UNIVERSIDADE ESTADUAL PAULISTA
“JÚLIO DE MESQUITA FILHO”
Instituto de Ciência e Tecnologia
Campus de São José dos Campos
SHORT COMMUNICATION DOI: https://doi.org/10.4322/bds.2024.e4342
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Braz Dent Sci 2024 Oct/Dec;27 (4): e4342
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Utilizing Texture Analysis technique in diagnostic imaging at
Dentistry: innovations and applications
Utilizando a técnica de Análise de Textura no diagnóstico por imagem em Odontologia: inovações e aplicações
Victoria Geisa Brito de OLIVEIRA1 , Vera Lucia ROSEIRA1 , Lana Ferreira SANTOS1 , Emanuel da Silva ROVAI1 , Andréa
Carvalho DE MARCO1 , Maria Aparecida Neves JARDINI1 , André Luiz Ferreira COSTA2 , Sérgio Lucio Pereira de Castro
LOPES1
1 - Universidade Estadual Paulista “Júlio de Mesquita Filho”, Instituto de Ciência e Tecnologia, Departamento de Diagnóstico e Cirurgia.
São José dos Campos, SP, Brazil.
2 - Universidade Cruzeiro do Sul, Programa de Pós-graduação em Odontologia. São Paulo, SP, Brazil.
How to cite: Oliveira VGB, Roseira VL, Santos LF, Rovai ES, De Marco AC, Jardini MAN et al. Utilizing Texture Analysis technique in diagnostic
imaging at Dentistry: innovations and applications. Braz Dent Sci. 2024;27(4):e4342. https://doi.org/10.4322/bds.2024.e4342
ABSTRACT
Background: The Texture Analysis (TA) technique allows the evaluation of intrinsic properties by extracting signal
patterns from pixels and voxels in images that are unnoticed by the human eye. In medical imaging, TA has been
applied to the characterization of various lesions. In Dentistry, in recent years, we have observed the application
of this tool in various specialties. Objective: This short communication aims to present the applications of the
texture analysis (TA) technique in Dentistry and its possibilities for the coming years. Material and Methods:
For this brief review, the search was conducted in the Pubmed, LILACS, and Google Scholar databases, using
the descriptors “Computer-Assisted Image Processing”, “Diagnostic Imaging” and Dentistry”. Were included
articles that addressed the topic, published in the last 5 years in English, and compatible with the present theme.
Considering the 22 articles found, it was observed that, for the most part, AT applications aim to assist in the
diagnosis of lesions of the maxillofacial complex. Then temporomandibular disorders and oral manifestations
of autoimmune conditions. There are also applications in orthodontics, periodontics, implant dentistry, and
cariology. Conclusion: The TA technique presents itself as a promising method within dental imaging, since,
through its mathematical and quantitative tools, it provides greater accuracy and objectivity. In this way, we
can see the emergence of a biomarker that assists professionals in the early diagnosis of injuries. However, the
research carried out to date has limitations, and more studies are needed to understand the capabilities of TA.
KEYWORDS
Computer Assisted Diagnosis; Diagnostic Imaging; Dentistry; Radiomics; Radiology.
RESUMO
Contexto: A técnica de Análise de Textura (AT) permite avaliar propriedades intrínsecas extraindo padrões de
sinal de pixels e voxels em imagens que são despercebidas pelo olho humano. Na imaginologia médica, a AT
tem sido aplicada na caracterização de lesões diversas. Na Odontologia, observamos nos últimos anos aplicação
dessa ferramenta em diversas especialidades. Objetivo: Esta
short communication
objetiva apresentar as
aplicações da técnica de análise de textura (AT) na Odontologia e suas possibilidades para os próximos anos.
Materiais e Métodos: Foi realizada busca nas bases de dados Pubmed, LILACS e Google Scholar, aplicando
os descritores “Computer-Assisted Image Processing”, “Diagnostic Imaging” e Dentistry”. Foram selecionados
artigos compatíveis com a temática, publicados nos últimos 5 anos e publicados em inglês. Considerando os 22
artigos encontrados, observou-se que, majoritariamente, as aplicações de AT objetivam auxiliar no diagnóstico
de lesões do complexo maxilofacial. Em seguida, desordens temporomandibulares. Nota-se também aplicações
na ortodontia, na periodontia, na implantodontia e na cariologia. Conclusão: A técnica de AT apresenta-se
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Oliveira VGB et al.
Utilizing T exture Analysis technique in diagnostic imaging at Dentistry: innovations and applications
Oliveira VGB et al. Utilizing Texture Analysis technique in diagnostic imaging at
Dentistry: innovations and applications
INTRODUCTION
In recent decades, imaging exams have rep-
resented signicant advancements in complemen-
tary medical and dental diagnosis [1,2]. Over the
past years, magnetic resonance imaging (MRI) and
cone-beam computed tomography (CBCT) have
stood out in dentistry due to their effective appli-
cations. More recently, predictive Articial Intel-
ligence tools, such as Machine Learning (ML) and
Texture Analysis (TA) techniques, have also gained
prominence. This technique promotes enhancing
the accuracy and precision of lesion diagnosis in
conditions such as brain tumors [3], autoimmune
diseases [4], and Diabetes Mellitus [5].
According to Strzelecki et al., texture is
perceived by humans as a visualization of complex
patterns composed of repeated and spatially
organized subpatterns, whose appearance is
characteristic. The local subpatterns inside of
an image are perceived through parameters like
specic brightness, color, roughness, directivity,
randomness, and smoothness. The TA technique is
a quantitative method that enables the visualization
of patterns and variations in the pixels or voxels
organization in specic areas of an image. This tool
identies and measures texture levels in images,
assisting the operator in perceiving subtle changes
that are imperceptible to the naked eye [6].
Texture is defined in the imaging area as
a descriptor of the internal structure of human
tissues or organs. Consequently, the TA of images
is an essential question in the processing and
comprehending of diagnostic images [7]. Essentially,
TA uses imaging exams as radiographs, computed
tomography, and magnetic resonance images as
substrates to analyze characteristics quantitatively.
This approach, which considers the grey levels in
a specied region of interest (ROI) [8,9], allows
us to evaluate the existing grey levels in the ROI
of an image. It also enables us to understand the
relationship between pixels in two-dimensional
images and voxels in three-dimensional images,
increasing accuracy and aiding in the interpretation
of diagnostic images [10].
In this context, Texture Analysis is
defined as an image evaluation technique
based on quantitative analysis, through the
spatial arrangement of pixels or voxels among
themselves, in a delimited ROI; and the evaluation
of all shades of gray arranged in this same ROI,
through histograms. It stands out as a differential
because it allows information on regions with
different levels of gray that go unnoticed by the
operator. Thus, the technique’s main indication is
the proposal to perform an objective comparative
analytical evaluation between images [2,11].
According to the literature, there are
different software applications for TA, such as
MATLAB [12,13] and Mazda. The last one stood
out in medical and dentistry imaging because
most studies in those areas have used it as the
tool for your aims [14-16]. The Mazda software,
then, has six AT methods, emphasizing the grey
level co-occurrence matrix (GLCM) and the grey
level run length matrix texture (GLRLM) [17].
It’s possible to analyze up to 11 parameters in
each method, according to Haralick et al. [10]
and presented in Figure 1.
Due to the increasing demand for diagnostics
with greater precision and accuracy in medicine
and dentistry and the growing use of TA, this
short communication proposes to conduct a brief
review of the current panorama of the applications
of the Texture Analysis technique in dentistry,
enumerating the limitations of this technique, and
future possibilities in oral radiology.
MATERIAL AND METHODS
The search was conducted in the Pubmed,
LILACS, and Google Scholar databases, using
the descriptors “Computer-Assisted Image
como um método promissor dentro na imaginologia odontológica, uma vez que, através de suas ferramentas
matemáticas e quantitativas, proporciona maior acurácia e objetividade. Dessa forma, percebe-se o surgimento de
um biomarcador que assista ao prossional no diagnóstico precoce de lesões. No entanto, as pesquisas realizadas
até o presente momento possuem limitações e mais estudos são necessários para entender as capacidades da AT.
PALAVRAS-CHAVE
Diagnóstico por Computador; Diagnóstico por imagem; Odontologia; Radiômica; Radiologia.
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Oliveira VGB et al.
Utilizing T exture Analysis technique in diagnostic imaging at Dentistry: innovations and applications
Oliveira VGB et al. Utilizing Texture Analysis technique in diagnostic imaging at
Dentistry: innovations and applications
Processing”, “Diagnostic Imaging” and Dentistry”.
The inclusion criteria were articles that addressed
the topic, published in the last 5 years in English,
and without restriction as to the country of origin.
As exclusion criteria, were selected articles that,
through reading the title and abstract, were not
related to the proposed topic were excluded.
In the end, 22 studies were selected for the
preparation of this brief review.
FUNDAMENTAL CONCEPTS
Before conducting a proper review, it is
crucial to present essential concepts to understand
this topic. Using specialized software, the TA
can be understood as the analysis of imaging
exams to capture the texture properties of the
ROI. Therefore, TA uses a mathematical method
for processing and analyzing digital images,
which consists of specifying descriptors related
to the distribution of grey levels in the image.
These descriptors, or properties, are expressed
objectively by detecting changes in grey levels
of an image [18].
What distinguishes this new model is the
quantitative analysis of the grey levels a region
should contain once the human eye, no matter
how trained and experienced, has limitations,
capturing only 60 of the 4000 levels of grey
between the pixels of a given area [19].
According to the literature, there are six TA
methods, such as GLCM, consisting of a square
matrix, in which the number of lines and columns
is equal to the grey levels of that image, revealing
properties about the spatial distribution of the
grey levels on the texture of the image. In a
few words, this method simplies the quantity
and the distribution of shades of grey in a
determined ROI. The GLRLM method represents
pixel series with the same grey level values [6].
In each process, it’s possible to analyze up to
11 parameters quantitatively, as Haralick et al.
proposed [10]. Figure 2 summarizes how the
initial Texture Analysis process takes place.
Pioneering applications
Despite research about the TA starting in the
late 1970s [10], the applications only became
prominent at the beginning of the 21st century.
In Medicine, the studies aimed to distinguish the
physiological aspect of tissue from lesions present
in different regions of the human body, associ-
ating the pathological aspect with considerable
contrast, for example.
Figure 1 - Example of a texture analysis (TA) method – GLCM - with the eleven parameters that can be evaluated. Source: adapted from
Haralick et al. [10].
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Oliveira VGB et al.
Utilizing T exture Analysis technique in diagnostic imaging at Dentistry: innovations and applications
Oliveira VGB et al. Utilizing Texture Analysis technique in diagnostic imaging at
Dentistry: innovations and applications
In medical literature, TA is most present in
critical studies, such as the study of Gibala et al.,
whose purpose was to validate TA as a biomarker
for detecting important characteristics in the
imaging exam to indicate prostate cancer risk.
In this investigation, the ndings expressed a
high dependency between the characteristics
of the image related to cancer markers and,
consequently, the risk of cancer [20]. Ye et al. [21],
in turn, aimed to explore the total TA value of
tumors based on magnetic resonance image
(MRI) to differentiate ovarian epithelial tumors
borderline to stage I/II malignant ovarian
epithelial tumors, whose results suggest that this
can be an auxiliary tool on differential diagnostics
of this lesions.
It’s worth emphasizing the studies that
evaluate texture parameters as possible biomarkers
for the prediction of cell invasion in these types of
cancer: liver [22] and gastric [23], on evaluation
of breast imaging on cancer screening [24] and
even on complementary analyze of COVID-19
pneumonia [25].
Applications in dentistry
In the last few years, there has been an increas-
ing number of studies involving texture analysis
and possible applications. Most studies have
applied this tool as an auxiliary diagnostic method
on maxillofacial lesions [5,7-9,12-15,26-30]. In
this section, we list some applications in Dentistry
found in the literature, and Figure 3 presents the
distribution of the articles found in our research.
•
Bone oral health:
Queiroz et al., assessed
variations in trabecular bone using AT and
compared TA technical features of different
areas in patients with medication-related
jaw osteonecrosis (MRONJ). The study used
cone beam computed tomography (CBCT)
scans and perceived those images of the
regions compared presented higher values
for the following parameters: contrast (CO),
entropy (S), and angular second moment
(ASM), regarding healthy areas, suggesting
major disorder on those tissues [8].
•
Oral Pathology:
Gomes et al. utilized a
substrate magnetic resonance image to
consolidate TA as an objective marker
to differentiate ameloblastoma from
Figure 2 - Flowchart of the texture analysis process, from acquisition, using MRI images as a substrate to applicate Texture Analysis. ROI:
Region of interest; TA: Texture Analysis. Illustration prepared by authors.
Figure 3 - Illustration of the area distribution of articles involving
TA and Dentistry found in the search. TMD: Temporomandibular
Disorders. Source: the authors.
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Oliveira VGB et al.
Utilizing T exture Analysis technique in diagnostic imaging at Dentistry: innovations and applications
Oliveira VGB et al. Utilizing Texture Analysis technique in diagnostic imaging at
Dentistry: innovations and applications
odontogenic keratocyst. The statistics analysis
reveals that two of the eleven parameters
presented significant relationships in
this differentiation [12]. In comparing
odontogenic maxillary sinusitis with non-
odontogenic maxillary sinusitis, the research
of Costa et al. [15] and Ito et al. [13]
obtained statistically signicant values in
their results about the analyzed parameters.
De Rosa et al. [7] published in 2020 the
results from the investigation into the use
of AT for the characterization of radicular
cysts (RC) from periapical granuloma (PG)
and the efcacy of this tool in differentiating
both lesions, already with histological
diagnosis. CBCT images were obtained from
19 patients with periapical lesions (14 RC
and 11 PG), conrmed by biopsy.
•
Temporomandibular joint disorders
(TMD):
Oliveira et al. [28] aimed to
characterize TA parameters of the condylar
medullary bone and the superior aspect
of the lateral pterygoid muscle (LPM) on
Magnetic Resonance Imaging (MRI) for
the identification of potential changes
in individuals with temporomandibular
disorders (TMD). Forthy MRI scans were
retrospectively selected, consisting of 20
patients without temporomandibular joint
(TMJ) changes (control group) and 20
patients diagnosed with TMD (TMD group).
All MRI scans adhered to a consistent
protocol. TA was performed using the
Mazda, and the ROIs were standardized for
all evaluated images. The texture parameters
were calculated through the GLCM method.
TA results underwent comparison using the
Mann-Whitney test. There was a statistically
signicant difference in the CO and IDM
parameters between control and TMD
groups, notably evident in images for the
region of the condylar medullary bone and
the LPM, respectively (p<0.05). Figure 4
illustrates one of the steps of texture analysis
in a similar study [31], in which MRI images
were also used as imaging exams, however,
to evaluate possible changes in individuals
with migraine headache.
•
Periodontology
: Gonçalves et al. [16]
evaluated patients with grade C periodontitis
to detect non-visible changes in the image.
Statistically significant differences were
observed in almost all parameters of the
intergroup analysis.
•
Dental Implants
: The study of Costa et al. in
2021 [32] aimed to characterize the alveolar
bone of edentulous jaws using CBCT images
and TA. Thus, the aim was to correlate the
results with the insertion torque, verifying
if TA is a predictive tool for implant
treatment results. The results showed a
direct correlation with the contrast of peri-
implant bone and an inverse correlation with
the entropy of the bone site of the implant.
•
Orthodontics:
Ito et al. aimed to quantitatively
assess the roots of upper central incisors
using the AT technique in pre-orthodontic
treatment patients, based on Cone Beam
Computed Tomography (CBCT) images, as
part of a quantitative texture analysis. This
retrospective case-control study included 16
patients with external apical root resorption
(EARR) and 16 age- and sex-matched
patients without EARR (control group),
after orthodontic treatment, who underwent
pre-orthodontic CBCT to address maxillary
deformities. All patients were treated with
xed orthodontic appliances before and after
surgical orthodontic treatment. The texture
characteristics of the upper central incisors
with and without EARR after orthodontic
treatment were analyzed using Mazda. Ten
texture features were selected, and, thus,
four features from the GLRLM method; and
six features from GLCM method showed
signicant differences between both groups
(p < 0.01) [33].
•
Cariology:
Obuchowicz et al. evaluated
the performance of texture feature maps
in recognizing discrete demineralization
related to caries plaque formation. Digital
protocols for intraoral radiographic image
analysis, incorporating features beyond
texture, such as rst-order features (FOF),
local binary patterns (LBP), and k-means
clustering (CLU), were used. Regarding
texture analysis itself, the GLCM and
GLRLM methods were employed. All of
these methods were applied to transform
digital intraoral radiographic images from
10 patients with a conrmed diagnosis of
caries, retrospectively reviewed in a dental
clinic. The performance of the resulting
texture feature maps was compared with
that of radiographs evaluated by radiologists
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Oliveira VGB et al.
Utilizing T exture Analysis technique in diagnostic imaging at Dentistry: innovations and applications
Oliveira VGB et al. Utilizing Texture Analysis technique in diagnostic imaging at
Dentistry: innovations and applications
and dental specialists. Signicantly improved
detection of carious spots was achieved
through the use of texture feature maps from
CLU and FOF. The area affected by caries
with sharp margins was well-dened by the
CLU approach. A pseudo-three-dimensional
effect was observed in the delineation of
demineralization zones within the cavity
using a specic protocol. In contrast, the LBP
and run-length matrix techniques produced
less satisfactory results, with blurry edges and
less detailed representation of lesions [34].
DISCUSSION
The literature has presented that imaging
exams are complementary tools for dental clinic
diagnostics, providing more accurate information
and helping with treatment success [35-37].
It’s important to highlight that, despite the
considerable experience of radiologists, there
are visual limitations inherent to human beings.
Those limitations occurred due to the human
capacity for perceiving only a tiny fraction of grey
levels and the subjective capacity for analysis,
which went unnoticed in some aspects of images
that could have atypia [15].
In this context, the advent of an objective
and quantitative analysis tool that can analyze
the information an ROI can provide through
mathematics and objective parameters is an
essential appliance in the diagnosis overview.
Thus, studies using the TA are emerging in
Medicine and Dentistry [2,3,38,39].
Figure 4 - Example of ROI delimitation of the articular disc in a T2-weighted image of the TMJ and calculation of texture parameters by using
the Mazda software. Source: Fardimetal. [31].
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Utilizing T exture Analysis technique in diagnostic imaging at Dentistry: innovations and applications
Oliveira VGB et al. Utilizing Texture Analysis technique in diagnostic imaging at
Dentistry: innovations and applications
We observed the predominant use of the
Mazda software in evaluating medical and
dental images [8,15,16,28,31,32]. The reason
for this predominance is still unclear, suggesting
the future realization of comparative studies
between Mazda and other programs listed in the
literature, such as MATLAB [12,13] and Machine
Learning [27]. The articles mainly used the GLCM
and GLRLM methods in their research, showing at
least one AT considerable parameter in statistics
terms for evaluating the object of interest.
The studies of Ricardo et al. [4], Queiroz et al.
[8], Gomes et al. [12] and Fardim et al. [31] all
presented entropy (S) as a meaningful parameter
in the results, suggesting that physiological
areas are associated with the lowest disorder. In
contrast, areas with lesions are related to higher
entropy, as well as other parameters, such as
higher contrast.
LIMITATIONS
As well as all image analysis methods, the AT
technique has some inherent limitations. Among
them, we list the evaluator’s skill in determining
the ROIs to be analyzed; the variability of image
acquisition parameters that can alter the results of
the calculation of pixel values by the competition
matrix; and also the time spent to calculate
texture parameters, which, depending on the size
of the region of interest and image resolution, can
be quite extensive. In other words, this technique
requires specialized training of operators and the
acquisition of high-quality images.
We further discuss that reduced sample
size in most research, demanding future studies,
retrospective or not, that have a more extensive
and considerable sample, avoiding biases.
Furthermore, there is a need for more studies
that aim to compare TA in two-dimensional and
three-dimensional images. In addition, future
research may have more than one examiner
for the initial stages, including the stage of
ROI delimitation, provided they are previously
calibrated with each other. Barioni et al. discuss
the need to standardize analyses for each AT
method, and develop guidelines; hence, there is
a need for multicenter research groups to develop
investigations in this regard [40,41].
Therefore, it’s understood that TA is an
auxiliary promising tool for dentomaxillofacial
diagnosis, which could provide objective
information with higher accuracy. However,
more studies are needed to verify the feasibility
of this technology in clinical routine, such as
consolidating your use according to scientic
evidence.
CONCLUSION
It was evident that the TA has superior
accuracy and can objectively assist in diagnosing
critical lesions, which, if detected initially through
subtle aspects, can obtain a better prognosis and
more appropriate treatment.
Author’s Contributions
VGBO, VLR, LFS, SLPCL: Conceptualization.
VGBO: Data Curation. VGBO, LFS, ESR, ACDM,
MANJ, ALFC, SLPCL: Formal Analysis. VGBO,
VLR, LFS, ESR, ACDM, MANJ, ALFC, SLPCL:
Funding Acquisition. VGBO, VLR, LFS, SLPCL:
Investigation. VGBO, VLR, SLPCL: Methodology.
VGBO, SLPCL: Project Administration. VGBO,
VLR, LFS, SLPCL: Resources. ALFC, SLPCL:
Software ESR, ACDM, MANJ, ALFC, SLPCL:
Supervision. SLPCL: Validation. VGBO, VLR, LFS,
ESR, ACDM, MANJ, ALFC, SLPCL: Visualization.
VGBO, VLR, LFS, ESR, ACDM, MANJ, ALFC,
SLPCL: Writing – Original Draft Preparation.
VGBO, VLR, LFS, ESR, ACDM, MANJ, ALFC,
SLPCL: Writing – Review & Editing.
Conict of Interest
The authors have no proprietary, nancial,
or other personal interest of any nature or kind
in any product, service, or company presented
in this article.
Funding
This study was nanced by the Coordenação
de Aperfeiçoamento de Pessoal de Nível Superior
- Brasil (CAPES) - Finance Code 001.
Regulatory Statement
This article consists of a brief literature
review and therefore does not involve the
collection of primary data from human or animal
subjects. No ethics committee approvals or
informed consent forms were required.
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Utilizing T exture Analysis technique in diagnostic imaging at Dentistry: innovations and applications
Oliveira VGB et al. Utilizing Texture Analysis technique in diagnostic imaging at
Dentistry: innovations and applications
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Braz Dent Sci 2024 Oct/Dec;27 (4): e4342
Utilizing Texture Analysis technique in diagnostic imaging at
Dentistry: innovations and applications
Oliveira VGB et al.
Utilizing T exture Analysis technique in diagnostic imaging at Dentistry: innovations and applications
Oliveira VGB et al. Utilizing Texture Analysis technique in diagnostic imaging at
Dentistry: innovations and applications
Date submitted: 2024 Apr 19
Accept submission: 2024 Nov 22
Victoria Geisa Brito de Oliveira
(Corresponding address)
Universidade Estadual Paulista “Júlio de Mesquita Filho”, Instituto de Ciência e
Tecnologia, Departamento de Diagnóstico e Cirurgia. São José dos Campos, SP,
Brazil.
Email: victoria.gb.oliveira@unesp.br
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