Literature DB >> 16830943

Automatic tracing of vocal-fold motion from high-speed digital images.

Yuling Yan1, Xin Chen, Diane Bless.   

Abstract

Defining characteristics of the phonatory vocal fold vibration is essential for studies that aim to understand the mechanism of voice production and for clinical diagnosis of voice disorders. The application of high-speed digital imaging techniques to these studies makes it possible to capture sequences of images of the vibrating vocal folds at a frequency that can resolve the actual vocal fold vibrations of a patient. The objective of this study is to introduce a new approach for automatic tracing of vocal fold motion from image sequences acquired from high-speed digital imaging of the larynx. The approach involves three process steps. 1) Global thresholding--the threshold value is selected on the basis of the histogram of the image, which is assumed to follow Rayleigh distribution; 2) applying a morphology operator to remove the isolated object regions; 3) using region-growing to delineate the object, or the vocal fold opening region, and to obtain the area of the glottis; the segmented object obtained after global threshold and the morphological operation is used as a seed region for the final region-growing operation. The performance, effectiveness and validation of our approach is demonstrated using representative, high-speed imaging recordings of subjects having normal and pathological voices.

Entities:  

Mesh:

Year:  2006        PMID: 16830943     DOI: 10.1109/TBME.2006.873751

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  13 in total

1.  Efficient and effective extraction of vocal fold vibratory patterns from high-speed digital imaging.

Authors:  Yu Zhang; Erik Bieging; Henry Tsui; Jack J Jiang
Journal:  J Voice       Date:  2008-05-27       Impact factor: 2.009

Review 2.  Advances in laryngeal imaging.

Authors:  Antanas Verikas; Virgilijus Uloza; Marija Bacauskiene; Adas Gelzinis; Edgaras Kelertas
Journal:  Eur Arch Otorhinolaryngol       Date:  2009-07-19       Impact factor: 2.503

Review 3.  Advanced computing solutions for analysis of laryngeal disorders.

Authors:  H Irem Turkmen; M Elif Karsligil
Journal:  Med Biol Eng Comput       Date:  2019-09-06       Impact factor: 2.602

4.  Laryngeal High-Speed Videoendoscopy: Rationale and Recommendation for Accurate and Consistent Terminology.

Authors:  Dimitar D Deliyski; Robert E Hillman; Daryush D Mehta
Journal:  J Speech Lang Hear Res       Date:  2015-10       Impact factor: 2.297

5.  Theoretical modeling and experimental high-speed imaging of elongated vocal folds.

Authors:  Yu Zhang; Michael F Regner; Jack J Jiang
Journal:  IEEE Trans Biomed Eng       Date:  2010-11-29       Impact factor: 4.538

6.  Non-invasive in vivo measurement of the shear modulus of human vocal fold tissue.

Authors:  Siavash Kazemirad; Hani Bakhshaee; Luc Mongeau; Karen Kost
Journal:  J Biomech       Date:  2013-12-01       Impact factor: 2.712

7.  Spatiotemporal analysis of normal and pathological human vocal fold vibrations.

Authors:  Christopher R Krausert; Yufang Liang; Yu Zhang; Adam L Rieves; Kyle R Geurink; Jack J Jiang
Journal:  Am J Otolaryngol       Date:  2012-07-26       Impact factor: 1.808

8.  The glottaltopogram: a method of analyzing high-speed images of the vocal folds.

Authors:  Gang Chen; Jody Kreiman; Abeer Alwan
Journal:  Comput Speech Lang       Date:  2014-09-01       Impact factor: 1.899

Review 9.  Voice assessment: updates on perceptual, acoustic, aerodynamic, and endoscopic imaging methods.

Authors:  Daryush D Mehta; Robert E Hillman
Journal:  Curr Opin Otolaryngol Head Neck Surg       Date:  2008-06       Impact factor: 2.064

10.  Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network.

Authors:  Mona Kirstin Fehling; Fabian Grosch; Maria Elke Schuster; Bernhard Schick; Jörg Lohscheller
Journal:  PLoS One       Date:  2020-02-10       Impact factor: 3.240

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