A non-self-intersecting adaptive deformable surface for complex boundary extraction from volumetric images. Comput. Graph. 2001, 25:421-440.
26. Paulsen R.R., Baerentzen J.A., Larsen R. Markov random field surface reconstruction. IEEE Trans. Vis. Comput. Graph. 2010, 16:636-646.
27. Petersen J., Nielsen M., Lo P., Saghir Z., Dirksen A., de Bruijne M. Optimal graph based segmentation using flow lines with application to airway wall segmentation. Information Processing in Medical Imaging. Lecture Notes in Computer Science 2011, vol. 6861:49-60. Springer, Berlin/Heidelberg.
28. Rothe R. Zum Problem des Talwegs. Sitz. ber. d. Berliner Math. Gesellschaft 1915, 14:51-69.
29. Seim, H., Kainmueller, D., Heller, M., Lamecker, H., Zachow, S., Hege, H.C., 2008. Automatic segmentation of the pelvic bones from CT data based on a statistical shape model. In: Proc. VCBM, pp. 93-100.
30. Seim, H., Kainmueller, D., Lamecker, H., Bindernagel, M., Malinowski, J., Zachow, S., 2010. Model-based auto-segmentation of knee bones and cartilage in MRI data. In: Ginneken, B. et al. (Eds.), Proc. MICCAI Workshop Medical Image Analysis for the Clinic: A Grand, Challenge, pp. 215-223.
31. Surazhsky, V., Gotsman, C., 2003. Explicit surface remeshing. In: Proceedings of the 2003 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing, Eurographics Association, Aire-la-Ville, Switzerland, pp. 20-30.
32. Terzopoulos D. The computation of visible-surface representations. IEEE Trans. Pattern Anal. Mach. Intell. 1988, 10:417-438.
33. Weber, B., 2008. Merkmalskurven auf triangulierten Oberflächen. Master's thesis. Department of Mathematics and Computer Science, FU-Berlin. Advised by Konrad Polthier and Steffen Prohaska.
34. Xu, C., Pham, D.L., Prince, J.L., 2000. Image segmentation using deformable models. In: Sonka M., Fitzpatrick, J.M. (Eds.), Handbook of Medical Imaging. Medical Image Processing and Analysis, vol. 2, pp. 129-174.
35. Yin Y., Zhang X., Williams R., Wu X., Anderson D.D., Sonka M. Logismos-layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint. IEEE Trans. Med. Imag. 2010, 29:2023-2037.
36. Zhang X., Tian J., Deng K., Wu Y., Li X. Automatic liver segmentation using a statistical shape model with optimal surface detection. IEEE Trans. Biomed. Eng. 2010, 57:2622-2626.