Non-contact thermal imaging as potential tool for personalized diagnosis and prevention of cellulite
Journal of Thermal Analysis and Calorimetry
Non-contact thermal imaging as potential tool for personalized diagnosis and prevention of cellulite
Joanna Bauer 0 1 2
Martyna Grabarek 0 1 2
Agnieszka Migasiewicz 0 1 2
Halina Podbielska 0 1 2
0 Department of Cosmetology, Faculty of Physiotherapy, Wrocław University School of Physical Education , Wrocław , Poland
1 Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology , Wrocław , Poland
2 & Joanna Bauer
Cellulite, the problem of dimpled appearance of the skin, affects approximately 85% of female population in developed countries and is classified as one of the worst tolerated by women deteriorating their quality of life and self-esteem. There is a lack of early, objective, quantitative and personalized diagnosis of different stages of cellulite, thus making prevention or early therapeutic intervention difficult. We have demonstrated the efficacy of thermal imaging using IR thermography in a group of female volunteers with different stages of cellulite. By analyzing the superficial temperature distribution of the body, it was possible to diagnose the cellulite stage. The thermal images of posterior site of thighs were recorded, and cellulite areas were identified for further quantitative analysis. We used a custom-designed classification scheme for automatic recognition of the different stages of cellulite as per the well-known Nu¨ rnberger-Mu¨ller diagnosis scheme. It was possible to diagnose the cellulite stages with over 80% accuracy. The accuracy can be further increased to over 97% using a threshold value correction scheme. Our work has shown that IR thermography when coupled with computer-aided imaging analysis and processing can be a very convenient and effective tool to enable personalized diagnosis and preventive medicine to improve the quality of life of women with cellulite problem.
Thermal imaging; Infrared thermography; Preventive medicine; Cellulite; Skin imperfection; Personalized medicine
Cellulite, also known as lipodystrophy, edematous
fibrosclerotic panniculopathy, adiposis edematosa,
dermopanniculosis deformans or status protrusus cutis, refers
to as pathological changes in skin formation that is
manifested in numerous cavities and irregularities in skin
morphology. Cellulite can be considered to be one of the
civilization diseases linked to the modern lifestyle and way
of nutrition. As statistics show, the problem of
lipodystrophy affects women, predominantly. The number of
women affected by cellulite is constantly increasing. More
worrisome is that they become affected by cellulites at an
earlier age. For example, more than 90% of women over
the age of 30 have at least one of the symptoms of
lipodystrophy such as edema, local microvascular
disorders, abnormalities in adipose tissue structure and
decreased skin and subcutaneous tissue elasticity. It is
estimated that around 85% of mature women suffering
from cellulite are in the developed world [
Cellulite tends to appear more in the region of lower
extremities, thighs and buttocks. It can also occur at lower
abdomen, shoulders and breast. These are regions where
estrogen is responsible for fat deposition [
]. The disease is
accompanied by a chronic inflammatory process involving
fat tissue, connective and peripheral lymphatic and blood
system, as well as osteoarthritis–fibrosis degenerative
materials of the subcutaneous tissues. The first symptoms
of cellulite may appear as early as during adolescence and
can affect about 12% of girls. The percentage of illnesses
increases significantly during pregnancy by about 20% due
to increased supply of female sex hormones. The increase
in the number of cases is also observed in menopausal or
perimenopausal women (nearly 25%) due to a decline in
steroid concentrations and water management disorders
Cellulite negatively affects women quality of life and
self-esteem. It has been classified as one of the
worst-tolerated symptoms by women [
]. As such, it attracted
numerous studies related to pathophysiology [
6, 9, 10
] prevention and treatment through various
anti-cellulite therapies [
symptoms appear as uneven, wrinkled skin surface with
numerous thickenings, bulges and furrows, which represent
defects and a weakening of connective tissues. The
appearance of cellulite is associated with morphological,
biochemical and structural changes. [
]. It is related to
a loss in the quantity and function of dermal collagen
fibers, which, in turn is connected with skin laxity,
flaccidity and sagging [
]. Personalized, early stage
intervention can potentially prevent the occurrence of
cellulite and is one of the goals of our present study.
Among the pathophysiology theories of cellulite formation,
the so-called vascular theory is relevant to our study and
will be discussed here.
The vascular theory of cellulite etiology classifies it as a
degradation process initiated by a deterioration of the
dermal vasculature. Usually cellulite starts with
microcirculation disorders and stagnation within the blood vessels
and lymph vessels, leading to decreased permeability. As a
consequence, there is a disruption of the nutrient supply to
the cells and disturbances in the discharge of unnecessary
metabolic products. This, in turn results in excessive
accumulation of fluid in the intercellular spaces [
The loss of the capillary networks  is caused by
clumped fat cells (adipocytes) that inhibit venous return
] and retain excess fluid within the dermal and
subcutaneous tissues [
]. Vascular changes begin to occur
within the dermis, protein synthesis decreases, and tissue
self-repair is affected. Beneath the skin, protein clumps
accumulate around the enlarged adipocytes. At this stage,
cellulite is, however, not seen and skin still appears as
smooth. These early symptoms can be only noticed when
skin is pinched between the thumb and the forefinger,
giving ‘orange peel’ appearance. In the next stage, hard
reticular proteins form around fat nodules within the
dermis, which is thinning while subcutaneous fat tissue
protrudes. Together, they translate into characteristic for
cellulite skin alterations which can be seen at surface [
Based on the consistency of skin, cellulite is usually
classified into four general types: hard, soft, edematous and
mixed. Hard cellulite occurs mainly in the slim and
physically active people. It often affects teenagers or young
women, whose skin is relatively tight and firm. Defects
only appear during a change in body position or during a
pinch test. Over time, this can transform into so-called soft
cellulite, usually found in mature women with low physical
activity. It is caused by hypotonia (loss of muscle mass,
strength and tone) and an increase in the fat volume.
Within the subcutaneous tissue, telangiectasia and
microcytosis occur. Irregular beads and nodules can appear too
and cause pain. This stage of cellulite is characterized by a
progressive loss of elasticity, pliability and skin flaccidity
]. Edematous cellulite is relatively rare and
manifests itself with a significant increase in the volume of the
lower limb tissues. The skin around the lesions is thin, pale
and distinctly colder, with microcirculation disorders and
local hypothermia. The patient has a severe feeling of
heavy and sore legs. Characteristic for this stage is a
positive result of Godet test (a dimple in the skin appearing
when pinched). Statistically, mixed cellulite occurs in
women most frequently. Here, one patient can have
different types and stages of cellulites at different locations of
the body .
Cellulite develops in a few overlapping stages, which
can take months, and sometimes even years, to fully
manifest. A number of classification schemes can be found
in the literature. Table 1 depicts one of the oldest and most
commonly used classification schemes proposed by Nu¨
rnberger and M u¨ller in 1978 [
]. This classification
scheme is based on palpation (Godet test) and visual
evaluation of skin. It is simple and the most convenient for
daily use. However, it is very subjective as it requires
physical pinching of the patient and visual inspection by a
trained dermatologist or cosmetologist.
Janda and Tomikowska [
] proposed in 2014 another
classification scheme that also identified 4 stages of
cellulite formations taking into account clinical,
thermographic and histopathological changes in the skin and
subcutaneous tissue. This classification, however, is more
complicated and requires special equipment and very
highly trained personnel for operation, thermography and
dermatological analysis and diagnosis. As such it is
difficult to implement in a decentralized, personalized,
Hexsel et al. [
] have proposed a photonumeric cellulite
severity scale (CSS). Five key clinical morphologic
features of cellulite were identified in this scale:
A. the number of evident depressions;
B. depth of depressions;
C. morphological appearance of skin surface alterations
like ‘orange peel’ appearance, ‘cottage cheese’
appearance or ‘mattress’ appearance;
D. grade of laxity, flaccidity or sagging skin; and
E. the classification scale originally described by Nu¨
rnberger and Mu¨ ller.
The severity of each of above-described items is graded
from 0 to 3, allowing to calculate a final sum of scores that
vary from 1 to 15. Based on the final numeric score,
cellulite is further classified as mild (total score 1–5),
moderate (total score 6–10) or severe (total score 11–15). This
elaborate scoring scheme provides a thorough quantitative
underpinning in the diagnoses although pinching of the
skin may still be required to make cellulites more visible.
As it can be seen in the above examples, diagnoses and
clear assessments of cellulite stages are not trivial.
Alternative means of cellulite diagnosis, however, may require
the use of advanced diagnostic tools and methods such as
magnetic resonance imaging, computed tomography, static
and dynamic elastography, contact thermography,
videocapillaroscopy and classical, high frequency or Doppler
]. These diagnostic tools are not always
available in a typical aesthetic or cosmetic practices or
dermatology centers due to high capital cost and
Here, we demonstrate the use of non-contact thermal
imaging as an alternative method for cellulite stage
classification for personalized diagnosis. Contact
thermography has been used for cellulite diagnosis where specialized
liquid crystal mats are required to be applied to the suspect
site of the body [
]. Infrared (IR) thermography, in
contrast, does not require any direct contact with the patient
and allows remote and non-contact assessment of the
surface temperature distribution of the examined body
]. The method is widely used for superficial
temperature distribution’ measurements in medicine, e.g.,
screening tests , prevention [
], diagnosis [
treatment’ assessment [
], physiotherapy [
well as personalized medicine [
It is completely safe for the patient because it is based
on the measurement of the electromagnetic radiation in the
infrared range naturally emitted from the human body itself
]. This allows obtaining information about both
physiological and pathological processes in the examined
part of the body. Dermatological effects such as cellulite
manifest as superficial temperature changes that can be
conveniently detected, quantified and machine-analyzed
using IR thermography for automatic decision making. In
this work, non-contact thermography has been used for an
objective and quantitative assessment of different stages of
Materials and methods
The small clinical trial involving human volunteers was
conducted in conformance to the ethical guidelines of the
Declaration of Helsinki following the approval of the
Senate Ethics Committee for Scientific Research at the
University School of Physical Education in Wrocław.
We created a database of a total of 118 thermal images
at a resolution of 320 9 240 pixels. These images were
taken under informed written consent from the female
volunteers, aged 19–22 with different stages of cellulite,
which had been diagnosed a priori by a licensed
cosmetologist using the Nu¨rnberger–Mu¨ller scale (Table 1).
Experiment strictly followed the recommendations of
European Association of Thermology for thermographic
measurements in medical applications, including
preparation of the volunteers, exclusion/inclusion criteria for
subjects, environmental conditions and imaging system
operational requirements [
In order to maintain constant ambient conditions,
measurements were taken in one room at a specific time of the
day. The air temperature was also monitored at 22–24 C,
and humidity was 35–40%. Before the testing, volunteers
were asked to expose their thighs. Then, they were asked to
stay in standing positions for 20 min so that they could
adopt to the room conditions. No severe physical activity
was performed so that the body temperature could be
stabilized. Thermal images of the backside (posterior part) of
thigh were recorded for each volunteer with a
thermographic camera FLIR T335 operating at a spectral range of
7.5–13 lm with a temperature sensitivity of 50 mK at
30 C. Images were taken at a fixed distance of 1.2 m.
All thermal images were analyzed using a ThermaCAM
Researcher Pro 2.10 software, which allowed automatic
normalization of the temperature distribution for all
images. This normalization was needed because the
temperature range of the originally recorded images varied
slightly for which the thermal imaging camera needed to
adjust automatically to find the coldest and warmest point
in the analyzed area. After normalization, the lowest and
highest temperatures for these images were set to the
values of 20.6 and 36.2 C, respectively.
Figure 1 shows typical thermal images taken on
volunteers. We assume that a non-pathological skin would
provide a reasonably uniform temperature distribution in the
thermal image (Fig. 1a). The cellulite in thermal images
forms contours of surfaces and shapes with uneven and
higher temperature than the surrounding tissues (Fig. 1b).
These are the regions of microcracks and swollen blood
vessels that cause thermal irregularity. Suspect cellulite
areas have high heterogeneity in temperature distribution
and were marked manually (Fig. 1c–e) using the
ThermaCAM software, which indicated a temperature
difference with respect to the surroundings.
These thermal images were then further analyzed in
ImageJ software to measure parameters such as the number
of cellulite irregularities and their corresponding areas in
pixels (px). This made it possible to obtain quantitative
parameters such as the cumulative area of cellulite spots.
Four different classification parameters were tested:
Classifier 1 Number of irregularities Classifier 2 Cumulative area of irregularities/px Classifier 3 Ratio of cumulative area of irregularities and area of thighs/px
Classifier 4 Product of irregularities’ number and area
For the purpose of this study, the collected database was
divided into 2 subsets: learning (59 images) and testing (59
images). This division allowed us to evaluate the
recognition accuracy of the tested images for different stages of
cellulite based on learning outcomes. After initial testing,
the classification system was optimized using a novel
optimization scheme discussed in the following section.
Results and discussion
For quantitative analysis that can be automated for
personalized medicine, it is important to define parameters
that will let high-quality classification of cellulite stages for
an unambiguous distinction using thermal images. In the
preceding section, we have defined four such parameters
(classifiers). We now examine whether there is a
relationship between our proposed classifiers and clinically
identified different stages of cellulite. For this, we need an
essential step for accepting or rejecting a particular
classifier (Fig. 2a–d). This relationship would represent the
dependence of individual classifiers’ mean values on the
respective subgroups of volunteers with different stages of
clinically diagnosed cellulites (stages 0, 1, 2 and 3,
Table 1). This is an acceptable starting point as the
respective mean values differ for different stages of
cellulite (Fig. 2a–d).
The validity of these parameters has been further tested
for distinct recognition of different stages of cellulite. This
has been carried out by calculating values for all four
classifiers for each of the 59 images in the learning
database. We then ranked these classifier values with respect to
the cellulite stages to obtain a robust threshold values that
can allow us easy distinctions between cellulite stages
using image analysis. Table 2 shows the lower and upper
limits of these values. It is acceptable that there will be
some overlap between successive stages such as 0–1, 1–2,
2–3. It is, however, not acceptable that there would be an
overlap between three stages such as 1–2–3. It can be seen
that Classifiers 1 and 2 were unable to distinguish between
cellulite stages such cases (Table 2, cellulite stages 1–2–3).
This means that the classifying powers of these two
parameters are low. On the other hand, Classifiers 3 and 4
do not have such ambiguity and possess stronger
classifying power. For further cellulite stage recognition, we
used only Classifiers 3 and 4.
We then use images from the testing database to check
whether the threshold values defined from the learning
database (Table 2) allows to distinguish different stages of
cellulite. Table 3 provides the accuracy of the stage
assignments based on such thresholds in Classifiers 3 and
4. Clearly, Classifier 3 shows, on an average, a better
ability of distinguishing different stages of cellulite (83.05
We believe that the accuracy can be further improved by
resorting to algorithm-based corrections, machine-based
identification of cellulite and a larger population for
learning database. To validate the first point, we make
some corrections of the threshold value separating the
particular cellulite stages as follows:
Correction no. 1 The threshold values of classifiers’
ranges were increased about 0.001 px (Classifier 3) and
10,000 px (Classifier 4).
Correction no. 2 The threshold values of ranges were
decreased adequately by 0.001 px (Classifier 3) and
20,000 px (Classifier 4).
The results of these corrections are listed in Table 4.
We then applied another correction:
Fig. 2 Dependence of
classifiers mean values on the
cellulite stage for the whole
analyzed population: Number of
irregularities (a), cumulative
area of irregularities (b), ratio of
cumulative area of irregularities
and area of thighs (c) and
product of irregularities’
number and area of
Correction no. 3 As a classification criterion both of
Classifierss 3 and 4 were taken altogether. During this
stage, we checked which of the images taken from the
tested database complied with both Classifier 3 and
Classifier 4 at the same time. The decision was taken
only in case where the same stage of cellulite of each
classifier was achieved. The images where there was a
conflict between assigned stages between Classifiers 3
and 4 had been termed as undefined. The results after
such corrections are given in Table 5.
The final, optimized threshold values after above
corrections are given in Table 6.
Similar to the previous study carried out by
Nkengne et al. [
], our report shows that IR thermography
can be used for non-contact diagnostic imaging of cellulite.
The significance of our investigation is in defining a
quantitative methodology of image analysis to obtain high
accuracy recognition of different stages of cellulite. We
have shown the feasibility of using IR thermography for
automatic recognition of these stages. The accuracy can be
further improved by using pattern recognition for more
automated cellulite identification, algorithm-based
corrections and a larger-scale clinical investigation for machine
It is worth to emphasize that IR thermography provides
relatively high reproducibility and low-cost diagnosis
opportunity compared to other state-of-the-art imaging
methods such as computed tomography, ultrasound and
magnetic resonance imaging. An additional advantage of
thermal imaging is a noninvasive nature and safety of
measurement that can lead to practical implementation in
personalized diagnosis, when supported by automatic
computer processing. It should be borne in mind, however,
that in order to obtain reliable results the patient must be
appropriately prepared for such diagnostic imaging
following recommendations made by regulatory authority
]). Observation of thermal changes can lead to
personalized, early stage intervention which will prevent or
delay the occurrence of cellulite and significantly help
dermatologists and cosmetologists to monitor the effects of
We have shown the feasibility of IR thermography in
accurately defining different stages of cellulites ([ 97%).
Women with diagnosed advanced cellulite have more
asymmetrical and heterogeneous superficial temperature
distribution seen as spots of different sizes and shapes on
the thermal image. This can be successfully imaged and
analyzed using parameters developed in the study based on
some threshold values that have been assigned to
distinguish different stages of cellulite. In addition to show the
feasibility of IR thermography as a potential tool for
personalized diagnosis of cellulite, our study also provides an
image analysis protocol that can be extended to
computeraided thermal image analysis. The protocol will facilitate
early and personalized monitoring of cellulite development
and allow preventive intervention, thus improving women
quality of life and self-esteem.
Acknowledgements The study was conducted thanks to the statutory
fund from the Polish Ministry of Science and Higher Education
(MNISW) which is gratefully acknowledged by the authors of this
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