↓ Skip to main content

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

Overview of attention for article published in BioMedical Engineering OnLine, October 2015
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
27 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
An automatic method to detect and track the glottal gap from high speed videoendoscopic images
Published in
BioMedical Engineering OnLine, October 2015
DOI 10.1186/s12938-015-0096-3
Pubmed ID
Authors

Gustavo Andrade-Miranda, Juan I. Godino-Llorente, Laureano Moro-Velázquez, Jorge Andrés Gómez-García

Abstract

The image-based analysis of the vocal folds vibration plays an important role in the diagnosis of voice disorders. The analysis is based not only on the direct observation of the video sequences, but also in an objective characterization of the phonation process by means of features extracted from the recorded images. However, such analysis is based on a previous accurate identification of the glottal gap, which is the most challenging step for a further automatic assessment of the vocal folds vibration. In this work, a complete framework to automatically segment and track the glottal area (or glottal gap) is proposed. The algorithm identifies a region of interest that is adapted along time, and combine active contours and watershed transform for the final delineation of the glottis and also an automatic procedure for synthesize different videokymograms is proposed. Thanks to the ROI implementation, our technique is robust to the camera shifting and also the objective test proved the effectiveness and performance of the approach in the most challenging scenarios that it is when exist an inappropriate closure of the vocal folds. The novelties of the proposed algorithm relies on the used of temporal information for identify an adaptive ROI and the use of watershed merging combined with active contours for the glottis delimitation. Additionally, an automatic procedure for synthesize multiline VKG by the identification of the glottal main axis is developed.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 22%
Student > Bachelor 4 15%
Student > Master 3 11%
Researcher 2 7%
Professor 1 4%
Other 3 11%
Unknown 8 30%
Readers by discipline Count As %
Engineering 6 22%
Medicine and Dentistry 6 22%
Nursing and Health Professions 3 11%
Social Sciences 2 7%
Computer Science 1 4%
Other 0 0%
Unknown 9 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 June 2016.
All research outputs
#18,464,797
of 22,879,161 outputs
Outputs from BioMedical Engineering OnLine
#565
of 823 outputs
Outputs of similar age
#205,026
of 284,730 outputs
Outputs of similar age from BioMedical Engineering OnLine
#11
of 17 outputs
Altmetric has tracked 22,879,161 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 823 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 284,730 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.