Pathological Vocal Folds Features extraction using a modified Active Contour Segmentation

Majlesi Journal of Electrical Engineering

مقاله 7، دوره 4، شماره 4، اسفند 2010   

نوع مقاله: Review Article

شناسه دیجیتال (DOI): 10.1234/mjee.v4i4.387

نویسندگان

Amaia Mendez^* ؛ Noha El-Zehiry؛ Begoña García Zapirain؛ Adel Elmaghraby

University of Deusto

چکیده

This paper presents the study of vocal videostroboscopic videos to detect morphological pathologies using an active contour segmentation and objective measurements. The ad-hoc designed active contour algorithm permits to obtain a robust and fast segmentation using vocal folds images in RGB format. In this work, we have employed connected component analysis as a post-processing tool. After the segmentation process the image is analyzed and the pathology can be localized automatically and we can extract some features of each fold (such as the size of the polyp or cyst, the glottal space, the position…). Experimental results demonstrate that the proposed method is effective. Our proposal segments correctly the 95% of database test videos and it shows a great advance in design. The objective measurements complete a new method to diagnose vocal folds pathologies.

کلیدواژه‌ها

Technology؛ Segmentation؛ en؛ Active Contour Model؛ Objective Measurements؛ Videoendoscopy؛ Benign Pathologies

مراجع

[1] “Taking care of your voice”. http://www.nidcd.nih.gov.

[2] Peter S. Popolo, Ingo R. Titze. “Qualification of a Quantitative Laryngeal Imaging System Using Videostroboscopy and Videokymography”, Ann Otol Rhinol Laryngol, p.117:404-412, (2008)

[3] J.S. Chang, E.Y. Kim, S. Hyun Park. “Lip Contour Extraction Using Level Set Curve Evolution with Shape Constraint”, Human-Computer Interaction, Part III, LNCS 4552, pp. 583-588, (2007)

[4] Y. Boykov and V. Kolmogorov. “Computing geodesics and minimal surfaces via graph cuts”, International Conference for Computer Vision, ICCV, (2003)

[5] L. D. Cohen. “On active contour models and balloons”, Computer Vision, Graphics, and Image Processing, Vol. 53, No. 2, pp. 211–218, (1991)

[6] A. Mendez, B. Garcia, I. Ruiz, I. Iturricha. “Glottal Area Segmentation without Initialization using Gabor Filters”, proc. ISSPIT, pp. 18-22. Sarajevo, (2008)

[7] Y. Yan, X. Chen, D. Bless. “Automatic tracing of vocal-fold motion from high-speed digital images”, IEEE Transactions on Biomedical Engineering, Volume 53, Issue 7, Page(s):1394 – 1400, (July 2006)

[8] Vladimir Kolmogorov, Ramin Zabih. “What Energy Functions Can Be Minimizedvia Graph Cuts?”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26 , Issue 2. pp. 147 - 159 (January 2004)

[9] H.L. Friedman. “Lewis-Randall to McMillan-Mayer conversion for the thermodynamic excess functions of solutions. Part I. Partial free energy coefficients”. Journal of Solution Chemistry, Vol. 1, No. 5, 387-412. (1972)

[10] Noha El-Zehiry, Steve Xu., Prasanna Sahoo, Adel Elmaghraby, “Graph cut optimization for the mumford-shah model”, Proceedings of the Seventh IASTED International Conference visualization, imaging and image processing, pp.182–187. Springer, Heidelberg (2007).

[11] Allin, S. Galeotti, J. Stetten, G. Dailey, “S.H. Enhanced snake based segmentation of vocal folds”, proc. ISBI 2004. 812- 815 Vol. 1, (2004)

[12] E. M. Ismaili Alaoui, A. Mendez, E. Ibn-Elhaj, B. Garcia. “Keyframes detection and analysis in vocal folds recordings using Hierarchical Motion Techniques and Texture Information”. Proc. of ICIP 2009. El Cairo, Egypt. (2009)