Literature DB >> 18243657

Segmentation of the glottal space from laryngeal images using the watershed transform.

Víctor Osma-Ruiz1, Juan I Godino-Llorente, Nicolás Sáenz-Lechón, Rubén Fraile.   

Abstract

The present work describes a new method for the automatic detection of the glottal space from laryngeal images obtained either with high speed or with conventional video cameras attached to a laryngoscope. The detection is based on the combination of several relevant techniques in the field of digital image processing. The image is segmented with a watershed transform followed by a region merging, while the final decision is taken using a simple linear predictor. This scheme has successfully segmented the glottal space in all the test images used. The method presented can be considered a generalist approach for the segmentation of the glottal space because, in contrast with other methods found in literature, this approach does not need either initialization or finding strict environmental conditions extracted from the images to be processed. Therefore, the main advantage is that the user does not have to outline the region of interest with a mouse click. In any case, some a priori knowledge about the glottal space is needed, but this a priori knowledge can be considered weak compared to the environmental conditions fixed in former works.

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Year:  2008        PMID: 18243657     DOI: 10.1016/j.compmedimag.2007.12.003

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  10 in total

Review 1.  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

2.  Observation and analysis of in vivo vocal fold tissue instabilities produced by nonlinear source-filter coupling: a case study.

Authors:  Matías Zañartu; Daryush D Mehta; Julio C Ho; George R Wodicka; Robert E Hillman
Journal:  J Acoust Soc Am       Date:  2011-01       Impact factor: 1.840

3.  Glottal Gap tracking by a continuous background modeling using inpainting.

Authors:  Gustavo Andrade-Miranda; Juan Ignacio Godino-Llorente
Journal:  Med Biol Eng Comput       Date:  2017-05-27       Impact factor: 2.602

Review 4.  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

Review 5.  State of the art laryngeal imaging: research and clinical implications.

Authors:  Dimitar D Deliyski; Robert E Hillman
Journal:  Curr Opin Otolaryngol Head Neck Surg       Date:  2010-06       Impact factor: 2.064

6.  A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation.

Authors:  Max-Heinrich Laves; Jens Bicker; Lüder A Kahrs; Tobias Ortmaier
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-01-16       Impact factor: 2.924

7.  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

8.  An automatic method to detect and track the glottal gap from high speed videoendoscopic images.

Authors:  Gustavo Andrade-Miranda; Juan I Godino-Llorente; Laureano Moro-Velázquez; Jorge Andrés Gómez-García
Journal:  Biomed Eng Online       Date:  2015-10-29       Impact factor: 2.819

9.  A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech.

Authors:  Ahmed M Yousef; Dimitar D Deliyski; Stephanie R C Zacharias; Alessandro de Alarcon; Robert F Orlikoff; Maryam Naghibolhosseini
Journal:  Appl Sci (Basel)       Date:  2021-01-27       Impact factor: 2.679

10.  Segmentation of Glottal Images from High-Speed Videoendoscopy Optimized by Synchronous Acoustic Recordings.

Authors:  Bartosz Kopczynski; Ewa Niebudek-Bogusz; Wioletta Pietruszewska; Pawel Strumillo
Journal:  Sensors (Basel)       Date:  2022-02-23       Impact factor: 3.576

  10 in total

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