Multispectral 3D Surface Scanning System RoScan and its Application in

Inﬂammation Monitoring and Quantiﬁcation

Adam Chromy

1,2

Department of Control and Instrumentation, Faculty of Electrical Engineering and Communications,

Brno University of Technology, Technicka 3082/12, 616 00 Brno, Czech Republic

Central European Institute of Technology, Brno University of Technology,

Purkynova 656/123, 612 00 Brno, Czech Republic

Keywords:

Multispectral Imaging, 3D Body Scanning, Thermal Imaging, Inﬂammation Monitoring, Inﬂammation

Quantiﬁcation, Treatment Evaluation.

Abstract:

This paper presents experimental multispectral 3D surface scanning system RoScan, which is capable of cap-

turing 3D models of a surface, containing a spatial representation of the object, colour of each point of the

surface, its temperature and roughness. Such models are provided with accuracy up to ±0.12 mm and thermal

resolution of 0.05

◦

C, what makes it suitable for 3D thermal body scanning in medicine. Basic principles,

parameters, and functional capabilities are discussed, and developed tools for data analysis are presented. The

RoScan system is suitable for early detection of inﬂamed regions and its objective quantiﬁcation. It can be

also used for evaluation of treatment suitability or for monitoring during a recovery process. To show this,

the case study monitoring of inﬂammation related to eczema caused by an allergic reaction is presented. The

inﬂammation development is studied using RoScan during eczema growth and after the application of two dif-

ferent external dermatologics – Protopic



0.1% topical ointment and ointment from shea butter and coconut

oil. On this particular subject, measured characteristics demonstrated a stronger effect of Protopic



0.1% on

eczema healing, as the evolution of inﬂammation in the area treated with this dermatologics started to recover

earlier and culminated on the lower value of temperature gradient then the second ointment.

1 INTRODUCTION

During last years, availability of thermal imagers

moved from expensive and bulky systems to afford-

able and practical solutions (Coffey, 2012). Develop-

ment of sensors and ﬁlters reaches such advances that

thermal cameras can be found already in smartphones

in the price range of up to 700 EUR (Hardwicke et al.,

2016). Even greater progress is evident in 3D scan-

ning market, where a bundle of new 3D scanning de-

vices is announced each year. Both technologies, al-

though they are mainly applied in engineering, has

capabilities, which can be useful as well in medicine.

Almost every injury, many diseases or pathologi-

cal changes are characterized by increased blood ﬂow

and stronger cellular metabolic rate in the affected re-

gion, what causes the local increase of temperature

(Chang et al., 2008). Such local thermal deviations

can be detected and visualized by thermal cameras,

working in the long-wave infrared spectrum (LWIR).

Digital Medical Thermal Imaging (DMTI) is used in

many medical applications nowadays, especially in

inﬂamed tissue analysis (Hilton-Jones, 2003; Ring

and Ammer, 2012) and cancer detection (Lu and Fei,

2014).

But all current DMTI solutions suffer from a sig-

niﬁcant drawback: although the 2D thermal imaging

is able to quantify the temperature of the individual

pixels of the image, the DMTI is still considered a

mere qualitative tool, enabling us to distinguish be-

tween the physiological and non-physiological states

of the body but lacking the ability to quantify them

(Vardasca and Simoes, 2013; Ju et al., 2005). This is

due to three main drawbacks of DMTI: almost impos-

sible deﬁnition of region of interest (ROI) in thermal

image due to lack of recognizable clearly bounded

thermal features in the image; distortions caused by

transforming 3D world to 2D representation (imag-

ine the ﬂoor plan of skyscraper – you know the co-

ordinates inside the building but you do not know the

ﬂoor); and dependence of the thermogram on the view

of the camera.

106

Chromy, A.

Multispectral 3D Surface Scanning System RoScan and its Application in Inﬂammation Monitoring and Quantiﬁcation.

DOI: 10.5220/0006557601060113

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 2: BIOIMAGING, pages 106-113

ISBN: 978-989-758-278-3

Figure 1: Scanned model contains information from spatial

(left), visible (middle) and LWIR spectrum (right).

The medical quantiﬁcation itself is a general long-

term problem that permeates across the entire health-

care system and that is still not reliably solved.

This paper presents multispectral 3D scanning

system RoScan dealing with these current drawbacks

of DMTI by data fusion of 3D body scans with colour

images and thermal images. This combination of sen-

sors provides 3D surface scans covered with colour

and thermal information in high resolution (Fig. 1),

what enables thermal medical quantiﬁcation. Thanks

to the colour layer, the ROI can be precisely selected,

the 3D model enables undistorted measurements, and

thermal layer exposes non-physiological areas and

provides quantiﬁable index reﬂecting the severity of

a disease.

In the following text, the abilities of RoScan are

outlined and the case study of monitoring inﬂamma-

tion related to eczema caused by an allergic reaction

is presented. The inﬂammation development is stud-

ied using RoScan during eczema growth and after the

application of two different external dermatologics.

Such studies are currently evaluated by methods

that are based on different scoring systems (Necas,

2011). Individual scoring systems have different pos-

itive or negative features, but they have one thing in

common - they are extremely subjective, coarse and

insensitive (Sprikkelman et al., 1997). As a result

of this, it is impossible to quantify the immediate

response of the human body immediately after drug

administration. The scoring systems only reﬂect the

long-term effect of drugs, and even if it is really sig-

niﬁcant (Lacarrubba et al., 2015).

As shown in the further text, proposed multispec-

tral 3D scanning system RoScan enables the possibil-

ity of objective monitoring of fast temporal changes

in the treated skin. Quantiﬁcation of dermatitis by

RoScan brings also higher sensitivity and selectivity

compared to the current state of the art, as well as

availability of more other quantitative parameters dur-

ing the evaluation.

2 ROSCAN SCANNING SYSTEM

Because the RoScan, a multispectral scanning system

using this sensor combination, brings a novel concept

in medical imaging, an extra section of this paper was

devoted to introduce basic principles of this method

and, in particular, to present its capabilities. From

this information the reader can get an idea in which

another research projects this method can be helpful.

Design of RoScan is built on the basis of Robotic

3D scanner (Chromy and Zalud, 2014a), which is

composed of high-accurate 2D proﬁle laser scanner

mounted on end-point of 6-axis industrial robotic ma-

nipulator. The robot used in this device is EPSON

C3, which is reaching accuracy of end-point place-

ment ∆

= ±0.013 mm (Epson Robots, 2011) and the

laser scanner is MicroEpsilon ScanCONTROL2750-

100 with accuracy ∆

= ±0.027 mm (Micro-Epsilon,

2008).

Sensoric head of this robotic 3D scanning system

has been extended by LWIR thermal camera Xenics

GOBI1954 and colour camera ImagingSource DFK

51BG02.H (Fig. 2).

The thermal imager has resolution 384 × 288 pix-

els, pixel pitch 25 µm and spectral response of wave-

length range 8 − 14 µm with the thermal resolution

of 50 mK (Xenics, 2009). Colour camera Imaging-

Source DFK 51BG02.H has resolution 1600 × 1200

pixels and is supplied with 1/1.8

Sony CCD chip.

According to (Chromy and Zalud, 2014b), overall

spatial accuracy

of entire RoScan scanning system is

∆

Xmax

= ±0.12 mm and repeatability of thermal mea-

surements is 0.05

◦

2.1 Capturing 3D Surface Models

Capturing of 3D surface model spatial data is based

on moving with the sensoric head around the scanned

object by the robotic manipulator and along prede-

Figure 2: RoScan overview with focus on sensoric head.

Term accuracy in this context can be deﬁned as max-

imal distance between computed (measured) position of

point relative to true position of point at 99.7 % of mea-

surements (±3σ)

Multispectral 3D Surface Scanning System RoScan and its Application in Inﬂammation Monitoring and Quantiﬁcation

107

Figure 3: Software for capturing multispectral 3D surface

models during process of scanning.

ﬁned scanning trajectory. Using robotic manipula-

tor empowers both ﬂexibility of movement and high-

accuracy of captured data, what is usually compro-

mise between these parameters at commercially avail-

able scanners (Curless, 1999).

At each point of scanning trajectory, the laser

scanner measures a distance to the scanned object

along measuring line and generates output in form of

2D distance proﬁle. Each captured proﬁle is then fur-

ther transformed to the world coordinates and linked

with neighbouring data into single mesh structure.

This entire process, as well as required transforming

equations, is deeply described in (Chromy and Zalud,

2014a).

The software, developed for this system, allows

deﬁnition of custom trajectories composed from geo-

metric primitives using the simple scripting language.

Such trajectories can be then easily launched for scan-

ning (Fig. 3).

2.2 Mapping 2D Images onto Surface

During the scanning process, data from the colour

camera and thermal imager are collected, and after

building the 3D model mesh, they are projected onto

the 3D surface. At this point, the ray-tracing algo-

rithm (Suffern, 2016) is used, which examine visibil-

ity of each point of mesh from the camera. If the sin-

gle point is visible from several images, the result-

ing temperature is given as average of values from

these images. The colour in such multi-imaged points

is given as linear interpolation between colours from

these images, weighted by the angle relative to 3D

surface normal, since the lightness of colour is inﬂu-

enced by the angle of light reﬂection.

Since the ray-tracing examination of point visibil-

ity is computationally demanding issue, the algorithm

uses Octree data structure (Kunii, 2012; Burian et al.,

2014), which divide the area of the 3D model into spa-

tial cubes, in which the parts of the mesh are classiﬁed

into.

This entire texture mapping process is more

deeply described in (Chromy and Klima, 2017).

2.3 Mutual Calibration of Sensors

For proper mapping of thermal and colour 2D images

onto 3D surface model, it is necessary to know very

precisely the intrinsic parameters of camera, as well

as its 6-DOF position

, from which the image has

been captured. This position is computed from the

position of sensoric head and the position of camera

relative to the sensoric head.

In order to estimate these parameters, we use the

calibration method based on capturing images of cal-

ibration pattern from several angles and further com-

paring of evaluated extrinsic parameters with the lo-

cation given by robotic manipulator. The pattern is

made from PCB with the heated copper layer, which

is visible on all sensors simultaneously. Calibrating

cameras using this method will ensure that the images

ﬁt exactly at the right place on the 3D surface.

This method is described in (Chromy, 2017) with

more details.

2.4 3D Model Analysis

An important part of RoScan scanning system is soft-

ware tool for displaying and analyzing scanned im-

ages (Fig. 4). It provides functionality not only to

browse through 3D models but also for various mea-

surements of spatial properties of selected regions.

It also supports export to standardized formats (e.g.

PLY or PTS) for further processing of captured data

in other 3D software tools.

It allows showing the scans in 4 different modes:

• Colour – Basic view as clinician can see the pa-

tient. This layer is mostly used for ﬁnding the

visible landmarks, which are used for orientation

(pigmented spots, markers drawn on skin, etc.).

Such points can be highlighted to be visible also

in other layers.

• Temperature – The layer with false colours re-

lated to the temperature of skin. The value or par-

ticular point can be examined by clicking. Slid-

6-DOF position means 3 coordinates for camera posi-

tion and 3 coordinates of rotation, deﬁning the direction of

camera view.

BIOIMAGING 2018 - 5th International Conference on Bioimaging

108

Figure 4: Software for displaying and analyzing multispec-

tral 3D surface scans.

ers can be used to adjust colour-temperature map-

ping in order to see better the contrast between

inﬂamed and healthy tissue.

• Roughness – Since we use the laser scanner

working on triangulation principle (Smith and

Zheng, 1998), besides position of reﬂected beam

a divergence of reﬂected beam is also mea-

sured. This value corresponds to roughness of the

scanned surface and can be also visualized with

false colours.

• Surface Only – Sometimes it is important to see

tiny details of the surface (like edges of the scars

or boundaries of chronic wound), which are nor-

mally hidden in colors. In such cases, only the 3D

surface can be displayed, without any coloring.

On each layer, the measurements tool can be acti-

vated. Following parameters can be evaluated in se-

lected points or regions of interest (ROI):

• Distances [mm] – between deﬁned points (di-

rectly, along the surface), circumferences of ROI.

• Angles [deg] – angle between three deﬁned points

(e.g. vertebrae positions).

• Surface area [mm

] – of entire model or ROI.

• Volume [mm

] – of entire model or ROI deﬁned

by cutting plane or deﬂected cutting surface.

• Color – of selected point or average color of ROI.

• Roughness – provides dimensionless index cor-

responding to roughness of selected point or aver-

age roughness of ROI.

• Temperature [

◦

C] – of selected point or average

temperature of ROI.

At triangulation laser scanner, the position of reﬂected

beam of detector deﬁnes the measured distance

Since the system is completely developed by au-

thor from the scratch, it can be easily adapted to any

other application, within or outside of medical sector.

3 MATERIALS AND METHODS

The case study has been performed on subject suffer-

ing with the allergy on hazel allergens. The experi-

ment began when itchy and red lesions appeared on

the superior side of the left forefoot, few hours af-

ter ingestion of small amount of allergic substance.

The area of the lesion had been highlighted by mark-

ers drawn on the skin and was divided into two parts,

marked as V and K (Fig. 6).

During the ﬁrst stage of the experiment, the sub-

ject was repeatedly scanned

using RoScan during

50 minutes period. After that, Protopic



0.1% topical

ointment was applied to the area K and ointment from

shea butter and coconut oil was applied to the area V .

During the second stage, the subject was once

again repeatedly scanned using RoScan for following

31 hours. In ﬁrst minutes, when a reaction to der-

matologics was expected, the spacing between mea-

surements was 2-3 minutes, then approx. 15 minutes

and then about 45 minutes. Most of the measure-

ments were taken during ﬁrst 4 hours when subject

was present in the laboratory. At following 27 hours,

only 3 measurements were taken due to unavailability

of the subject to come for measurements.

When processing the results, the areas K and V

were selected on each thermal 3D scan using the

colour layer of the 3D surface model, where mark-

ings drawn on the skin of the subject are visible (Fig.

5). Average temperature and selected surface area of

each region were then computed

. The area of se-

lected ROI serves as controlling value since it shall

Figure 5: Selection of areas on color layer (left) and aver-

aging of temperature on thermal layer (right).

Exact time of scanning has been saved and used for

further evaluation.

Both values are directly provided by RoScan software

tool

Multispectral 3D Surface Scanning System RoScan and its Application in Inﬂammation Monitoring and Quantiﬁcation

109

Figure 6: Affected area immediately after dermatologics

application.

stay unchanged at all samples, even if captured from

slightly different positions. The average temperature

was used as the quantitative parameter.

Because surface temperature of forefoot depends

also on physical activity or ambient heating, reference

temperature was measured on each scan. As a refer-

ence point, the area of letter V marked on the skin was

used. We are assuming, that external inﬂuences affect

the entire surface equally.

For quantiﬁcation of inﬂammation in affected

area, following metric has been applied:

(t) =

(t) − T

(t)

(0) − T

(0)

− 1 (1)

where T

(t) is average temperature of points be-

longing to the area A in time t, T

(t) is reference

temperature in time t. In this context, the mean-

ing of δ

(t) is relative change of difference between

area temperature and reference temperature, relative

to the time and state when dermatologics were ap-

plied

. Such relative metric had been chosen due to

the unequal distance of both areas from the edge of

the body, what causes differences in absolute values

of a temperature. This approach normalizes both val-

ues to the same scaled index and makes both areas to

be comparable between each other.

Uncertainties of measurements were evaluated ac-

cording to (Palencar et al., 2001) as the standard un-

certainty of indirect measurement, where all 4 values

inﬂuencing the results are measured with the same un-

certainty of ∆ = 0.05

◦

C. Particular uncertainties are

shown as error bars in following ﬁgures 8 - 10.

Note: Be aware, that purpose of this experiment

is not to evaluate treatment efﬁciency of both derma-

tologics, but to show that RoScan is able to quantify

e.g. δ

(1min) = 10% means that during ﬁrst 10 min-

utes after application, the gradient of area temperature rela-

tive to the ref. point has grown by 10%

Figure 7: Affected area 31 hours after dermatologics appli-

cation.

the inﬂammation and that is sufﬁciently sensitive for

monitoring of inﬂammation progress. The results of

this study bring information only about the reaction

of this particular subject to both dermatologics and

cannot be generalized to the human population.

4 EXPERIMENTAL RESULTS

Figure 8: Development of δ(t) during 31 hours after appli-

cation of ointments.

Development of δ

(t) and δ

(t) during 31 hours after

the application of ointments is shown in Fig. 8.

The temperature gradient in the area treated by

Protopic



0.1% culminated at 45% gradient relatively

to starting state. The area treated by ointment from

shea butter and coconut oil culminated at 65%. From

point of culmination, both areas are healing with a

similar trend.

Both areas were similarly progressing before ap-

plication of drugs, as shown in Fig. 9. Note, that

BIOIMAGING 2018 - 5th International Conference on Bioimaging

110

Figure 9: Development of δ(t) before application of oint-

ments.

Figure 10: Detail of δ(t) characteristics in ﬁrst 100 minutes

after application of ointments.

there is no signiﬁcant difference between the thermal

progress of both areas until application of ointments.

After that, the characteristics become different.

On Fig. 10, there is a detail of ﬁrst 100 minutes af-

ter application, which is not visible in full scale. The

temperature of area K is growing faster than in area

V , but after 10 minutes stops to grow and after that,

the increase during the time is signiﬁcantly slower.

5 DISCUSSION

Both areas are evolving in a similar way before appli-

cation of dermatologics (Fig. 9), what might rebut the

assumption, that both areas are affected by differently

advanced inﬂammation and that both areas would be

then evolving differently even if not treated. For fur-

ther considerations, we assume that both areas

are afﬂicted with inﬂammation of same severity, also

because of the same symptoms (same redness, rash,

and itching).

The active substance of Protopic



0.1% is

Tacrolimus (Lazarous and Kerdel, 2002), the topical

calcineurin inhibitor (TCI) working by weakening the

skin’s defense (immune) system, thereby decreasing

the allergic reaction and relieving the eczema (Baldo

et al., 2009). Since atopic dermatitis is skin inﬂam-

mation (Nedorost, 2012), which is partially caused by

immunologic factors (Grey and Maguiness, 2016), its

development should be reduced by dosing this drug.

On the contrary, the ointment from shea butter

and coconut oil has no similar active substance and

acts only as moisturizer (Tollefson et al., 2014), so

it should serve only for prevention from cracked skin

(Varothai et al., 2013).

This is in consensus with our observation from

Fig. 8, where Protopic



0.1% has a stronger effect

on stopping the development of eczema than ointment

from shea butter and coconut oil.

According to subject’s feelings, 2 minutes after

application of ointments, the area K started to be

strongly burning. This feeling culminated at 7 min-

utes and at 12 minutes burning and itching in the area

K fully stopped. Itching in the area V stays at the

same level during this period. The surface of area K

also became sticky and oozing, as shown in Fig. 11.

This report correlates with response of inﬂamma-

tion development in ﬁrst minutes after drug applica-

tion (Fig.10), where the K area is initially more in-

ﬂamed then V , but then inﬂammation decrease speed

of development more than in area V . The burning

after an application is the well known side effect of

Protopic



0.1% (Lazarous and Kerdel, 2002), but its

cause is not known. Anyway, it is recognizable on

time evolution of 3D thermogram.

6 CONCLUSIONS

The presented paper introduced and evaluated capa-

bilities of novel imaging technique for inﬂammation

detection and monitoring. But the 3D + thermal +

colour data fusion technology is not limited only to

this domain – it can be used also in ﬁeld robotics (Ne-

jdl et al., 2015), autonomous mapping (Zalud et al.,

2015) or in augmented reality (Zalud, 2006)

RoScan, the experimental equipment using this

technology, is designed to producing multispectral 3D

models of the body surface.

The main purpose of RoScan is being able to

quantify inﬂammatory processes inside the human

body. This inﬂamed region should be close to the

Multispectral 3D Surface Scanning System RoScan and its Application in Inﬂammation Monitoring and Quantiﬁcation

111

Figure 11: Oozing surface of area K after application of

Protopic



0.1%.

skin, or sufﬁciently ”strong” to be able to inﬂuence

even the skin temperature. Case study above shown,

that using RoScan for quantifying inﬂammation is

possible.

RoScan can be useful also for early detection of

inﬂamed areas since its sensitivity is higher than com-

monly used methods, which are mostly based on vi-

sual observations (Lipsky et al., 2004; Krysko et al.,

2008). Looking at differences between Fig. 6 and Fig.

7, it is clear, that RoScan brings more evidence-based

diagnostic data, which are normally invisible. This

was also shown in the presented case study.

Multispectral 3D scanning can be useful even if

the symptoms are visible. The ability to preserve the

exact condition of the patient’s body (for comparison

during the next visit at the clinician) brings the possi-

bility to objectively evaluate the progress of a disease,

even if changes are very small.

Extending 3D model with thermal information can

also help in assessing if spatial changes of a body are

caused by physiological (e.g. muscle growth) or non-

physiological factors (e.g. edema).

On the other hand, this technology has also many

limitations, when most important one is inhomo-

geneity of temperature distribution along body sur-

face. The areas of higher temperature can result not

only from inﬂammations but can be caused also by

anatomical constitutions, e.g. when arteries come

closer to the skin. In such areas, small inner in-

ﬂammations can stay unrecognised (false negative) or

the area itself can be detected as inﬂammation (false

positive). Interpretation of 3D surface thermogram

will still depend on experiences of a clinician, but it

will help him to quantify the problem, what common

methods can not.

ACKNOWLEDGEMENTS

This work was supported by grant No. FEKT/FIT-J-

17-4745 ”Intermodal 3D data registration in health-

care” ﬁnanced from Internal Science Fund of Brno

University of Technology; by grant No. 692470,

H2020, ECSEL-04-2015-Smart Health, ”Advancing

Smart Optical Imaging and Sensing for Health (AS-

TONISH)”; and by grant No. FEKT-S-17-4234 ”In-

dustry 4.0 in automation and cybernetics” ﬁnanced

from Internal Science Fund of Brno University of

Technology.

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