Journal of Voice
Volume 24, Issue 1 , Pages 21-29 , January 2010

Efficient and Effective Extraction of Vocal Fold Vibratory Patterns from High-Speed Digital Imaging

  • Yu Zhang
    • ,
  • Erik Bieging
    • ,
  • Henry Tsui
    • ,
  • Jack J. Jiang

      Affiliations

    • Corresponding Author InformationCorresponding author. Department of Surgery, Division of Otolaryngology Head and Neck Surgery, 5745 Medical Science Center, 1300 University Avenue, University of Wisconsin Medical School, Madison, WI 53706, USA.

,Accepted 14 March 2008.

References 

  1. Svec JG, Schutte HK. Videokymography: high speed line scanning of vocal fold vibration. J Voice. 1996;10:201–205
  2. Yan Y, Damrose E, Bless D. Functional analysis of voice using simultaneous high-speed imaging and acoustic recordings. J Voice. 2007;21:604–616
  3. Wittenberg L, Tigges M, Mergell P, Eysholdt U. Functional imaging of vocal fold vibration: digital multislice high speed kymography. J Voice. 2000;14:422–442
  4. Baken RJ. Clinical Measurement of Speech and Voice. San Diego, CA: Singular; 2000;
  5. Hirano M, Bless D. Videostroboscopic Examination of the Larynx. San Diego, CA: Singular; 1993;
  6. Yan YL, Chen X, Bless D. Automatic Tracing of vocal-fold motion from high-speed digital images. IEEE Trans Biomed Eng. 2006;53:1394–1400
  7. Westphal LC, Childers DG. Representation of glottal shape data for signal processing. IEEE Trans Acoust. 1983;31:766–769
  8. Hong KH, Kim HK, Niimi S. Laryngeal gestures during stop production using high speed digital images. J Voice. 2002;16:207–214
  9. Yan Y, Ahmad K, Kunduk M, Bless D. Analysis of vocal fold vibrations from high speed laryngeal images using a Hilbert transform based methodology. J Voice. 2005;19/2:161–175
  10. Wittenberg T, Moser M, Tigges M, Eysholdt U. Recording, processing, and analysis of digital high-speed sequences in glottography. Mach Vis Applicat. 1995;8:399–404
  11. Jiang JJ, Yumoto E, Lin SJ, Kadota Y, Kurokawa H, Hanson DG. Quantitative measurement of mucosal wave by high-speed photography in excised larynges. Ann Otol Rhinol Laryngol. 1998;107:98–103
  12. Saadah AK, Galatsanos NP, Bless D, Ramos CA. Deformation analysis of the vocal folds from videostroboscopic image sequences of the larynx. J Acoust Soc Am. 1998;103:3627–3641
  13. Marendic B, Galatsanos N, Bless D. A new active contour algorithm for tracking vocal folds. Proc IEEE Int Conf Image Process. 2001;1:397–400
  14. Lohscheller J, Dollinger M, Schuster M, Schwarz R, Eysholdt U, Hoppe U. Quantitative investigation of the vibratory pattern of the substitute voice generator. IEEE Trans Biomed Eng. 2004;51:1394–1400
  15. Lohscheller J, Toy H, Rosanowski F, Eyesholdt U, Dollinger M. Clinically evaluated procedure for the reconstruction of vocal fold vibrations from endoscopic digital high-speed videos. Med Image Analysis. 2007;11:400–413
  16. Zhang Y, Jiang JJ. Spatiotemporal chaos in excised larynx vibrations. Phys Rev E. 2005;72:35201–35204
  17. Schwarz R, Hoppe U, Schuster M, Wurzbacher T, Eysholdt U, Lohscheller J. Classification of unilateral vocal fold paralysis by endoscopic digital high-speed recordings and inversion of a biomechanical model. IEEE Trans Biomed Eng. 2006;53:1099–1108
  18. Tao C, Zhang Y, Jiang JJ. Extracting physiologically relevant parameters of vocal folds from high-speed video image series. IEEE Trans Biomed Eng. 2007;54:794–801
  19. Van den Berg J, Tan TS. Results of experiments with human larynxes. Pract Oto-Rhino-Laryngologica. 1959;21:425–450
  20. Jiang JJ, Zhang Y, Ford CN. Nonlinear dynamics of phonations in excised larynx experiments. J Acoust Soc Am. 2003;114:2198–2205
  21. Jeffreys H, Jeffreys BS. Lagrange's Interpolation Formula. In: Methods of Mathematical Physics. 3rd ed.. Cambridge, MA: Cambridge University Press; 1988;p. 260
  22. Canny J. A computational approach to edge detection. IEEE Pattern Anal Machine Intell. 1986;PAMI-8:679–698
  23. Bracewell RN. The Fourier Transform and Its Applications. 3rd ed.. New York: McGraw-Hill; 2000;
  24. Bocker W, Muller W-U, Streffer C. Comparison of different automatic threshold algorithms for image segmentation in microscope images. Proc SPIEInt Soc Opt Eng. 1995;2564:230–241
  25. Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int J Comput Vision. 1988;1:321–331
  26. Xu C, Prince J. Snakes, shapes, and gradient vector flow. IEEE Trans Image Process. 1998;7:359–369
  27. Xu J, Chutatape O, Chew P. Automated optic disc boundary detection by modified active contour model. IEEE Trans Biomed Eng. 2007;54:473–482
  28. Shan ZY, Ji Q, Gajjar A, Reddick WE. A knowledge-guided active contour method of segmentation of cerebella on MR images of pediatric patients with medulloblastoma. J Mag Res Imag. 2005;21:1–11
  29. Yezzi A, Kichenassamy S, Kumar A, Olver R, Tannenbaum A. A geometric snake model for segmentation of medical imagery. IEEE Trans Med Imag. 2006;16:199–209
  30. Kay Elemetrics Corp . High-Speed Video (HSV) Model 9700: Instruction Manual. Lincoln Park, NJ: Kay Elemetrics Corporation; 2002;

PII: S0892-1997(08)00043-X

doi: 10.1016/j.jvoice.2008.03.003

Journal of Voice
Volume 24, Issue 1 , Pages 21-29 , January 2010

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