Complex Automatic Determination of Morphological Parameters for Bone Tissue in Human Paranasal Sinuses

Abstract

Introduction
Application of automated analysis currently occupies a leading position in every field of science and technology.
The aim of our study was to provide a complex automatic determination of morphological parameters for bone tissue in human paranasal sinuses.

Materials and Methods
The study involved 50 patients aged 20 to 60, male and female without signs of inflammatory or other pathological processes in the paranasal sinuses (PNSs).

Results
Bone density in a high-contrast image of the section can be determined by fluctuations in colour intensity. Before cleaning, the image is blurred using the Gaussian function. As a result of this operation, the images become less clear and small details merge. An algorithm known as the Connie Border Detector has found widespread use.
The curves denoting the contours can run vertically, horizontally or diagonally at different angles. Detection of the direction of curves passing vertically and horizontally is not complicated, and for curves of the diagonal direction, the Sobel operator is used, with the vertical direction Gy and horizontal Gx as the value of the first derivative. Selection of areas of bone tissue requires the assessment of brightness gradient along the long side of the area. For clarity, this operation was shown graphically.

Conclusion
Within the scope of this work, we have developed a method for an automatic comprehensive assessment of the morphological structure of the PNSs walls with the measurement of bone density and thickness.

Full text

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130
DOI: 10.2174/18750362021140100130, 2021, 14, (Suppl-M1) 130-137
The Open Bioinformatics Journal
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RESEARCH ARTICLE
Complex Automatic Determination of Morphological Parameters for Bone
Tissue in Human Paranasal Sinuses
Alina.S. Nechyporenko1, Radiy Radutny2, Victoriia V. Alekseeva3,*, Ganna Titova4 and VitaliyV. Gargin5
1Department of Systems Engineering, Technical University of Applied Sciences, Wildau, Germany, Kharkiv National University of Radio Electronics,
Kharkiv, Ukraine
2National Aviation University, 1 Liubomyra Huzara Ave., Kyiv03058, Ukraine
3Department of Histology, Cytology, Embryology, Kharkiv National Medical University, Kharkiv, Ukraine
4Department of Internal Medicine, Academician L.T. Malaya, Clinical Immunology and Allergology No. 2, Kharkiv National Medical University,
Kharkiv, Ukraine
5Department of Pathological Anatomy, Kharkiv National Medical University, Kharkiv, Ukraine
Abstract:
Introduction:
Application of automated analysis currently occupies a leading position in every field of science and technology.
The aim of our study was to provide a complex automatic determination of morphological parameters for bone tissue in human paranasal sinuses.
Materials and Methods:
The study involved 50 patients aged 20 to 60, male and female without signs of inflammatory or other pathological processes in the paranasal
sinuses (PNSs).
Results:
Bone density in a high-contrast image of the section can be determined by fluctuations in colour intensity. Before cleaning, the image is blurred
using the Gaussian function. As a result of this operation, the images become less clear and small details merge. An algorithm known as the Connie
Border Detector has found widespread use.
The curves denoting the contours can run vertically, horizontally or diagonally at different angles. Detection of the direction of curves passing
vertically and horizontally is not complicated, and for curves of the diagonal direction, the Sobel operator is used, with the vertical direction Gy
and horizontal Gx as the value of the first derivative. Selection of areas of bone tissue requires the assessment of brightness gradient along the long
side of the area. For clarity, this operation was shown graphically.
Conclusion:
Within the scope of this work, we have developed a method for an automatic comprehensive assessment of the morphological structure of the
PNSs walls with the measurement of bone density and thickness.
Keywords: Automated analysis, Spiral computed tomography, Bone density, Bone thickness, Paranasal sinuses, Traumatology.
Article History Received: December 15, 2020 Revised: August 13, 2021 Accepted: September 16, 2021
1. INTRODUCTION
The development of technologies for medical image
analysis that utilize computer vision algorithms for the
assessment of scans is on high demand nowadays [1]. This
* Department of Histology, Cytology, Embryology, Kharkiv National Medical
University, 4 Nauky Avenue, Kharkiv, Ukraine Tel: +380999668976;
E-mail: vik13052130@i.ua
approach is based on CT scans processing as an input data for
further evaluation and decision-making.
Computed tomography, in particular, Spiral Computed
Tomography (SCT), is one of the fastest growing research
methods today. This research method is employed to quickly,
accurately, informatively and non-invasively obtain a two- or
even three-dimensional image of any anatomical region of the

Complex Automatic Determination of Morphological Parameters The Open Bioinformatics Journal, 2021, Volume 14 131
human body, draw conclusions about the presence or absence
of abnormal changes, and describe in detail their localization
and nature. It can even be employed in the examination of
medical staff [2].
The widespread introduction of computed tomography in
almost all branches of medicine has a positive effect on the
accuracy of diagnosis of a number of diseases, but significantly
increases the burden on medical staff. The method of manual
calculation of indicators, examined on CT images, has become
routine nowadays. Thus, interpretation of SCT scans requires
significant time and effort from physicians. In this regard, there
is a need to develop a basic algorithm to automate this process.
Traumatology became one of the first branches of medicine
where this task was successfully developed and implemented to
interpret the condition of the bone tissue of long tubular bones.
(The Mathworks Inc., Natick, MA) [3]. Attempts to automate
the calculation of fetal bone parameters are also known [4].
Also of interest is the introduction of a method for automating
the evaluation of images when working with scintigrams.
There is a study [5] describing segmentation of images
obtained in patients with prostate cancer, for which an
algorithm was developed to automatically determine the
presence of foci of metastatic nature throughout the human
body.
There is a series of studies [6, 7], and [8] on evaluation of
the volume of individual bones as well as measurement and
visualization of cortical bone thickness. These studies refer to
the manual approach, semi-automated and automated methods
respectively. The measurement was based on voxel generation
and segmentation, the segmentation was performed using
MeVisLab V1.6 tool (MeVis Medical Solutions AG, Bremen,
Germany) [6]. Moreover, the classifier, automatically
separating all voxels labeled as “bone” into two classes
“femur” and “tibia/fibula” was applied. In addition, there is a
study [5] presenting a deep learning-based image interpretation
system for skeleton segmentation as a novel approach for
automated measurement of the bone scan index. However, the
accuracy and duration of the measurement procedure in the
above-mentioned researches are not suitable for measuring the
skull bones in ENT field.
It should also be noted that despite the abundant amount of
researches on the automation of the study of medical images,
insufficient attention is paid to determine the structure and size
of the skull bones. There are only a few studies dealing with
this aspect. However, these studies take into account panoramic
images, not tomograms [9] and there are no studies aimed at
measuring bone density.
The evaluation and interpretation of medical images are of
particular interest and importance in otolaryngology [10]. To
date, ENT diseases, in particular, the nose and paranasal
sinuses, rank a leading position among all diseases of the upper
respiratory tract and have a steady upward trend. The number
of both acute and chronic forms of rhino sinusitis, as well as
the percentage of complications, are growing every year.
Considering the inaccessibility of the ENT organs during
examination, often only additional methods are employed to
make a diagnosis [11, 12], thereby selecting the most effective
method of treatment. However, the widespread introduction of
SCT into the protocols for the diagnosis and treatment of ENT
disorders entails a significant increase in the burden on the
medical staff, which inevitably leads to inaccuracies and
diagnostic errors [13, 14].
One of the most important structural parameters of the
walls of the paranasal sinuses, which directly affect the
occurrence and progression of inflammatory processes in the
paranasal sinuses (PNSs), namely rhinosinusitis, are the
thickness and density of the bone. Moreover, preliminary
assessment of the bone thickness and density can be helpful in
avoiding the development of further complications of
rhinosinusitis.
Calculation of these indicators is a rather time-consuming
process, taking a lot of time and efforts. In addition, the issue
of the method and choice of the correct points for computing
the values of the required indicators remains open.
Thus, automation of measurement for CT images of the
paranasal sinuses is a promising method. Taking into account
all of the above, the aim of our study was to develop a
comprehensive method for automatic assessment of the
morphological structure of the PNS’s walls, their thickness and
density.
2. MATERIALS AND METHODS
The study involved the examination of 50 SCT scans of
men and women aged 20 to 50 without ENT disorders, who
underwent SCT procedures due to conditions not related to
ENT diseases (unconfirmed stroke, etc.).
Spiral CT scanner Toshiba Aquilion 64, Japan, was used to
obtain the scans. Toshiba Aquilion 64-slice is a new model in
the portfolio of CT scanners of Toshiba Corporation (Japan),
which utilizes state-of-the-art technological developments and
provides a high-quality diagnosis with reduced radiation
exposure.
Generally, image analysis has been an active research in
many branches of medicine. It gives the possibility to obtain
histological [15], roentgen or other images. It results in the
development of a scientific computing program for obtaining
adequate data for biomedical, epidemiological [16 - 18]
studies.
The appropriate parameters were measured using the
RadiANT DICOM viewer, which is widely used for the study
of medical images [19 - 21]. It is employed for most accurate
determination of the maximum and minimum values of
parameters for bone thickness and density [22].
The first task was to define the minimum bone thickness.
The maxillary sinus was chosen for the experiment as it is
responsible for the maximum incidence of abnormal changes.
It is characterized by maximum size, proximity of the location
of the teeth and complexity of the location of the natural
anastomosis with the nasal cavity. The upper wall is of
particular interest due to the high risk of intra-orbital
complications [23, 24].
The first part (Fig. 1) of the study is described in detail in
the material [25].

132 The Open Bioinformatics Journal, 2021, Volume 14 Nechyporenko et al.
Fig. (1). Examples of the studied SCT sections.
Preparatory stage involved preliminary arrangement of CT
images: areas of the maxillary sinus wall with a minimum
thickness, coordinates of points with a minimum density on the
Hounsfield scale [26]. A site of known length (25 mm) was
marked on the SCT image for calibration.
The software analyzes the contour of the image, measures
the image thickness, denoting bone tissue, setting the size of
the image in required locations. As soon as an element of a
predetermined size is identified in the image, automatic
conversion to millimeters or other units is performed.
Thus, the measurement of bone thickness was not so
complicated from the technical point of view. Density
measurement caused many predicaments. The specified
parameter is characterized by excessive variability in values. In
this regard, it was decided to use a coordinate system for a
more accurate calculation of density indicators.
For example, in the presented image (Fig. 2) the minimum
value of the density index corresponds to the coordinates X:
156, Y: 264, as indicated in the lower left corner of the image.
Similar points were found on all sections of the SCT in all
cases under investigation.
Obviously, the measurement procedure for bone density at
an arbitrary point should be divided into several steps. Firstly,
it is necessary to determine the points of minimum (hereinafter
referred to as the MIND) and maximum (MAXD)) density. The
MAXD point is only needed to calibrate the range and does not
provide useful information for the physician, so it is not
marked on the images. Determination of the MIND point is
associated with a number of difficulties. To begin with, let us
consider the codification of information in the photo. The
image is black and white with grayscale. The image contains a
large amount of noise in the form of fragments of white stake
on a black or gray background, or vice versa, fragments of
black on a white or gray background.
Fig. (2). Determination of the minimum bone density of the wall of
Maxillary sinus on a SCT section using RadiANT software. Coronary
section.
Densitometric parameters of bones and other parts of the
body are encoded by the brightness of the colour. Soft tissues
have +40 units on the Hounsfield scale, bones +400 and above.
Soft tissues have the appearance of dull areas on radiographs;
bones are bright white. The transitions between tissue types are
usually clear.
Images of bones located at different distances from the
film or camera also differ in intensity. Images of bones located
at a big distance can almost merge with the image of soft
tissues.
Obviously, before processing, the photo should be cleaned
from noise. The operation is described in many sources, in
particular in another study [27 - 29], so we would like to focus
on the main points. Before cleaning, the image is blurred using
the Gaussian function, as described [30]. As a result of this
operation, the images become less cleaned and small details
merge.
The next step is to mark the contours on the image. From a
human point of view, a contour is a visible edge that separates
an object from the background. From the point of view of
software, the contour is a curve along which there is a sharp
change in brightness. The internal content of the object is not
taken into account. If a distinct edge cannot be detected, or if
the contours overlap, errors, such as incorrect contour
selection, may occur.
The algorithm known as the Canny Edge Detector [31] has
found widespread use.
The curves denoting the contours can run vertically,
horizontally or diagonally at different angles. Detection of the
direction of curves passing vertically and horizontally is not
challenging. In order to identify the curves in the diagonal
direction, the Sobel operator is implemented. The first
derivative was calculated to detect the vertical direction Gy and
the horizontal Gx.
These parameters allow us to obtain the angle of the
direction of the contour boundary according to the expression
Q = arctan (Gx / Gy). The angle of the boundary is rounded to

Complex Automatic Determination of Morphological Parameters The Open Bioinformatics Journal, 2021, Volume 14 133
one of four angles, which are vertical, horizontal, and two
diagonals, such as 0, 45, 90, and 135 degrees.
For instance, for a 3x3 grid:
- If the angle of the gradient is zero, the point will be
considered as a border; while its brightness is greater than the
points above and below this point,
- If the angle of the gradient is equal to 90 degrees, the
point will be considered the border, given that its brightness is
greater than the points on the left and right,
- If the angle of the gradient is equal to 135 degrees, the
point will be considered the border, while its brightness is
greater than the points located in the upper left and lower right
corners of this point.
Thus, it is possible to obtain an image that contains the
boundaries of the contours.
The next step involves the removal of all the small
contours containing noise. It has been experimentally proved
that all contours smaller than 10x10 pixels can be considered
noise for images of 512 by 512 pixels.
Here, there is a risk of accidental removal of small parts of
the image, so the algorithm should be supplemented by
analysis of the location of the contours (e.g., for overlapping
contours which can potentially become one large contour).
Binarization of the image with a certain threshold enables
the detection of white areas that mark the bone tissue. The
method of obtaining such an image and determining the type of
tissue is described in another study [28]. Within the current
research, the threshold was chosen empirically by the authors.
The calculation of the optimal binarization threshold will be
performed in our future studies.
After the selection of areas of bone tissue, it is necessary to
provide the assessment of the brightness gradient along the
long side of the area. For clarity, this operation is shown
graphically.
From the brightness values at each point, an array is
formed, in which it is easy to find the minimum and maximum
points. The maximum value is not currently used, and the
minimum means the area where bone density is the lowest.
It should be understood that it is determined not by
absolute but by relative density. To determine the absolute
density of the image, it is necessary to identify a region of
predetermined density, for example, on the Hounsfield scale, or
to have a table of the ratio of density-brightness of the image.
The issue of determining the absolute density will be
considered in further studies. Series of experiments will be
performed to establish a calibration curve of density. For each
given pixel of CT, a relative linear attenuation coefficient will
be determined, then the value of the coefficient will be
normalized to the reference material, in particular water. The
obtained result will be multiplied by magnified constant, which
depends on the CT scanner. The creation of the calibration
curve for each scanner will allow us to quantify and minimize
inter- and intrascanner variations and obtain the absolute
density of CT image.
Python 3.10 programming language and additional Open
CV library were used for image processing.
The next steps are performed according to the following
algorithm:
(1) Image blurring using Gaussian function [25].
(2) Cleaning the image from noise [27 - 29].
(3) Creating a copy of the image of determining the areas
of the minimum thickness of the thickness of the bones.
(4) Determining the minimum bone thickness areas.
(5) Creating a copy of the image to determine areas of
minimum bone density.
(6) Determining the minimum bone density areas.
(7) Overlaying on the original image of the markers of
minimum thickness and minimum density.
(8) Outputting of additional information, in particular
relative or absolute thickness and density.
Information on bone density in areas of their minimum
thickness (Fig. 3) may also be useful, so these figures are also
derived.
Fig. (3). An example of a combined image that contains information
about the thickness and density of bones at points of minimum
thickness and at several characteristic points.
3. RESULTS
To assess the accuracy of the method, several series of
tests were performed on real images; the results are
summarized in the table, showing calculated average and
maximum error of the method.
The results of applying the method are shown in Table 1.

134 The Open Bioinformatics Journal, 2021, Volume 14 Nechyporenko et al.
Table 1. Results of applying the method of automated analysis of the bone thickness (mm) and Density (Hu).
SCT name X auto Y auto X man Y man dX dY dL % x % y Density, Hu
1 2 3 4 5 6 7 8 9 10 11
r1 276 238 278 241 2 3 3,61 0,72 1,26 54
r5 182 96 181 104 -1 8 8,06 0,55 8,33 30
r6 336 559 324 555 -12 -4 12,65 3,57 0,72 23
r7 250 440 251 438 1 -2 2,24 0,40 0,45 142
r12 168 310 165 315 -3 5 5,83 1,79 1,61 127
r13 340 249 339 266 -1 17 17,03 0,29 6,83 28
r17 368 383 370 391 2 8 8,25 0,54 2,09 86
r18 332 242 334 250 2 8 8,25 0,60 3,31 52
r19 74 256 53 191 -21 -65 68,31 0,00 0,00 73
r23 159 329 160 333 1 4 4,12 0,63 1,22 62
r24 373 585 369 593 -4 8 8,94 1,07 1,37 245
r25 13 268 13 267 0-1 1,00 0,00 0,37 61
r29 223 437 222 437 -1 01,00 0,45 0,00 41
r30 263 335 263 339 04 4,00 0,00 1,19 209
r31 276 305 277 294 1 -11 11,05 0,36 3,61 256
r36 138 250 130 241 -8 -9 12,04 5,80 3,60 294
r37 101 170 96 181 -5 11 12,08 4,95 6,47 263
r38 68 67 68 68 01 1,00 0,00 1,49 76
r42 42 247 43 250 1 3 3,16 2,38 1,21 263
r43 4 313 4 313 0 0 0,00 0,00 0,00 76
r44 311 284 311 290 06 6,00 0,00 2,11 175
r48 37 278 37 278 0 0 0,00 0,00 0,00 35
r49 164 506 167 509 3 3 4,24 1,83 0,59 333
r50 6 233 2 244 -4 11 11,70 66,67 4,72 249
Column SCT name indicates the name of the scan which
was processed, column X auto shows the coordinate of the
point of least bone thickness along the X axis, column Y auto
along the Y axis, respectively. Columns X man and Y man
indicate the coordinates of the point of smallest thickness
defined by the operator manually.
Columns dX, dY and dL indicate the difference between
the point defined by the software and the point defined by the
operator on the X and Y axes, as well as the distance along the
line.
Columns% x and% y show the same percentage.
Table 1 shows that in some cases the evaluation of the
software and the operator coincides completely, in most cases
the deviation is small, but there may be poor quality photo,
which causes difficulties for the software.
Thus, it is noteworthy the strong discrepancy (68 pixels)
between the evaluation of the software and the operator when
processing photos R19. The discrepancy is caused by the low
quality of the photo; in particular, the image is placed
diagonally. Taking into account this image, the average
deviation along the X axis is 8.6%, and 5.6% along the Y axis
which, in the opinion of the authors, is quite acceptable for
practical use.
In the case of culling a low-quality image, the average
deviation along the X axis is 6.6%, and 3.7% along the Y axis.
4. DISCUSSION
To date, this study is the first to develop a method for
automatic evaluation of human PNSs images. Despite the huge
number of available investigations [4 - 6], concerning various
areas of research, a single unified algorithm for automatic SCT
diagnosis of the state of PNSs has not been developed yet. This
study is of great medical importance; it is essential for
understanding the development of pathological processes in
PNSs and preventing their complications.
The area of human PNSs at this point in time is rather
poorly understood. Perhaps, this fact is associated with many
emerging intra- and postoperative complications [30]. In view
of the small amount of knowledge about the PNS structure,
most of the information is obtained by otolaryngologists in the
course of diagnostic or therapeutic procedures, which does not
bring positive effects.
Attempts have been made to study these values separately;
however, in our opinion, only a comprehensive assessment will
allow us to assess the real picture of the state of PNSs, to
calculate the risks of rhinosinusitis and their complications.
Bone density has received too little attention today. There
are only a few works devoted to this indicator and they were
mostly carried out on animals [31] rather than on humans.
However, the degree of density decrease can correlate with the
degree of bone alteration, expressed in rarefaction and
disappearance of trabeculae and focal sclerosis.

Complex Automatic Determination of Morphological Parameters The Open Bioinformatics Journal, 2021, Volume 14 135
There is also a point of view about the relationship
between destructive changes in the walls of the PNSs and a
decrease in the minimum bone thickness.
However, it can be assumed that the simultaneous
measurement of these indicators would be more informative.
Thus, this study helps to automatically measure the bone
density of the walls of human PNSs according to SCT as
accurately as possible. Moreover, it reduces the measurement
time significantly compared to other methods. For instance,
manual measurement requires approximately 180 s, the
procedure of measurement according to the method presented
in another study [5] takes 112 s, while the measurement time
according to our method ranges from 3.5 to 9.3 s. One more
advantage of the proposed method is the ability to obtain data
from previously taken images, which makes it possible to track
the time course of changes in the densitometric parameters of
the patient's bones.
The developed method can be useful not only for PNSs,
but also in dentistry to diagnose disorders of neighboring soft
tissues [32] or even processes of reparation [33] and predict
complications of some diseases [34, 35] for cohort study [36,
37].
Experiments have also shown that the output of
information is sometimes superimposed, making it a little
tricky in terms of perception. The developers have identified
several ways to overcome this disadvantage, in particular, the
definition of the minimum information areas in the image and
the output of information to them, or a switch that can turn off
the output of information in part or in full. The optimal solution
regarding the ergonomics is planned to be defined after
implementation of methods in practice and interviewing of a
focus group of medical doctors and other users of the software.
This study is a continuation of our previous work aimed at
studying and detailing the structure of human PNSs. Earlier
attempts were made to calculate the uncertainty of the values of
morphological parameters of the walls of the PNSs and the
ostiomeatal complex [38, 39] both in physiological conditions
and during the development of abnormal processes in them.
The obtained results were validated on the 3D model of the
PNSs and ostiomeatal complex built by authors and during the
subsequent measurement of the pressure in the anastomosis
The developed method can help save time and facilitate the
daily routine of medical personnel, which means that, as soon
as possible, accurately and effectively determine the structural
features of the PNSs.
CONCLUSION
Within the scope of this study, we have developed a
method for an automatic comprehensive assessment of the
morphological structure of the PNSs walls with the
measurement of bone density and thickness. The obtained
results make it possible to provide an automatic, accurate and
informative assessment of the main indicators of the bone
tissue structure of the PNSs walls. Today, this method can be
optimal in the study of CST of the PNSs, as it will allow to
exclude operator error when assessing the state of the PNSs, to
prevent the development of complications of inflammatory
processes and reduce the risk of injury during surgical
interventions. Our future research will be related to the
development of algorithm for automatic identification of
paranasal sinuses` areas with minimal density and thickness as
a sign that leads to the development of intracranial or
intraorbital complications.
AUTHORS' CONTRIBUTIONS
Labaratory Practices: Alina Nechyporenko, Radiy
Radutny.
Concept: Victoriia Alekseeva, Ganna Titova.
Design: Alina Nechyporenko, VitaliyGargin.
Data Collection or Processing: Victoriia Alekseeva,
Ganna Titova.
Analysis or Interpretation: Vitaliy Gargin, Radiy
Radutny.
Literature Search: Ganna Titova, Radiy Radutny.
Writing: Victoriia Alekseeva, Alina Nechyporenko,
Vitaliy Gargin.
ETHICS APPROVAL AND CONSENT TO PARTI-
CIPATE
The study was approved by the Commission on Bioethics
of Kharkiv National Medical University (Nº 5 of 11.11.2019).
HUMAN AND ANIMAL RIGHTS
No animals were used in this research. All human research
procedures were followed in accordance with the ethical stan-
dards of the committee responsible for human experimentation
(institutional and national), and with the Helsinki Declaration
of 1975, as revised in 2013.
CONSENT FOR PUBLICATION
Аll patients under the investigation gave their voluntary
consent to participate in the study.
AVAILABILITY OF DATA AND MATERIALS
The data supporting the findings of the article is available
within the article.
FUNDING
None.
CONFLICT OF INTEREST
The authors declare no conflict of interest, financial or
otherwise.
ACKNOWLEDGEMENTS
Declared none.
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