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

  • Yu Zhang
    Affiliations
    Department of Surgery, Division of Otolaryngology Head and Neck Surgery, University of Wisconsin Medical School, Madison, Wisconsin
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  • Erik Bieging
    Affiliations
    Department of Surgery, Division of Otolaryngology Head and Neck Surgery, University of Wisconsin Medical School, Madison, Wisconsin
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  • Henry Tsui
    Affiliations
    Department of Surgery, Division of Otolaryngology Head and Neck Surgery, University of Wisconsin Medical School, Madison, Wisconsin
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  • Jack J. Jiang
    Correspondence
    Corresponding 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.
    Affiliations
    Department of Surgery, Division of Otolaryngology Head and Neck Surgery, University of Wisconsin Medical School, Madison, Wisconsin
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      Summary

      High-speed digital imaging can provide valuable information on disordered voice production in voice science. However, the large amounts of high-speed image data with limited image resolutions produce significant challenges for computer analysis, and thus effective and efficient image edge extraction methods allowing for the batch analysis of high-speed images of vocal folds is clinically important. In this paper, a novel algorithm for automatic image edge detection is proposed to effectively and efficiently process high-speed images of the vocal folds. The method integrates Lagrange interpolation, differentiation, and Canny edge detection, which allow objective extraction of aperiodic vocal fold vibratory patterns from large numbers of high-speed digital images. This method and two other popular algorithms, histogram and active contour, are performed on 10 sets of high-speed video data from excised larynx experiments to compare their performances in analyzing high-speed images. The accuracy in computing glottal area and the computation time of these methods are investigated. The results show that our proposed method provides the most accurate and efficient detection, and is applicable when processing low-resolution images. In this study, we focus on developing a method to effectively and efficiently process high-speed image data from excised larynges. However, in addition we show the clinical potential of this method by use of example high-speed image data obtained from a patient with vocal nodules.The proposed automatic image-processing algorithm may provide a valuable biomedical application for the clinical assessment of vocal disorders by use of high-speed digital imaging.

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      References

        • Svec J.G.
        • Schutte H.K.
        Videokymography: high speed line scanning of vocal fold vibration.
        J Voice. 1996; 10: 201-205
        • Yan Y.
        • Damrose E.
        • Bless D.
        Functional analysis of voice using simultaneous high-speed imaging and acoustic recordings.
        J Voice. 2007; 21: 604-616
        • 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
        • Baken R.J.
        Clinical Measurement of Speech and Voice.
        Singular, San Diego, CA2000
        • Hirano M.
        • Bless D.
        Videostroboscopic Examination of the Larynx.
        Singular, San Diego, CA1993
        • Yan Y.L.
        • Chen X.
        • Bless D.
        Automatic Tracing of vocal-fold motion from high-speed digital images.
        IEEE Trans Biomed Eng. 2006; 53: 1394-1400
        • Westphal L.C.
        • Childers D.G.
        Representation of glottal shape data for signal processing.
        IEEE Trans Acoust. 1983; 31: 766-769
        • Hong K.H.
        • Kim H.K.
        • Niimi S.
        Laryngeal gestures during stop production using high speed digital images.
        J Voice. 2002; 16: 207-214
        • 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
        • 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
        • Jiang J.J.
        • Yumoto E.
        • Lin S.J.
        • Kadota Y.
        • Kurokawa H.
        • Hanson D.G.
        Quantitative measurement of mucosal wave by high-speed photography in excised larynges.
        Ann Otol Rhinol Laryngol. 1998; 107: 98-103
        • Saadah A.K.
        • Galatsanos N.P.
        • Bless D.
        • Ramos C.A.
        Deformation analysis of the vocal folds from videostroboscopic image sequences of the larynx.
        J Acoust Soc Am. 1998; 103: 3627-3641
        • 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
        • 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
        • 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
        • Zhang Y.
        • Jiang J.J.
        Spatiotemporal chaos in excised larynx vibrations.
        Phys Rev E. 2005; 72: 35201-35204
        • 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
        • Tao C.
        • Zhang Y.
        • Jiang J.J.
        Extracting physiologically relevant parameters of vocal folds from high-speed video image series.
        IEEE Trans Biomed Eng. 2007; 54: 794-801
        • Van den Berg J.
        • Tan T.S.
        Results of experiments with human larynxes.
        Pract Oto-Rhino-Laryngologica. 1959; 21: 425-450
        • Jiang J.J.
        • Zhang Y.
        • Ford C.N.
        Nonlinear dynamics of phonations in excised larynx experiments.
        J Acoust Soc Am. 2003; 114: 2198-2205
        • Jeffreys H.
        • Jeffreys B.S.
        Lagrange's Interpolation Formula.
        in: Methods of Mathematical Physics. 3rd ed. Cambridge University Press, Cambridge, MA1988: 260
        • Canny J.
        A computational approach to edge detection.
        IEEE Pattern Anal Machine Intell. 1986; PAMI-8: 679-698
        • Bracewell R.N.
        The Fourier Transform and Its Applications.
        3rd ed. McGraw-Hill, New York2000
        • 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
        • Kass M.
        • Witkin A.
        • Terzopoulos D.
        Snakes: active contour models.
        Int J Comput Vision. 1988; 1: 321-331
        • Xu C.
        • Prince J.
        Snakes, shapes, and gradient vector flow.
        IEEE Trans Image Process. 1998; 7: 359-369
        • Xu J.
        • Chutatape O.
        • Chew P.
        Automated optic disc boundary detection by modified active contour model.
        IEEE Trans Biomed Eng. 2007; 54: 473-482
        • Shan Z.Y.
        • Ji Q.
        • Gajjar A.
        • Reddick W.E.
        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
        • 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
        • Kay Elemetrics Corp
        High-Speed Video (HSV) Model 9700: Instruction Manual.
        Kay Elemetrics Corporation, Lincoln Park, NJ2002