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Vocal Cord Kinematics – New Evaluation Parameters


Authors: J. Pešta 1;  J. Slípka 1;  M. Vohlídková 1;  T. Ettler 2;  P. Nový 2;  F. Vávra 3
Authors place of work: ORL klinika FN Plzeň, přednosta kliniky doc. MUDr. J. Slípka, CSc. 1;  Fakulta aplikovaných věd ZČU Plzeň, Katedra informatiky a výpočetní techniky, vedoucí katedry doc. Ing. P. Brada, MSc., Ph. D. 2;  Fakulta aplikovaných věd ZČU Plzeň, Katedra matematiky, vedoucí katedry prof. RNDr. P. Drábek, DrSc. souhrn 3
Published in the journal: Otorinolaryngol Foniatr, 65, 2016, No. 2, pp. 88-96.
Category: Original Article

Summary

This study deals with new findings about vocal folds kinematics acquired by using High–Speed Video camera. The parameters usually applied to measuring vocal folds kinematics are supplemented with new ones which are based on detection of glottis and determination of its main axis. Parameters of glottis symmetry and parameters of motion of glottis center point against its main axis are illustrated on selected case reports.

Keywords:
laryngology, vocal folds, glottis, voice quality, laryngoscopy, speech production measurement


Zdroje

1. Aghlmandi, D., Faez, K.: Automatic segmentation of glottal space from video Images based on mathematical morphology and the hough zransform. International Journal of Electrical and Computer Engineering (IJECE),.2, 2012, 2, s. 223~230, ISSN: 2088-8708. (http://iaesjournal.com/online/index.php/IJECE).

2. Andrade Miranda, G., Godino Llorente, J. I., Moro Velazquez, L., Gomez Garcia, J. A.: An automatic method to detect and track the glottal gap from high speed videoendoscopic images. BioMedical Engineering OnLine. 2015.

3. Blanco, M., Chen, X., Yan, Y.: A restricted, adaptive threshold segmentation approach for processing high-speed image sequences of the glottis. Engineering, 5, 357-362, Published Online October 2013 (http://www.scirp.org/journal/eng).

4. Bleau, A., Leon, L.: Watershed-based segmentation and region merging. Computer Vision and Image Understanding, 77, 2000,3, s. 317-370.

5. Ettler, T.: Analýza vysokorychlostního záznamu kmitání hlasivek. Diplomová práce, ZČU v Plzni, FAV, Katedra informatiky a výpočetní techniky, Plzeň, 2012.

6. Chen, J.: Vocal fold analysis from high speed videoEndoscopic data. A disertation. Louisiana State University. 2014.

7. Kittler, J., Illingwrth, J.: Minimum error thresholding, SERC rutherford appleton laboratory, Chilton, Didcot, Oxon OX11 0QX, U.K, 1986.

8. Kroupa, L.: Systém vyhodnocení parametrů jednoho kmitu hlasivek. Diplomová práce, ZČU v Plzni, FAV, Katedra informatiky a výpočetní techniky, Plzeň, 2015.

9. MacQueen, J. B.: Some methods for classification and analysis of multivariate observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1. University of California Press, s. 281-297. MR 0214227. Zbl 0214.46201. Retrieved 2009-04-07. 1967.

10. Mehta, D. D.: Investigating the impact of in vivo human vocal fold vibratory asymmetries: Co-variations among measures from laryngeal high-speed videoendoscopy, acoustic voice analysis, and auditory-perceptual voice assessment of sustained vowel phonation. Thesis Research for the Degree of Doctor of Philosophy. Massachusetts Institute of Technology, 2009.

11. Mehta, D. D., Deliyski, D. D., Quatieri, T. F., Hillman, R. E.: Automated measurement of vocal fold vibratory asymmetry from high-speed videoendoscopy recordings. Journal of Speech, Language, and Hearing Research, 54, 2011, s. 47-54.

12. Ng, H. F.: Automatic thresholding for defect detection, Pattern Recognition Letters, 27, 2006, s. 1644-1649.

13. Nový, P., Vávra, F., Kotlíková, M.: Voice range profile examination method and its applications. Summer School DATASTAT 03, Proceedings, ISBN - 80-210-3564-1, Brno, Masaryk University, 2003.

14. Nový, P., Vávra, F., Pešta, J. et al.: Parameters identification from phoniatrical examinations, summer school DATASTAT 06. Proceedings, 2007, s. 221-234, ISBN 978-80-210-4493-7, Brno: Masaryk University.

15. Palm, C., Keysers, D., Lehmann, T. et al.: Gabor filtering of complex hue/saturation images for color texture classication. Proc. JCIS 2000, Atlantic City, USA, s. 45-49.

16. Peng, B., Zhang, L.: Automatic image segmentation by dynamic region merging. IEEE Trans. Image Processing, 12, 2011, 12, s. 3592-3605.

17. Pešta, J., Slípka, J., Nový, P. et al.: Evaluating the quality of the glottis closure. Otorinolaryng. a Foniat./Prague/, 59, 2010, 4, s. 190-196,ISSN 1210-7867.

18. Russ, J. C.: The image processing handbook. Fourth Edition, CRC 2002,ISBN 0-8493-2532-3.

19. Shapiro, L., Stockman, G.: Computer vision. Prentice Hall, 2002, s. 69-73.

20. Schenk, F., Aichinger, P., Roesner, I., Urschler, M.: Automatic high-speed video glottis segmentation using salient regions and 3D geodesic active contours. Annals of the BMVA, 2015, 1, s. 1-15.

21. Švec, J.: Studium mechanicko-akustických vlastností zdroje lidského hlasu. Dizertační práce, Přírodovědecká fakulta, Univerzita Palackého v Olomouci, Olomouc, 1996.

22. Tatiraju, S., Mehta, A.: Image segmentation using k-means clustering, EM and normalized cuts. Department of EECS, 2008, s. 1-7.

23. Laryngoscopic diagnosis system HRES ENDOCAM 5562, Richard Wolf GmbH, [Cit. 29. 7. 2015]. Dostupné z URL:

http://www.richardwolf.be/userfiles/files/products/kno/G647_hres_endocam_5562_en.pdf

http://www.richard-wolf.com/discipline/ent.html

24. Color High-Speed Video System (CHSV), Model 9710, KayPENTAX, [Cit. 29. 7. 2015]. Dostupné z URL: http://www.kayelemetrics.com/index.php?option=com_product&view=product&Itemid=3&controller=product&cid[]=77&task=pro_details

Štítky
Audiology Paediatric ENT ENT (Otorhinolaryngology)
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