Automatic Quantification of Vocal Cord Paralysis
An Application of Fibre-optic Endoscopy Video Processing
Radhika Menon
, Lykourgos Petropoulakis
, John J. Soraghan
, Heba Lakany
Kenneth MacKenzie
, Omar Hilmi
and Gaetano Di Caterina
Department of Electronic & Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow, U.K.
Department of Biomedical Engineering, University of Strathclyde, 106 Rottenrow East, Glasgow, U.K.
NHS Greater Glasgow & Clyde, Glasgow, U.K.
Keywords: Motion Estimation, Automatic Segmentation, Computer-assisted Diagnosis, Fibre-optic Endoscopy, Vocal
Cord Motion.
Abstract: Full movement of the vocal cords is necessary for life sustaining functions. To enable correct diagnosis of
reduced vocal cord motion and thereby potentially enhance treatment outcomes, it is proposed to objectively
determine the degree of vocal cord paralysis in contrast to the current clinical practice of subjective
evaluation. Our study shows that quantitative assessment can be achieved using optical flow based motion
estimation of the opening and closing movements of the vocal cords. The novelty of the proposed method
lies in the automatic processing of fibre-optic endoscopy videos to derive an objective measure for the
degree of paralysis, without the need for high-end data acquisition systems such as high speed cameras or
stroboscopy. Initial studies with three video samples yield promising results and encourage further
investigation of vocal cord paralysis using this technique.
The co-ordinated movement of the vocal cords in the
human throat facilitates breathing, swallowing and
voice production. Partial or complete paralysis of the
vocal cords adversely affects these vital functions. In
order to appropriately treat this condition, it is
essential to determine the degree of paralysis as
accurately as possible. However, in the current
clinical practice, the judgement of the extent of
paralysis is made subjectively by visual inspection
of the vocal cords using endoscopy. It is therefore
challenging for clinicians to ascertain and evaluate
the paralysis, particularly in the case of slight partial
paralysis. Therefore, an objective assessment
technique based on video processing is proposed in
this paper, to automatically quantify vocal cord
paralysis, in order to aid and enhance current
diagnostic practices.
A number of approaches have been developed to
quantify vocal cord motion such as Glottal Area
Waveform or GAW (Panek et al., 2015; Woo, 2014;
Gonzalez et al., 2013), phonovibrography
(Lohscheller et al., 2008), kymography (Švec and
Schutte, 2012), glottography (Karakozoglou et al.,
2012), spatiotemporal analysis (Zhang et al., 2007)
etc. Most of the research studies have focussed on
quantitative assessment of vocal cord vibration
during voice production. The high frequency (100-
250Hz) vibrations are visualised using high speed
cameras with frame rates over 2000 frames per
second or by using stroboscopy. The latter technique
involves illuminating the vocal cords periodically
with bright flashes of light to produce the effect of
viewing the vibration in slow motion. The rigid
stroboscope inserted orally is the most commonly
used endoscope in these studies because good
quality images can be obtained (Verikas et al.,
2009). Such acquisition systems tend to be used
primarily in specialised voice clinics due to the need
for specialist expensive equipment for the technique
and its recording. In the UK, the majority of cases
are examined and diagnosed with the flexible fibre-
optic endoscope by observing the opening
(abduction) and closing (adduction) movements of
the vocal cords, which are slow enough to be
observed by the human eye and captured using an
ordinary 25 frame per second camera. Moreover, the
Menon R., Petropoulakis L., Soraghan J., Lakany H., MacKenzie K., Hilmi O. and Di Caterina G.
Automatic Quantification of Vocal Cord Paralysis - An Application of Fibre-optic Endoscopy Video Processing.
DOI: 10.5220/0006231001080113
In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 108-113
ISBN: 978-989-758-215-8
2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
nasal insertion of the endoscope allows the vocal
cords to be viewed in a natural position and is better
tolerated by patients.
Therefore, in our study we aimed to exploit the
flexible fibre-optic endoscope videos to analyse
vocal cord abduction and adduction, in order to
derive a measurable descriptor of vocal cord
paralysis. To the best of our knowledge, an
automated algorithm developed for this purpose has
not been reported yet in the literature.
The algorithm proposed in this study caters to
two main challenges. Firstly, the techniques for
quantification of vocal cord motion using vibration
analysis cannot be directly applied to the slower
abduction/adduction movements of paralysed vocal
cords. For example, the accuracy of GAW based
methods and the phonovibrography is limited by the
precise identification of the glottal midline
(Karakozoglou et al., 2012). Determining the
midline becomes challenging when there is a shift in
the position of the arytenoids (anatomical structures
bordering the posterior side of the glottal area in a
laryngeal image). Moreover, the normal vocal cord
may cross the midline when adducting, in an attempt
to compensate for the reduced motion of the
abnormal vocal cord. For these reasons, for the work
presented in this paper, we resorted to a technique
that was not based on the identification of the
midline or glottal area. The movement of each vocal
cord is tracked using an optical flow algorithm and
features are extracted from the resulting flow vector
patterns. In (Zorrilla et al., 2012), a block matching
technique has been used to differentiate between
normal and paralysed vocal cords but the degree of
paralysis was not measured by the authors.
Secondly, the videos acquired using the flexible
fibre endoscope are of poorer image quality than
those recorded with the rigid laryngoscope.
Therefore, video pre-processing is required to
remove artefacts and enhance the image frames to
enable proper segmentation of the vocal cord edges.
The remainder of the paper is organised as
follows: Section 2 explains the methodology for data
acquisition, pre-processing, ROI detection and
motion estimation using the optical flow technique.
Section 3 contains the results and discussion.
Finally, Section 4 concludes the paper and provides
the course of future work.
An outline of the methodology is provided in
Figure1 and is explained in detail in the following
2.1 Data Acquisition
Routine clinical video data were acquired with the
consent of the subjects. The motion of the vocal
cords was acquired by inserting a flexible fibre-optic
endoscope through the nose and recording the scene
using a 25 frame per second camera. RGB video
frames of resolution 768x576 were produced. Figure
2 provides some sample frames of the raw data. The
subject was asked to phonate making an "ee" sound,
followed by taking a deep breath. This sequence was
performed at least twice. A database consisting of 10
videos of approximately 30 seconds’ duration each
of normal and paralysed vocal cords has been
created in our study so far. In this paper, we use 3
videos (2 normal and 1 severe right palsy cases)
from this database, with an aim to provide a proof of
concept for automated processing of fibre-optic
videos and quantification of left-right motion
symmetry of vocal cords.
Figure 1: Overview of proposed technique.
Figure 2: From left to right - selected frames from raw
video sequence of right vocal cord palsy showing
maximally adducted to maximally abducted positions, and
zoomed region showing the right and left vocal cords,
marked with ‘R’ and ‘L’, respectively.
2.2 Video Pre-processing
After extracting the image frames from a video, a
sequence of frames representing abducted (opened)
vocal cords were manually selected. This sequence
of frames was provided as input to the automated
ut video
Automated glottal area segmentation
Vocal cord ed
e tracin
Motion estimation using Optical Flow
Quantitative paralysis assessment
Automatic Quantification of Vocal Cord Paralysis - An Application of Fibre-optic Endoscopy Video Processing
algorithm developed in this study using MATLAB.
A honey-comb structure is observed in the
original image frames as seen in Figure 3. This
artefact is produced due to the sub-sampling of the
scene by the amount of glass fibres present in the
fibre-optic bundle. It was eliminated by spectral
filtering using a star shaped band stop filter (Winter
et al., 2006). The filtered image was smoothed with
a wiener filter. Figure 3 shows the resultant image.
The next step in the pre-processing stage served
the purpose of automatically stabilising the video as
well as cropping the region of interest from each
frame. Video stabilisation is required to minimise
the translational movement of the vocal cords from
frame to frame due to the motion of the larynx itself
or that of the endoscope. Since the structures in the
larynx are mostly pink or red coloured, only the red
channel was used for data processing from this stage
onwards. The technique involved manually selecting
a template containing the region of interest (ROI) in
the first frame and then applying normalised cross
correlation to find the best match for this template in
the second frame. Subsequently, the ROI located in
the second frame was used as the template to search
for the ROI in the following frame, and then the
process was automatically repeated for all the
frames. This resulted in a new video sequence where
every frame comprised the ROI centred in the frame.
Figure 4a provides a sample image of normal vocal
cords from a pre-processed video sequence.
2.3 Glottal Area Segmentation
The glottal area can be segmented with the
knowledge that it appears darker than the
surrounding anatomical structures due to limited
illumination. A two-phase procedure was used to
segment the glottal area. In the first phase, a
preliminary segmentation of the glottal area was
obtained by thresholding using a non-linear
transform, followed by the use of morphological
operations including dilation, boundary object
removal, hole filling and selecting the largest object
in the image. In the second phase, the segmented
regions were used as masks to provide an initial
contour for an active contour algorithm (Kass,
1988), to identify the glottal area boundaries more
Figure 3: Left- cropped section of original RGB image to
illustrate the honey-comb structured artefact caused by the
fibre-optic bundle; Right: pre-processed image with
artefact suppressed.
A non-linear transform, shown to be effective in
the presence of uneven illumination (Andrade-
Miranda et al., 2015) was used for thresholding the
image. Consider an image with intensity I(x,y) [0,
255], where x = 1,2,…,N and y = 1,2,…,M denote
the number of a pixel in the horizontal and vertical
directions, i.e., column and row numbers, resp. The
transformed image is computed as follows:
255 ∗ 
The factor L
accounts for the row-wise varying
lighting conditions. The parameters α and γ were
determined empirically to be 1.5 and 2, respectively.
Figure 4b illustrates the binarised image using this
technique. Note the glottal area localised in the
centre of the image.
Thereafter morphological operations were
performed on the binarised images, commencing
with dilation operation. The images were then
complemented and boundary object removal was
performed because the glottal area is not expected to
be near the image borders, as the ROI has been
centred in the frames. Consequently, holes were
filled and finally all the objects except the largest
one were erased. The resultant image after all the
morphological operations is shown in Figure 4c.
Active contours have shown to be successful in
glottal area segmentation and was therefore adopted
as the technique for the final segmentation
(Karakozoglou et al., 2012; Yan et al., 2006). The
method is an energy minimisation scheme that is
used to detect the boundary of objects by curve
evolution influenced by internal and external factors
(Kass, 1988). The energy of a curve
= (x
, y
) is
given as:
BIOIMAGING 2017 - 4th International Conference on Bioimaging
= 
The internal spline energy, E
, provides a
measure of the tension and rigidity of the curve
during bending. E
results in the curve being
pulled towards lines, edges and corners. E
represents the energy of the external constraint
forces that influence the curve being attracted to
local minima. The algorithm is initially provided as
input a contour that closely matches the boundary to
be detected; thereafter the curve is deformed by
iteratively minimising its energy. Figure 4d provides
the final contour bordering the glottal area detected
by the active contour algorithm.
2.4 Vocal Cord Edge Tracing
In order to discard non-vocal-cord structures
bordering the segmented area, empirically
determined values of 40% and 5% of the vertical
contour length were used to erase the top and bottom
sections, resp., of the extracted boundary. This
resulted in two curves, each corresponding to one
vocal cord edge, as shown in Figure 4e. Note the left
edge of the extracted boundary corresponds to the
anatomic right vocal cord edge and vice versa.
2.5 Optical Flow Analysis
The movement of the vocal cord edges between
successive frames were computed using the optical
flow algorithm, which provides an approximation to
the velocity field associated with each pixel in an
image sequence. By assuming that pixel intensities
are translated spatially between consecutive time
frames, the velocity of a pixel can be computed
using a least squares estimation (Barron et al., 1994)
over a window of neighbouring pixels. For each
block the following squared error is minimised:
+ 
+ 
where, I
and I
are spatial image intensity gradients,
the intensity gradient over time, and v
and v
horizontal and vertical pixel velocities, resp. W is a
weighting function to focus on constraints centred in
the window and is implemented as a 5x5 kernel with
1D weights (0.0625, 0.25, 0.375, 0.25, 0.0625) in the
horizontal and vertical directions. The arrows, in the
enlarged view of the image in Figure 4f, depict the
motion velocities of the vocal cord edges.
(a) (b)
(c) (d)
(e) (f)
Figure 4: Demonstration of methodology with a sample
image frame of normal vocal cords: (a) Pre-processed
image (b) Image thresholded using non-linear thresholding
(c) Result after morphological operations on image b (d)
Segmented glottal area by applying active contour method
using image c as initial mask (e) Left and right vocal cord
edges (note that the edge appearing on the left side of the
image is the anatomical right vocal cord edge) (f) zoomed
view of flow vectors indicating motion of vocal cords
compared to the previous frame in the image sequence.
2.6 Quantitative Assessment
In order to quantify the degree of paralysis, the mean
value of the magnitude of the optical flow vectors
for each vocal cord was computed in every frame.
This produced two vectors, each representing the
mean flow magnitudes per frame for the left and
right vocal cords. The vectors, which can be plotted
as one-dimensional signals as depicted in Figures 5a
Automatic Quantification of Vocal Cord Paralysis - An Application of Fibre-optic Endoscopy Video Processing
and 5b, follow the change in average flow
magnitude in the image sequence. Each signal can
be considered as a signature or pattern of the motion
of a vocal cord. A feature known as the waveform
length, which has been widely used in EMG signal
processing (Hudgins et al., 1993), was then
calculated for the left and right sides. It is the
cumulative length of a signal and provides a
measure of waveform complexity. Finally, the
contribution of each vocal cord to the overall motion
is computed by the following equation:
× 100%
where, C
represents the contribution of the left vocal
cord edge to the overall motion occurring in the
image sequence, WL
and WL
are the waveform
lengths of the left and right vocal cords,
respectively. Similarly, C
can be calculated. Normal
vocal cords move in synchronisation with each other
and therefore motion symmetry can be used as an
indicator of normal functioning.
The results from applying the proposed algorithm to
two normal cases and one right palsy patient are
provided in Figure 5. The plots in Figures 5a and 5b
show the mean flow magnitudes for individual vocal
cords. It is observed that the blue waveform in the
graph in Figure 5b, associated with the right vocal
cord of the palsy subject, is smaller than the red one.
An objective measure of the degree of paralysis is
provided in Figure 5c. The measure depicts the
contribution of a vocal cord to the total motion in the
image sequence and is expressed in percentage. It is
obvious that the normal cases demonstrate almost
equal contribution by both vocal cords, whereas the
palsy case shows reduced motion of the right vocal
While these results appear promising, further
analysis of more videos needs to be performed in
order to derive a calibrated measure that corresponds
to the degree of paralysis identified by clinicians.
This shall be done in future work and validation of
the results by comparing with subjective evaluation
by experienced clinicians will also be performed.
Moreover, to achieve a robust quantitative
assessment tool, other features that can be extracted
from the motion vectors shall also be investigated.
In order to prove the advantage of our method
over other midline based approaches such as the
GAW, a similar measure of contribution to motion
using the waveform length was computed - but
instead of using the motion magnitudes as the
signature, the glottal area waveforms of the left and
right sides were used to calculate the waveform
length. The left and right sides were determined
automatically by fitting an ellipse on the segmented
glottal area and assigning its major axis length as the
midline of the glottal area (Panek et al., 2015). As
can be observed in Figure 6, the glottal area
technique is inappropriate for identifying paralysis
of the vocal cords.
(a) Subject: Normal1
(b) Subject: Palsy
(c) Left to right: Normal1, Normal2, Palsy
Figure 5: Results- (a) Plot of average flow magnitudes for
a normal case (subject id Normal1) for right (blue curve)
and left (red curve) vocal cords; (b) Similar plot for right
palsy patient; (c) Quantitative measure of paralysis.
BIOIMAGING 2017 - 4th International Conference on Bioimaging
Figure 6: Quantitative measures using Glottal Area
Waveform (GAW) as a signature of motion in the image
Our work emphasises the use of data acquisition
procedures which are widely used in hospitals
worldwide, in order to develop a generalisable
technique that can be seamlessly integrated with
current clinical practices, rather than utilising state-
of-the-art systems for developing techniques that
have limited scope of implementation outside the
laboratory. Towards this end, we aimed to utilise the
commonly used fibre-optic videos in order to assess
abduction/adduction movements of the vocal cords
as done by clinicians in the current clinical practice.
However, the diagnosis can be enhanced by
introducing quantitative measures, potentially being
useful for trainees or for very challenging cases,
particularly where the degree of paralysis is subtle or
where there may be subtle pathology of a vocal cord
affecting its movement.
Our results are very encouraging to further
analyse fibre-optic endoscopy videos for
quantification of vocal cord paralysis using motion
estimation techniques.
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Automatic Quantification of Vocal Cord Paralysis - An Application of Fibre-optic Endoscopy Video Processing