Minimally interactive MRI segmentation for subject-specific modelling of the tongue

Lecture Notes in Computational Vision and Biomechanics

Volumn 13, Issue , 2014, Pages 53-64

  Harandi, Negar M ^a   Abugharbieh, Rafeef ^a   Fels, Sidney ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84910660295     PISSN: 22129391     EISSN: 22129413     Source Type: Book Series    
DOI: 10.1007/978-3-319-03590-1_5     Document Type: Article

References (32)

1. Steele CM, Van Lieshout P (2009) Tongue movements during water swallowing in healthy young and older adults. J Speech Lang Hear Res 52(5):1255
2. Lloyd JE, Stavness I, Fels S (2012) ARTISYNTH: a fast interactive biomechanical modeling toolkit combining multibody and finite element simulation. Soft tissue biomechanical modeling for computer assisted surgery. Springer, Berlin, Heidelberg
3. Badin P, Bailly G, Reveret L, Baciu M, Segebarth C, Savariaux C (2002) Three-dimensional linear articulatory modeling of tongue, lips and face, based on MRI and video images. J Phonetics 30(3):533–553
4. Gerard JM, Wilhelms-Tricarico R, Perrier P, Payan Y (2006) A 3D dynamical biomechanical tongue model to study speech motor control. arXiv preprint physics/0606148
5. Sonomura M, Mizunuma H, Numamori T, Michiwaki H, Nishinari K (2011) Numerical simulation of the swallowing of liquid bolus. J Texture Stud 42(3):203–211
6. Neal ML, Kerckhoffs R(2010) Current progress in patient-specific modelling. Briefings Bioinform 11(1):111–126
7. Peled S, Yehezkel Y (2001) Superresolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging. Magn Reson Med 45(1):29–35
8. Bai Y, Xiao H, Prince JL (2004) Super-resolution reconstruction of MR brain images. Proceedings of 38th annual conference on information sciences and systems (CISS?04)
9. Woo J, Murano E, Stone M, Prince J (2012) Reconstruction of high resolution tongue volumes from MRI. IEEE Trans Biomed Eng 6(1):1–25
10. Keyak JH, Meagher JM, Skinner HB, Mote CD (1990) Automated three-dimensional finite element modelling of bone: a new method. J Biomed Eng 12(5):389–397
11. Teo JCM, Chui CK, Wang ZL, Ong SH, Yan CH, Wang SC, Wong HK, Teoh SH (2007) Heterogeneous meshing and biomechanical modeling of human spine. Med Eng Phys 29(2):277–290
12. Bucki M, Nazari MA, Payan Y (2010) Finite element speaker-specific face model generation for the study of speech production. Comput Methods Biomech Biomed Eng 13(4):459–467
13. Bresch E, Narayanan S (2009) Region segmentation in the frequency domain applied to upper airway real-time magnetic resonance images. IEEE Trans Med Imaging 28(3):323–338
14. Peng T, Kerrien E, Berger MO (2010) A shape-based framework to segmentation of tongue contours from MRI data. In: IEEE international conference on acoustics speech and signal processing (ICASSP), IEEE Press, pp 662–665
15. Eryildirim A, Berger MO (2011) A guided approach for automatic segmentation andmodeling of the vocal tract in MRI images. In: European signal processing conference (EUSIPCO)
16. Lee J, Woo J, Xing F, Murano EZ, Stone M, Prince JL (2013) Semi-automatic segmentation of the tongue for 3D motion analysis with dynamicMRI. In: IEEE 10th international symposium on biomedical imaging (ISBI), IEEE Press
17. Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(11):1768–1783
18. Leventon ME, Grimson WEL, Faugeras O (2000) Statistical shape influence in geodesic active contours. Computer vision and pattern recognition. In: Proceedings, IEEE conference on 1
19. Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson WE, Willsky A (2003) A shape-based approach to the segmentation of medical imagery using level sets. Med Imaging IEEE Trans 22(2):137–154
20. Foulonneau A, Charbonnier P, Heitz F (2009) Multi-reference shape priors for active contours. Int J Comput Vis 81(1):68–81
21. Bresson X, Vandergheynst P, Thiran J (2006) A variational model for object segmentation using boundary information and shape prior driven by the Mumford-Shah functional. Int J Comput Vis 68(2):145–162
22. Cootes TF et al (1995) Active shape models: their training and application. Comput Vis Image Underst 61(1):38–59
23. Heimann T, Münzing S, Meinzer HP (2007) A shape-guided deformable model with evolutionary algorithm initialization for 3D soft tissue segmentation. In: Karssemeijer N, Lelieveldt B (eds) Inf Process Med Imaging 5484:1–10. Springer
24. Saddi KA, Rousson M, Chefd HC, Cheriet F (2007) Global-to-local shape matching for liver segmentation in CT imaging. MICCAI
25. Somphone O, Mory B, Makram-Ebeid S, Cohen L (2008) Prior-based piecewise-smooth segmentation by template competitive deformation using partitions of unity. In: Computer vision? ECCV 2008. Springer, Berlin Heidelberg, pp 628–641
26. Gilles B, Pai D (2008) Fast musculoskeletal registration based on shapematching. In:Metaxas DN, Axe L (eds) MICCAI 2008, LNCS, vol 5242. Springer, Heidelberg, pp 822–829
27. Muller M, Heidelberger B, Teschner M, Gross M (2005) Meshless deformations based on shape matching. ACM Trans Graphics 24(3):471–478. ACM
28. Freedman D, Zhang T (2005) Interactive graph cut based segmentation with shape priors. Computer vision and pattern recognition (2005). IEEE computer society conference on 1, IEEE
29. Mory B, Somphone O, Prevost R, Ardon D (2012) Real-Time 3d image segmentation by userconstrained template deformation. In: MICCAI 2012. Springer, Berlin, Heidelberg, pp 561–568
30. SOFA (2013) Simulation open framework architecture. www.sofa-framework.org
31. Cachier P, Ayache N (2001) Regularization in image non-rigid registration: I. Trade-off between smoothness and intensity similarity. Technical report, INRIA
32. Top A, Hamarneh G, Abugharbieh R (2011) Active learning for interactive 3d image segmentation. In: Peters T, Fichtinger L (eds) MICCAI 2011, LNCS, vol Springer, Heidelberg, pp 603–610