REVIEW ARTICLE

Advances in laryngeal imaging

Antanas Verikas Æ Virgilijus Uloza ÆMarija Bacauskiene Æ Adas Gelzinis ÆEdgaras Kelertas

Received: 5 December 2008 / Accepted: 7 July 2009 / Published online: 19 July 2009

� Springer-Verlag 2009

Abstract Imaging and image analysis became an

important issue in laryngeal diagnostics. Various tech-

niques, such as videostroboscopy, videokymography, dig-

ital kymograpgy, or ultrasonography are available and are

used in research and clinical practice. This paper reviews

recent advances in imaging for laryngeal diagnostics.

Keywords Larynx � Image analysis � High-speed video �Digital kymography � Videostroboscopy �Glottal area detection

Introduction

Attempts to examine the human larynx date back more than

150 years [1]. In 1854 Manuel Garcia, a vocal music tea-

cher, examined his own larynx using a dental mirror and a

hand mirror, and published his observations in 1855 [1].

Only two years latter, Turck, a neurologist from Vienna,

and Czermak, a physiologist from Prague, re-invented the

same technique and demonstrated its use in medicine [1].

Imaging technologies applicable in laryngology developed

enormously since then.

Nowadays, the diagnostic procedure of laryngeal dis-

eases in clinical practice is rather complex and is based on

the evaluation of patient’s complaints, history, and data of

instrumental as well as histological examinations. During

the last two decades a variety of imaging techniques for the

examination of the larynx and obtaining objective mea-

surements of voice quality have been developed [2, 3].

Evaluation of larynx has improved significantly with the

establishment of computer tomography (CT) and magnetic

resonance imaging (MRI), as these technologies provide

insights into the endoscopically blind areas and reveal the

depth of tumor infiltration [4]. These technologies may be

beneficial in staging laryngeal carcinoma and planning the

most appropriate surgical procedure [5–8]. Ultrasonogra-

phy is useful in cases of larger laryngeal lesions and may

have some role in screening for unilateral vocal fold

pathologies. At the same time, further fine-tuning of the

technique may be necessary [9, 10].

Laryngeal still images, image sequences, voice signal,

and patient’s questionnaire data can be considered as the

main information sources to characterize human larynx.

Nowadays, automated analysis of voice is increasingly

used for detecting and screening laryngeal pathologies

[11–18]. It was demonstrated that even telephone-based

voice records may be used for screening of laryngeal

disorders [13]. It was also demonstrated that even ques-

tionnaire data can be an important source of information

in screening for laryngeal disorders [19]. There were very

few attempts to create systems for automated analysis of

still laryngeal images. More work has been done in the

field of analysis of image sequences obtained from

A. Verikas (&)

Intelligent Systems Laboratory, Halmstad University,

Box 823, 301 18 Halmstad, Sweden

e-mail: antanas.verikas@hh.se

A. Verikas � M. Bacauskiene � A. Gelzinis � E. Kelertas

Department of Applied Electronics,

Kaunas University of Technology,

Studentu 50, 51368 Kaunas, Lithuania

e-mail: marija.bacauskiene@ktu.lt

A. Gelzinis

e-mail: adas.gelzinis@ktu.lt

V. Uloza

Department of Otolaryngology,

Kaunas University of Medicine, 50009 Kaunas, Lithuania

e-mail: virgilijus.uloza@kmuk.lt

123

Eur Arch Otorhinolaryngol (2009) 266:1509–1520

DOI 10.1007/s00405-009-1050-4

high-speed cameras, laryngeal videostroboscopy or

videokymography.

Different tasks can be pursued with laryngeal imaging.

Obtaining images of high quality and resolution, revealing

the detailed morphology of the glottal structures, is one of

the main tasks. Such images are essential in making correct

diagnosis and choosing the treatment. Quantitative char-

acterization of the vibratory behavior of the vocal folds is

another important task. Characterization in the form of a

rather small set of physically interpretable parameters is

desired. Several indices, describing the glottal wave form,

are usually used for the characterization. Figure 1 presents

three laryngeal images recorded from the same patient with

cyst in the left vocal fold. The two video-laryngo-strobo-

scopic images on the left-hand side of Fig. 1 provide

mainly the ‘‘functional information’’ about the vibratory

function of the vocal folds and the glottal closure. The

image shown on the right-hand side of Fig. 1 was obtained

via direct microlaryngoscopy, and provides more detailed

information about structural changes in the vocal fold and

morphological peculiarities of the cyst which is funda-

mental in making the correct diagnosis. This image was

used to quantitatively characterize the distribution of color,

the geometry of the edges of the vocal folds, and the

texture.

Specific tasks are often pursued by applying ultrasonic

imaging. Autofluorescence laryngoscopy often focuses on

revealing early malignant changes, while CT and MRI help

to reveal the depth of tumor infiltration. It is noteworthy

that other methods, such as inverse filtering and electro-

glottography (EEG), are also often used to examine the

vocal fold vibrations and to calculate glottal wave form

parameters [20, 21]. The aim of this work was to review the

state-of-the-art imaging and image analysis techniques

used in laryngoscopy. 3D imaging technologies, such as

CT or MRI, were not considered in this review.

Analysis of still images

An image analysis-based technique for automated catego-

rization of manually marked suspect lesions into the

healthy and diseased classes has been proposed [22]. The

categorization is based on textural features extracted from

co-occurrence matrices [23, 24] computed from manually

marked areas of vocal fold images, taken by a CCD cam-

era. The classification accuracy of 81.4% was reported

when testing the technique on a very small set of 35 ima-

ges. A set of 785 color laryngeal images obtained via direct

microlaryngoscopy has been used in studies presented in

literature [25, 26]. The classification accuracy of over 94%

was achieved when categorizing the images into one

healthy and two pathological (nodular and diffuse) classes.

Figure 2 presents characteristic examples from the three

decision classes considered. As can be seen from Fig. 2,

examples representing the three classes are rather dissim-

ilar. However, it is worth noting that due to a large variety

of appearance of vocal fold mass lesions, classification can

sometimes be difficult even for a trained physician [27].

Figure 3 exemplifies some results of the automated anal-

ysis. Images were automatically segmented into regions of

homogenous color. The region boundaries found are shown

with thin black curves. Two lines in the image on the right-

hand side of Fig. 3 delineate the polyp. Lines in the other

two images approximate the edges of vocal folds. As can

be seen, one of the lines was misplaced. When categorizing

the same set of images into seven classes (one healthy and

six pathological), the classification accuracy of over 80%

was reported [28]. Image texture, distribution of color, and

geometry of edges of vocal folds were the types of features

used for the categorization. The investigation showed that

color was among the most discriminative types of features.

Hanson et al. [29] used color, an average value of the

normalized red component r given by Eq. 1, to quantify the

degree of erythema

r ¼ R

Rþ Gþ Bð1Þ

where R, G, and B are three components of the color

images recorded by the color CCD camera. Five different

areas were manually selected from each laryngeal image to

estimate the r component. The value of the r component

computed for normal subjects was compared to the r

component values computed for patients with chronic lar-

yngitis. The examination showed that the r values for

patients with chronic posterior laryngitis were significantly

higher than the r values computed for normal larynges. It is

worth noting that the camera was color-balanced before

Fig. 1 Images obtained from

the same patient by

videostroboscopy (left and

middle) and direct

microlaryngoscopy (right)

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each recording. However, variations in illumination,

geometry, and appearance of vocal folds have not been

taken into consideration.

Imaging is successfully used in indirect autofluores-

cence and fluorescence (5-ALA-induced PPIX, for exam-

ple) laryngoscopy [30–34]. Autofluorescence laryngoscopy

is based on the fact that normal cells emit green fluores-

cence when exposed to blue light, while precancerous or

cancerous lesions display a significant loss of green fluo-

rescence and appear reddish. Autofluorescence is usually

induced by filtered blue light of a xenon lamp or light of a

blue laser. A color CCD camera is usually used for

recording. Autofluorescence and fluorescence imaging

helps to detect borderlines between tumors and healthy

tissue. It is argued that autofluorescence endoscopy shows

greater sensitivity in the detection of precancerous or

cancerous lesions, compared to standard laryngomicros-

copy [32]. However, the image analysis procedures used

are limited to image visualization. Quantification of color,

texture, and shape of lesions and normal tissue could help

in more accurate categorization of lesions as well as in the

follow-up procedures.

Hsiao et al. [35, 36] and Shau et al. [37] used color

Doppler ultrasound imaging to assess laryngeal phonation

function non-invasively. Vocal fold mucosal wave velocity

and elasticity of vocal folds were quantified on the basis of

color Doppler imaging. The mean vocal fold displacement

velocity was assessed from the pseudocolor codes of the

ultrasound image. A commercially available high resolu-

tion ultrasound scanner was used for imaging. The authors

pointed out that the technique was applicable for the

evaluation of the phonation function affected by vocal

disorders that alter the vibration of the mucosa. Ultrasonic

imaging was also used by Niimi et al. [38] to obtain

quantitative measurements of the vertical movement of the

larynx. A linear type ultrasonic tomograph with 128

ultrasound emitting elements aligned on a straight line

served as an imaging device, ensuring a spatial resolution

of 1 mm.

In [39], image analysis-based procedures were used to

make relative measurements of some laryngeal parameters.

Analysis of image sequences

Videostroboscopy

Videostroboscopy is one of the standard methods used to

examine moving objects. Laryngo-videostroboscopy is

used extensively for the analysis of vocal folds and in the

clinical practice for diagnosing voice disorders [40–44].

For example, videostroboscopy is a well-established tech-

nique for measuring the glottal gap or examining the glottic

closure [41, 45, 46]. Laryngo-videostroboscopy is able to

reveal a number of abnormalities, including abnormalities

of laryngeal structure, absence of vibration, and vibratory

asymmetry. Flashing light is used to illuminate an object in

stroboscopy. When the flashes are synchronized with the

vocal fold vibrations, a stationary view of the vocal folds is

obtained.

However, the single-flash-timing laryngeal videostro-

boscopy has a limitation that it is effective only when vocal

fold vibrations exhibit only one single fundamental fre-

quency. Multiple tones (fundamental frequencies) may be

recorded in the presence of some diseases, such as polyps,

nodules, or cysts [47]. In such cases, a clear view of the

vibrating vocal folds cannot be obtained with the single-

flash-timing laryngeal videostroboscopy. A multiple-flash-

timing laryngeal videostroboscopy technique was proposed

by Deguchi et al. [47], in order to deal with such cases.

Multiple light emitting diodes (LED) are used as illumi-

nation sources.

Fig. 2 Images from the nodular(left), diffuse (middle), and

healthy (right) classes

Fig. 3 Images illustrating the

results of automated analysis

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Schade et al. [48] proposed using the short-interval

color-filtered double strobe flash-stroboscopy to obtain

quantitative measurements of vocal fold movements. With

this technique a double exposure is accomplished in each

video frame. Thus, information about the vocal fold

velocity is available from a single image obtained with this

technique.

Among other issues, the usefulness of laryngeal trans-

illumination in combination with stroboscopy for auto-

mated endoscopic images-based quantitative measurement

of glottal area function was studied in [49]. The results

showed that the utilization of color information (available

due to transillumination) facilitates automated quantitative

measurements of the glottal area function and the detection

of the glottal contour area. In [50] image sequences

recorded with the stroboscopy system were used to mea-

sure the glottic angle and the angular velocities of vocal

fold abduction and adduction. The authors indicated that

semi-automated edge tracking would be an important

improvement of the technique.

In clinical practice, decision-making is quite often based

on subjective evaluation of video data. Quantitative mea-

sures of motion and geometry of vocal folds can provide

objective information and may be useful in planning

medical treatment and tracing progress over time. Video-

strobokymography introduced in [45] creates multiple

kymograms (multi-line kymography) based on multiple

lines of interest from recorded successive frames of stro-

boscopic video images, and enables calculation of some

quantitative parameters. Two quantitative parameters,

namely open quotient and asymmetry index, are calculated

from video-strobokymograms. To facilitate the analysis,

images are rotated to make the glottal axis vertical before

creating a kymogram. To create a kymogram from a

sequence of images, one—and always the same—line is

taken from each image. The lines are then combined into a

new image showing the time course of the fold vibrations.

Figure 4 shows an example of a video-strobokymogram

obtained with the ‘‘EndoSTROB’’ device (XION medical).

A wave of mucous membrane and a clear closure phase are

well recognizable.

Videokymography

A special CCD camera-based video system for videoky-

mography was developed [51, 52]. The system can function

in two modes. In the first mode, the system functions as an

ordinary video camera. In the second—line-imaging—

mode, videokymographic images are recorded. Thus, vocal

fold vibration frequency and amplitude, left–right asym-

metry, and open and closed phases of the glottal cycle can

be quantified. One drawback of the system is that only a

single kymogram can be obtained during one examination.

A new-generation videokymographic system has been

recently introduced [53, 54]. Two CCD sensors are used to

provide simultaneously a laryngoscopic and kymographic

image for clinical vocal fold examination. A color CCD

camera is used for obtaining a laryngoscopic image, while

a high-speed line-scan CCD is used to capture a kymo-

graphic image at a selected position, which is shown on the

laryngoscopic image. Since the color CCD camera is not

fast enough, the laryngoscopic image appears blurred,

while the kymographic image is of high quality, high

signal-to-noise-ration and high spatial and temporal

resolution.

In [55], an approach to quantitative characterization of

videokymographic images was presented. An active con-

tour model-based algorithm tracks the vibration wave in a

videokymographic image, and quantifies its properties in

terms of parameters. The parameters considered are: the

amplitude and period ratios between the right and left vocal

fold, and the ratio between the opening and the closing

phases. Verdonck-de Leeuw et al. [56] studied the effect of

irregular vocal fold vibration on voice quality by simulta-

neously recording and analyzing videokymographic images

and speech signals.

Digital kymography

Vocal fold vibrations occurring during voice production

can be observed with a relatively high spatial and temporal

resolution by applying high-speed imaging techniques [57].

Digital high-speed imaging systems were introduced in

1980s [58–61]. A solid endoscope is usually attached to a

high-speed camera and used to illuminate the target and to

collect the reflected light. In another imaging setup, sug-

gested by researchers from the University of Tokyo, a

flexible fiberscope inserted through the nasal cavity is used

[60, 62, 63]. Such system allows for examining the vocal

Fig. 4 An example of a video-strobokymogram obtained with the

‘‘EndoSTROB’’ device (courtesy: XION medical)

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fold vibrations during running speech including conso-

nants. However, images recorded using a solid endoscope-

based imaging system are of higher quality [62].

At present, videokymography and high-speed imaging

(also referred to as high-speed video or high-speed glot-

tography) are the only available techniques for directly

studying aperiodic vibrations of vocal folds [64]. Such

studies are usually performed by creating and analyzing

kymograms. Kymograms obtained (using the technique

explained in ‘‘Videostroboscopy’’) from a high-speed video

are usually called digital kymograms. Kymography meth-

ods allow for the condensation and quantification of

information generated by a high-speed imaging system. It

is evident that the analysis of the huge amount of infor-

mation available from high-speed imaging may help cli-

nicians in gaining a better insight on the effects various

treatments have on vocal folds [65].

By using appropriate image analysis techniques a

parametric description of the vocal fold dynamics can be

obtained from digital kymograms. Digital images lend

themselves for calculating various quantitative measures

characterizing the vocal fold dynamics. Parametric

description of vocal fold dynamics is important in clinical

investigation of laryngeal pathologies, and is usually done

by finding the glottal area via image segmentation and

plotting the glottal area as a function of time—computing

the glottal area waveform (GAW). This approach is often

criticized on the basis that asymmetries between the

oscillation dynamics of the left and the right vocal fold are

not taken into consideration [66]. Single-line or multi-line

kymography [67], evaluating the time-dependent distance

of a specific point/points on the vocal fold edge to the

longitudinal glottal axis (experimental 1D trajectory/tra-

jectories), overcomes this drawback. Different symmetry/

asymmetry measures can be used to assess the oscillations

of vocal folds [68]. For example, vocal fold vibration

irregularities were studied in [69] based on high-speed

video and digital kymography. Two types of irregularities

were studied: a vibration frequency difference between

the right and left vocal folds (horizontal asymmetry) and

the vertical asymmetry, measured on one side between the

ventral and dorsal fold third. The aim was to obtain inde-

pendent quantitative measurements for voice therapy

planing and control. However, the symmetry measures did

not incorporate details about the time-varying shape of the

vocal fold edges [66].

Lohscheller et al. [70] identified the following main

requirements for an image analysis system designed to

investigate vocal fold vibrations based on high-speed video:

1. robust segmentation of glottis;

2. edge identification for the left and the right vocal fold,

aiming to exploit this information for distinguishing

between the influences of the left and right vocal fold

on the laryngeal dynamics (asymmetry of the oscilla-

tion directions, velocities, and amplitudes);

3. high edge detection accuracy;

4. high computation speed;

5. minimal manual intervention.

One advantage of high-speed video endoscopy over

videokymography is the possibility to remove errors caused

by involuntary motion of the endoscope. For example,

Deliyski [71, 72] presents an image analysis-based tech-

nique to compensate for endoscopic motion in high-speed

video. The technique is based on the assumption that the

difference between the endoscopic and vocal fold dynam-

ics is sufficient to accurately estimate the endoscopic

motion in relation to the vocal folds.

Though expensive, high-speed video endoscopy systems

are commercially available. However, extraction, quanti-

fication, and presentation of essential diagnostic informa-

tion to the clinician in an effective interpretable form are

still rather problematic [71]. Accurate extraction of glottal

area, compact quantitative representation of information

available from kymograms, accuracy of measurements, and

calibration are issues important for all the techniques dis-

cussed above. Therefore, we review works related to these

issues in the following sections.

Detecting glottal area

Simple thresholding techniques are often used in the image

segmentation process to extract the glottal area [66, 73–

75]. Such image thresholding often results in error, espe-

cially when the image quality is not high. False and mul-

tiple vocal fold contours may appear in different frames.

Figure 5 presents several examples of images taken from

the same image sequence recorded with a laryngo-video-

stroboscope. It is evident that due to illumination variation

and a large variability of the vocal fold appearance, simple

thresholding techniques will often fail, especially in cases

when the opening is narrow. Determining the presence of

glottal area in an image automatically is not a trivial issue.

One has also bear in mind the fact that vocal folds can be

occluded by other structures. In [76], a semi-automatic

technique based on gray level derivative is applied to

detect the glottal edge. When parameters of the technique

are determined, the edge detection and calculation of the

glottal area is performed automatically.

More advanced techniques, like region growing [77, 78],

active contour models or ‘‘snakes’’, are sometimes applied

for extracting the glottal area [79, 80]. The approach

proposed in [77] is based on image thresholding and

morphological operations followed by region-growing.

A histogram-based image thresholding is applied,

Eur Arch Otorhinolaryngol (2009) 266:1509–1520 1513

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assuming that the image histogram follows the Rayleigh

distribution. The thresholding technique is similar to that of

Otsu [81], except that the threshold is determined by

minimizing the pixel classification error instead of maxi-

mizing the between-cluster variance (when partitioning the

histogram into two clusters) in the Otsu’s technique. The

image processing technique developed in [70] for extract-

ing vocal fold edges from high-speed video sequences is

based on the seeded region-growing algorithm. Having the

edges, the so-called vibration trajectories characterizing

deflections of the vocal folds perpendicular to the glottal

main axis are defined and used in the analysis. The success

of the image segmentation process highly depends on the

choice of an appropriate region homogeneity criterion. The

technique requires the manual definition of one or several

seed points. The manual intervention enables to achieve a

high processing speed amounting to 98 images/s. In total

186,000 images were analyzed. The authors claim that the

segmentation accuracy exceeds the one obtained by human

experts.

Active contour models are based on the minimization of

some energy function, usually exploiting information on

gray-level gradient. Additional information expressing our

prior knowledge can also be incorporated into such func-

tions [80]. In [82], the active contour-based algorithm has

been used to detect the pseudo-glottis and track its defor-

mations within a high-speed image sequence of the vibra-

tion pattern of the substitute voice generating element. The

algorithm is initialized with a region of interest extracted

by a region-growing algorithm. Active contour models,

however, are rather time-consuming. A rather generic

glottal area detection algorithm has been recently proposed

in [83]. The algorithm is based on several image processing

techniques, and does not require any initialization.

Characterizing vibrations of vocal folds quantitatively

Recently, high-speed imaging combined with advanced

image analysis is considered as the most promising

approach to explore the relation between voice disorders

and vibrations of vocal folds [61, 70, 84–86]. However,

there are no standard procedures to characterize vocal fold

vibrations and in clinical practice high-speed videos are

largely interpreted subjectively. To characterize the

vibrations of vocal folds, several positions on the vocal fold

edges are selected, usually dorsal, medial, and ventral [87]

(see Fig. 6), and trajectories (experimental trajectories) for

these points are extracted and used for further character-

ization. Often, only the medial trajectory is utilized [88–

90]. GAW is used instead of the trajectories sometimes.

The regularity and symmetry of vocal fold vibrations are

usually characterized quantitatively by computing various

measures such as amplitude and phase asymmetry, and

instability of the fundamental frequency [69, 87, 91].

Yan et al. [74, 92] have recently suggested using the

Hilbert transform-based characterization. To describe the

experimental trajectory u(t), the complex signal /(t) is

defined as

/ðtÞ ¼ uðtÞ þ jHfuðtÞg ð2Þ

where j is the imaginary unit and H{u(t)} stands for the

Hilbert transform of u(t):

HfuðtÞg ¼ 1

p

Zþ1

�1

uðsÞs� t

ds ð3Þ

Thus, the Hilbert transform is obtained by convolving

u(t) with a time function {-(1/pt)}, i.e. by filtering with a

Fig. 5 Examples of images taken from the same image sequence recorded with a laryngo-videostroboscope

Fig. 6 A frame of vocal folds obtained by videostroboscopy

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90�-phase-shift filter or quadrature filter. Having the

Hilbert transform, the envelope r(t) [88] or the Nyquist

plot [92] are computed to characterize the dynamics of

vocal folds

rðtÞ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffifR½/ðtÞ�g2 þ fI½/ðtÞ�g2

qð4Þ

where R[] and I[] stand for the real and the imaginary part

of the complex signal.

In [92], the Nyquist plot-based analysis is used to

characterize the dynamics of vocal folds. The approach is

applied to both the normalized glottis area waveform

extracted from high-speed digital images and the acoustic

signal. The Nyquist plot or the analytic phase trace maps an

instantaneous trace of the vibratory signal. The phase trace

repeats itself from period to period for a periodic signal.

Scattering in the Nyquist plot is used as a measure of

irregularity of the vocal fold vibrations.

An interesting approach to the characterization of vocal

fold vibrations has been recently proposed by Lohscheller

et al. [84, 93]. The authors suggest representing vocal fold

vibrations by a phonovibrogram (PVG)—a 2D image of

distances of vocal fold edges from the glottal main axis.

The distances are measured at M points equidistantly

spread over the main axis between the posterior and the

anterior glottal ending points. One image frame generates

one column in the 2D image. The image consists of

2M ? 1 rows—one middle point concatenating M distance

values to the edges of the left and the right vocal fold,

respectively. PVG images are considered as vocal fold

vibration fingerprints, as a specific vibration pattern creates

a specific geometrical form within a PVG. The authors

suggest characterizing the PVG geometry by ten regression

lines represented by a set of ten angles.

Granqvist and Lindestad [94] proposed a Fourier analy-

sis-based technique for estimating and visualizing vibration

frequencies in the entire laryngeal area. Vibration frequen-

cies can be evaluated in a specific position defined by an

image pixel or a line in an image, as well as in all pixel

positions. To visualize oscillations, the estimated frequen-

cies are superimposed on an image frame by coloring pixels

corresponding to the oscillating parts of the larynx.

Quantitative characterization in multi-line kymography

Multi-line digital kymography is performed by using many

cross-section lines of the glottis simultaneously [65, 67, 69,

95, 96]. Multi-line kymography is used to reveal glottic

movements differing between the anterior and posterior

commissure. Multi-line kymography is the usual way to

represent the temporal and spatial information on the

vibration amplitude of vocal fold edges. However, such

technique of the representation of the vibration amplitude

is rather redundant and apt to be compromised by mea-

surement errors easily [97]. Liu et al. [97] presented rather

an efficient way to represent information obtained during

multi-line kymography. The authors suggested collecting

the fold vibration amplitude measurements akl made at

different time moments l = 1, 2,..., L and in different

locations k = 1, 2,..., K into a K 9 L matrix A. The matrix

A can be represented as

A ¼XK

i¼1

riuivTi ð5Þ

with ui, vi being the left and the right singular vectors of A

and ri is the corresponding singular value. The vectors ui

and vi reflect spatial and temporal behavior of the fold

vibrations and are called by the authors the spatial and the

temporal eigenfolds. The authors demonstrated that the first

spatial eigenfold reflects the average shape of the vocal

folds, the second eigenfold captures the closing pattern of

the folds, while the third spatial eigenfold reflects the

motion of the vocal folds in the longitudinal direction. The

first temporal eigenfold captures the vibration amplitude

differences between the left and right fold. It is worth

noting that when approximating A with M \ K B L sin-

gular vectors, an optimal approximation is obtained in the

sum of the squared error sense.

Combining imaging and other analysis techniques

A solid endoscope-based high-speed imaging system is

limited to studies of vocal fold vibrations in sustained

phonation. To study the vocal fold vibrations in the sus-

tained phonation and in the production of consonants in

running speech, Kiritani et al. [61, 62] used both the solid

endoscope and the fiberscope-based digital high-speed

imaging systems. High-speed image sequences, speech

waveforms, and EGG signals were utilized. Sakakibara

et al. [63] used a fiberscope-based high-speed imaging

system to study throat singing. High-speed image sequen-

ces, sound and EGG waveforms, and inverse filtering were

utilized in the study. In [58, 98], high-speed image

sequences were compared with synchronized speech

waveforms and EGG signals. Koster et al. [99] synchro-

nously recorded a high-speed image sequence, sound and

EGG and used these three modalities to study voice onset.

The same three modalities have also been used by Bailly

et al. [100] to study the ventricular band vibration and its

interaction with the vocal fold vibration. Henrich et al. [21]

synchronized a high-speed image sequence with an EGG

signal and investigated the suitability of the differentiated

EGG signal for determining the glottal opening and clo-

sure. Granqvist et al. [101], aiming to study the relation-

ship between the vocal fold vibrations and the associated

trans-glottal airflow, recorded high-speed video, EGG, oral

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airflow, oral air pressure, and sound pressure (audio). The

glottal area extracted from the high-speed image sequence

was compared with inverse filtered airflow.

In [102], when studying ‘‘Mongolian throat singing’’,

the kymographic images were manually compared frame

by frame to the sound signal and the high-speed images.

According to Wittenberg et al. [95], an important appli-

cation of simultaneous analysis of high-speed video and

audio signals is searching for glottalizations in the speech

signal. Hertegard and Gauffin [20], by using EGG, inverse

filtered flow waveform, and videostroboscopy, demon-

strated the correspondence between the phases of the

glottal vibration cycle observed in the image sequence and

the inverse filtered flow waveform.

Accuracy of measurements and calibration

Accuracy of measurements of vocal folds on videostrobo-

scopic images was studied in [103]. This included the

investigation of the magnitude of the error of measurement

of the surface areas. The glottal area and the area of the

lesion were studied. The study emphasized the necessity of

correction for differences in magnification and view angle

between images of two recordings, to make a correct

comparison between the images. It was also pointed out

that the measurement error is mainly due to the uncertainty

of the outline of areas with a fuzzy boundary.

To obtain absolute metric spatial dimensions of laryngeal

structures, a high-speed camera system was combined with a

laser projection system [104, 105]. The dimensions of lar-

yngeal structures were determined on digital images. Image

segmentation was applied to extract the glottal area. The

precision of the measurements was limited by non-orthog-

onality of the glottal plane to the optical axis, not exactly

parallel the two laser beams used, and the relatively low

spacial resolution of the camera [104].

A Radon/Hough transform-based technique to calibrate

high-speed image sequences aiming to obtain vocal fold

measurements in a metric scale was recently proposed

[106]. As in [104, 105], an endoscope is equipped with a

laser projection device projecting two parallel lines with a

known distance between them onto the vocal folds. The

distance is the information used for the calibration. The

Radon/Hough transform maps 2D images in such a way

that lines manifest themselves as peaks. The peaks are

detected using image processing procedures.

Discussion

Advanced image analysis procedures are used in laryngeal

diagnostics with increasing frequency. Nonetheless, the

image analysis procedures used are quite often limited to

image visualization, for example, in indirect autofluores-

cence laryngoscopy. Quantification of color, texture, and

shape of lesions and normal tissue could help in a more

accurate categorization of lesions as well as in follow-up

procedures.

High temporal and spacial-horizontal resolution is

achieved in the new generation videokymography system.

One limitation of videokymography is that vibration of the

anterior and posterior parts of vocal folds cannot be

observed simultaneously. One more drawback of video-

kymograpy is the fact that errors caused by the movements

of the endoscope cannot be corrected after recording. In

digital kymograpgy, by contrast, image analysis-based

procedures are available for removing the errors.

High-speed imaging systems are still too expensive to be

widely used in clinical practice. However, regarding the

investigation of vocal fold vibrations, it seems that high-

speed imaging is the most promising approach. Currently,

the low spatial resolution often amounting to 256 9 256 or

less pixels is identified as the main technical limitation of

high-speed imaging systems [70, 107]. A spatial resolution

of at least 1,024 9 1,024 pixels is desired. Higher temporal

resolution would also be very helpful.

The most common temporal resolution of 2,000–4,000

frames/s, currently used in research (in combination with

the spatial resolution of 256 9 256 pixels), is still not high

enough for accurate examination of vocal fold vibrations,

especially for phonations with a high fundamental fre-

quency. For fundamental frequency of 200 Hz and higher,

only a few images are obtained for the open phase of a

glottal area pulse. Thus, the precision of the parameters

calculated from the image sequences is rather limited. Due

to the limited precision and the uncertainty arising in

the glottal area detection, it can be difficult to identify the

source of small variations in values of the parameters. The

variations may occur due to the measurement uncertainty

as well as the treatment applied.

High-speed imaging and digital kymography allow for

extracting numerous image data-based parameters (fea-

tures) that can provide detailed characterization of the

temporal behavior of different parts of the glottis. The

characterization and discrimination power of the parame-

ters can be explored by applying powerful data analysis

techniques. The parameters may enable automated dis-

crimination between various laryngeal disorders and sev-

eral stages of disorders. However, large data sets are

required to assess the validity and reliability of the

parameters. Since high-speed laryngeal imaging systems

are not widely used in clinical practice, gathering large data

sets is rather problematic. Moreover, there are no generally

accepted clinical protocols regarding the use of high-speed

imaging data in laryngology.

1516 Eur Arch Otorhinolaryngol (2009) 266:1509–1520

123

Currently, high-speed imaging is black and white.

Future color high-speed imaging will provide even more

information for studying laryngeal disorders. However,

color imaging requires an increased frame rate and an

increased amount of light. An increased amount of light

may lead to burns in the patient’s oral cavities.

The accuracy and robustness of glottal area detection is

an important issue in quantitative characterization of vocal

fold vibrations, since the robustness of the characterization

is dependent on the robustness of the glottal area detection.

Due to variation in illumination and a large variability of

the appearance of vocal folds, simple thresholding tech-

niques often fail, especially in cases when the glottal

opening is narrow. Therefore, it is difficult to correctly

determine the time moment of gap opening and closure. To

achieve the desired accuracy, semi-automatic glottal area

detection procedures are often utilized, meaning that glottal

area detection techniques sometimes require manual setting

of some parameter values. Therefore, the results of area

detection can be highly sensitive to the operator’s choices.

The results of glottal area detection are also dependent on

the distance between the vocal folds and the endoscope. If

procedures for the compensation of endoscope motion are

not applied, the absolute values of the glottal area cannot

be compared.

Due to a large number of image frames usually recorded

in one sequence during high-speed imaging, fully auto-

mated procedures are highly desirable. Fully automated

procedures, however, require very robust glottal area

detection. This is a complicated task. Due to partial

occlusion of vocal folds by other laryngeal structures, it is

sometimes even impossible to estimate the glottal area

from the images. Advanced data analysis techniques can be

used to provide a value of some certainty measure

regarding glottal area measurement results for a given

image frame. Frames with low certainty values could be

checked manually or skipped, if possible. Thus, high spa-

tial resolution and advanced image analysis techniques can

increase the glottal area detection accuracy and robustness,

and reduce the necessity of manual human involvement.

Acknowledgments We acknowledge the support from The Agency

for International Science and Technology Development Programmes,

Lithuania (COST Action 2103). We acknowledge very helpful

comments from the anonymous reviewer.

Conflict of interest statement There is no conflict of interest.

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