Acta Polytechnica CTU Proceedings


doi:10.14311/APP.2018.18.0055
Acta Polytechnica CTU Proceedings 18:55–60, 2018 © Czech Technical University in Prague, 2018

available online at http://ojs.cvut.cz/ojs/index.php/app

UTILIZATION OF IMAGE AND SIGNAL PROCESSING
TECHNIQUES FOR ASSESSMENT OF BUILT HERITAGE

CONDITION

Petr Koudelka∗, Veronika Koudelková, Tomáš Doktor,
Ivana Kumpová, Daniel Kytýř, Jaroslav Valach

Czech Academy of Sciences, Institute of Theoretical and Applied Mechanics, Prosecká 809/76, 190 00 Prague 9,
Czech Republic

∗ corresponding author: koudelkap@itam.cas.cz

Abstract. Historical buildings represent invaluable heritage from the past and therefore their
protection is a very important task. Assessment of their condition must not cause damage accumulation,
thus the least possible volume removed from the structure is essential. As many historical buildings
in the Czech Republic are built using sandstone that can be considered as a typical heterogeneous
system, statistical signal processing is a promising approach for determination of the representative
volume element (RVE) dimensions. Such calculations can be carried out on the domain of logical
arrays representing binary images of the materials microstructure. This paper deals with processing of
image data obtained using SEM-BSE and high resolution flatbed scanner for determination of RVE
dimensions. Advanced image processing techniques are employed and results from calculation using
grayscale data are presented and compared with results calculated on the basis of color input images.

Keywords: heterogeneous systems, natural stones, image processing, K-means clustering, representa-
tive volume element.

1. Introduction
Conservation, rehabilitation and strengthening of the
built heritage are all claimed by modern societies re-
quiring development of appropriate intervention tech-
niques [1]. In order to facilitate a proper maintenance,
historic buildings’ condition has to be assessed on reg-
ular basis. Assessment itself must not cause damage
accumulation and has to be carried out with cau-
tion [2]. Therefore, in the case of material parameters
required for evaluation, the least possible volume of
removed sample is essential. The extracted speci-
men should represent the material at the level of a
constructional element and thus be suitable for investi-
gation of material properties leading to determination
of representative volume element (RVE).
There are several methods for quantification of

porosity and/or volume fractions of constituting
phases ranging from radiography imaging to experi-
mental methods such as mercury porosimetry. Among
available approaches, image processing routines offer
balanced ratio between equipment or time demands
and reliability, eventually reproducibility of results.
In this paper, possibility of utilization of image pro-
cessing techniques for consequent spectral analysis of
the specimens’ microstructures is demonstrated on a
set of natural stone samples commonly used in the
Czech historical buildings.

2. Materials
In the Czech Republic, many historical buildings have
been built using different types of sedimentary rocks.

In the Prague region, this involves particularly opuka
stone and different types of sandstone gathered at
various locations in the area. In many of the most fa-
mous Prague monuments including the Charles Bridge,
sandstones of the following types were used for both
architectural and decorative purposes:

(1.) Petřín sandstone - hard sandstone with ferrug-
inous matrix from Petřín quarry is a dark brown
quartz-rich stone with claystone fragments as ad-
dendum (see Figure 1). The average value of bulk
density is 1930 kg/m3 and porosity is 29.2 % [3].

(2.) Nehvizdy sandstone - middle grained porous sand-
stone from Nehvizdy quarry has horizontal layering
conditioned by alternation of middle grained lami-
nas and fine grained laminas (see Figure 2). Main
component of both clasts and matrix is SiO2. The
average value of bulk density is 1940 kg/m3 and
porosity is 24.33 % [4].

(3.) Sandstone from the Charles Bridge - light pinkish-
grey hard porous arkose (type of sandstone with
more than 25 % of the feldspar grains) is coarse
grained sandstone from Žehrovice (see Figure 3.
The average value of bulk density is 2030 kg/m3

and porosity is 25.16 % [3].

3. Methods
From the microstructural point of view, all of these
materials can be described as multi-phase heteroge-
neous systems (composites) suitable for analysis using

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http://dx.doi.org/10.14311/APP.2018.18.0055
http://ojs.cvut.cz/ojs/index.php/app


P. Koudelka, V. Koudelková, T. Doktor et al. Acta Polytechnica CTU Proceedings

Figure 1. Petřín sandstone sample captured using
high-resolution flatbed scanner.

Figure 2. Nehvizdy sandstone sample captured using
high-resolution flatbed scanner.

Figure 3. Žehrovice sandstone sample captured using
high-resolution flatbed scanner.

statistical signal processing methods [5, 6]. The con-
stituent phases are in this case harder particles, binder
(matrix) and pores. In order to utilize grayscale-based
statistical signal processing algorithms for RVE dimen-
sions assessment, acquired images have to be properly
segmented to set of logical arrays, where every array
contains structural information related to only one of
constituent phases.

3.1. Image acquisition
There are numerous methods and devices suitable for
acquisition of images in sufficient quality and reso-

lution for precise structural information extraction.
According to application of two different segmentation
techniques presented in this paper two different data
sources were selected:
(1.) SEM - MIRA II LMU (Tescan, CZ) SEM device
equipped with the back-scattered electron (BSE)
detector. The scanning was performed with iden-
tical operating parameters to guarantee the same
acquisition conditions. Samples were polished and
coated by a thin layer of carbon (thickness approx.
15 nm) prior to scanning to increase surface conduc-
tivity for SEM observations. The device produces
64 dpi 8-bit grayscale images (see Figure 4).

(2.) High resolution flatbed scanner - to compare re-
sults from SEM imaging with data produced with
commonly available device, images of other samples
were captured using high resolution flatbed scanner
EPSON Perfection V350 (Seiko Epson Corp., Japan)
with resolution 6400 dpi, 3.75µm pixel size and 16-
bit sRGB output images (Figures 1, 2, and 3).

3.2. Image processing
Several techniques have been developed for image seg-
mentation ranging from general purpose algorithms
to highly specialized single-purpose routines for spe-
cific applications. However, for successful and effec-
tive image segmentation, proper technique has to be
always combined with domain’s specific knowledge.
Generally, image segmentation procedure encounters:
i) thresholding, ii) clustering, iii) compression-based
methods, iv) histogram-based methods, v) edge de-
tection, vi) region-growing methods, vii) watershed
transformation, and viii) multi-scale segmentation.
Selection of segmentation algorithm or their combi-
nation depends not only on domain’s properties, but
also on character of the input data.

3.2.1. Segmentation of grayscale image data
It has been shown that for cellular metals thresholding
based segmentation on grey-scale image is sufficient
enough for reliable predictions of RVE dimensions and
numerical homogenization routines [6]. However for
natural stones results of simple thresholding may not
be satisfactory and more complex routines have to
be applied (see Figure 5). In this paper the follow-
ing briefly characterized methods have been used on
grayscale image data captured in SEM:
(1.) Gradient magnitude calculation - here multi-
dimensional filter with Sobel operator is used for
intensity gradient magnitude calculation to empha-
sise borders of objects in the image.

(2.) Morphological techniques - these operations are
based on dependence of a value of given pixel on val-
ues of pixels in defined neighbourhood characterized
by considered structuring element. There are two
general types of morphological procedures - opening
and closing. Opening is erosion (setting the value
of each pixel to the minimum value of the pixels

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vol. 18/2018 Image and signal processing for assessment of heritage condition

Figure 4. Arkose from the Charles Bridge (left) and Petřín sandstone (right) captured using BSE detector in SEM.

in the neighbourhood) followed by dilation (setting
the value of each pixel to the maximum value of
the pixels in the neighbourhood), whereas closing
is dilation followed by erosion [7]. More precise
morphological technique is the morphological re-
construction. Here, first steps in both opening and
closing of the image are followed by image recon-
struction, which is conceptually repeated dilation
between marker and mask image. Processing of
images using reconstruction is based on connectiv-
ity concept rather than a structuring element and
the routine repeats until object stability is reached,
i.e. mask image does not change with increasing
iterations.

(3.) Thresholding - the simplest segmentation method
based on a threshold value to convert a greyscale
image into a binary image [7]. It is based on the
premise that distinct objects in the image have dif-
ferent levels of brightness and that the value of
brightness is the same/similar in pixels of a continu-
ous object. In this method, determination of thresh-
old value depends on domain’s character and has
significant influence on segmentation results. When
threshold value is defined globally from the whole
image, its initial estimation can be performed using
Otsu’s method [8], which is histogram shape-based
image thresholding algorithm. Possible problems
arising from illumination inhomogeneity may be
overcome by local thresholding. Instead of having
a single global threshold value, the algorithm al-
lows threshold itself to be smoothly varied across
the image. The initial value can be determined
by various approaches (median value of selected
neighbourhood, Bernsen method, Mid-gray method,
Niblack’s, Sauvola’s method, etc.).

(4.) Watershed transformation - for the purpose of
watershed transformation, grayscale image is con-
sidered as a topographic relief defined by intensities
of individual pixels. Principle of this transformation

is directly derived from behaviour of water drops
falling on a topographic relief. Water drop follows
(flows along) gradient of a path to reach a local
minimum (minimum altitude in an area enclosed
by a topographical border). Then, watershed of
a relief corresponds to the limits of the adjacent
catchment basins of the drops of water [9, 10].

(5.) Edge detection - segmentation of the image can
be also performed using edge detection to iden-
tify individual elements in the image (in this case
individual particles of stones) with consequent ap-
plication of methods mentioned hereinbefore. Many
of edge detection methods (e.g. Canny [11]) are
based on intensity gradient magnitude calculation,
when edges are detected at locations, where the gra-
dient is maximal. Gradient calculation is followed
by non-maximum suppression. Image is searched
to determine if the gradient magnitude assumes a
local maximum in the gradient direction producing
logical array of edges referred to as thin edges. Then
a two-level thresholding with hysteresis has to be
applied in order to filter isolated pixels representing
isolated local maxima.

3.2.2. Segmentation of colour image data
Particularly when SEM imaging device is not avail-
able for image acquisition, one can be interested in
possibility of segmentation of images captured by gen-
eral purpose devices that are commonly available.
This approach is in this paper shown on colour image
segmentation of images captured by high resolution
flatbed scanner. All image segmentation methods can
be applied, when full colour image is simply converted
into monochromatic form at the disadvantage of loss
of information during the conversion. However, on
the colour images, clustering approach is promising
even though computational requirements may be high
according to size of input data. Here, K-means clus-
tering is a common method of cluster analysis. To

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P. Koudelka, V. Koudelková, T. Doktor et al. Acta Polytechnica CTU Proceedings

Figure 5. Result of improper segmentation in grey in-
tensity space leading to substantial loss of information
with upscaled detail of over-segmented structure.

separate objects in analysed space and aggregate them
into groups (clusters), K-means clustering treats each
object as having a unique location in space [12]. Par-
titions are found such that objects within each cluster
are as close to each other as possible, and as far from
objects in other clusters as possible. This process can
be noted as minimizing within-cluster sum of squares
(objective function) according to

argmin
k∑
i=1

∑
xj∈Si

‖ xj − µi ‖2, (1)

where µi is the mean of points in Si (cluster centre).
In the demonstrated application, input parameter for
the calculation is number of phases constituting the
material that has to be a priori known. This number
of phases then corresponds to number of clusters to
be generated.
According to algorithmization used in this work,

image representation of the investigated material has
to be transformed into CIELAB colour space derived
from CIE XYZ tristimulus values. This space consists
of the luminosity layer L, the chromaticity-layer A,
indicating, where colour falls along the red-green axis,
and the chromaticity-layer B indicating, where the
colour falls along the blue-yellow axis (similarly to
the human vision). All of the colour information is
contained in the A and B layers and the difference
between two colours can be measured using the Eu-
clidean distance metric. Since the colour information
exists only in the a∗ b space, objects for clustering are
then pixels with A and B values.

3.3. RVE assessment
Utilization of statistical signal processing techniques
on determination of typical structural periods in cel-
lular metals was shown in [13], whereas application
on natural stones was presented in [6]. The method is

based on spectral analysis of a segmented binary im-
age, where large peaks in the power spectrum density
(PSD) function indicate typical structural periods of
the material’s microstructure and hence the RVE di-
mensions. All calculations are created on the domain
of spatial path variable ϕ (s) for the material’s phase
function leading to the following formulation of au-
tocorrelation function R∗xx (σ) and its corresponding
PSD function S∗xx (fs)

R∗xx (σ) = lim
S→∞

1
S

∫ Ls

0
φ (s)φ (s+ σ) ds, (2)

S∗xx (fs) =
∫ ∞
−∞

R∗xx (σ) e−2πiσdσ, (3)

where Ls is the one-dimensional path length and fs is
the structural frequency in m−1, which is reciprocal
of the searched structural period. Spectral estimation
itself has been performed using periodogram denoted
by the equation

S∗xx (fs) = 1
Ls
|ξ (fs, Ls)|2 , (4)

where ξ (fs, Ls) is the discrete Fourier transform
(DFT) of the phase function ϕ (s) given by

ξ (fs, Ls) =
∫ Ls

0
ϕ (s) e−2πifssds. (5)

4. Results
Hereinbefore characterized methods and tools have
been applied on SEM BSE and optical scanner images
in order to develop image processing procedures for
phase volume fraction identification in natural stone
materials as well as to show their applicability on
pre-processing of image data for spectral analysis.
Concerning the SEM grayscale data, edge detec-

tion, watershed transformation and pure gradient
magnitude algorithms proved unsuitable for analy-
sis of materials with such a kind of microstructure.
Edge detection approach is in this case significantly
computationally demanding and fails due to indis-
tinct grain boundaries and in some cases due to low
contrast between the neighbouring elements (grains).
Watershed transformation was applied in the form of
Meyer implementation. When the captured images
are seen as a topographic relief, it is evident that
watershed algorithm is not in this case applicable.
From topographic point of view, the images are in
major part constituted by plateaus rather than val-
leys. This implies that watershed may be used only to
identify pores, but still, due to complex and stochastic
connectivity between grains of individual phases, the
watershed algorithm fails with almost no watershed
lines created, substantial oversegmentation and loss
of information as a result of numerical errors. Conse-
quently, marker-controlled watershed was tested with
set of markers pre-defined by morphological operations.

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vol. 18/2018 Image and signal processing for assessment of heritage condition

However, even this effort led to the same results. As
a demonstration, pure gradient magnitude calculation
was performed resulting in boundaries between grains
generated together with noise and spurious elements,
where grains are constituted of material with mixtures.
Any algorithm applied on these results fails in over-
segmentation showing that on such a data additional
processing has to be carried out.
Hence, the following proposed image processing

method for quantification of individual phase’s volume
fractions was developed and implemented in Matlab
environment. Visual inspection of images captured in
SEM shows at high magnification that considerable
noise is present already during acquisition. For this
reason, the images are firstly manipulated to remove
noise. Among available tools, adaptive filtering using
Wiener filter [14] was applied to preserve edges and
possible high frequency parts of microstructure. Then,
local variances in intensities (inside grains) were cor-
rected by morphological reconstruction procedure com-
prising both opening- and closing-by-reconstruction
processing. In the next step, custom created thresh-
olding routine to extract information about the grey
phase (quartz particles) was applied. In this moment,
visual inspection is necessary to set upper and lower
intensity limit of the thresholding routine. These pa-
rameters cannot be determined automatically due to
two reasons: the images exhibit inhomogeneous in-
tensity represented by different intensity of selected
grains of the same material (that should be the same
thanks to BSE detector) caused by the morphological
reconstruction and it is necessary to extract informa-
tion about only one phase implying that two level
threshold is necessary. To segment into black phase
(pores filled with epoxy resin) simple global threshold
can be used again preferably with manually selected
threshold value after visual inspection. Resulting bi-
nary images are repeatedly dilated and eroded with
various structural elements to smooth the microstruc-
ture and to avoid isolated elements. Third phase
(feldspar) was identified by subtraction of quartz and
porous areas from the original image. In the last step,
volume fractions were evaluated by summations of
logical arrays of relevant phases. Segmentation to
quartz particles is shown in Figure 6.

The same effort was made to identify the constituent
phases in the colour image data. Images that were
cropped to include only homogeneous area without
oblique borders were in the first step converted from
sRGB space to the LAB space suitable for cluster
analysis. K-means clustering was performed with al-
gorithm pre-set to find 3 clusters corresponding to 3
phases present in the samples. Resulting 3 images
of clusters were then re-converted to RGB space and
transformed to binary ones for volume fractions’ com-
putation. Moreover when the clusters were visually
inspected it has been found out that such an anal-
ysis is an applicable approach for identification of
dominating structural directions in the material in-

Figure 6. Output from segmentation procedure show-
ing image suitable for structural analysis with high-
lighted detail of microstructure.

dicating overall planes of layer formations originated
during material’s sedimentation and compaction (see
Figure 7).

Figure 7. Microstructure subjected to cluster analy-
sis with depicted dominating planes (yellow lines).

Volume fractions determined in all the calculations
were compared to results from mercury porosimetry
measurements. It has been ascertained that volume
fractions from image computations are generally lower
due to inability of imaging devices to capture micro-
pores in the material that significantly contribute to
overall porosity. SEM BSE images proved to be prefer-
able for image processing and phases identification as
the image data are derived from atomic composition
of the phases rather than their appearance in optical
spectrum. As the developed image analysis routines
are superior to commonly used methods, the signal

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P. Koudelka, V. Koudelková, T. Doktor et al. Acta Polytechnica CTU Proceedings

processing approach to RVE dimensions calculation
then yields more reliable results. Furthermore, it en-
ables RVE dimensions to be calculated not only with
respect to porosity (or its distribution), but also ac-
cording to distribution of individual phases present in
the materials.

5. Conclusion
Microstructures of heterogeneous materials extracted
from historic buildings have been analysed to identify
volume fractions of individual phases present in the
materials, to calculate porosity and to demonstrate
utilization of signal processing routines for RVE di-
mensions calculation. Advanced image processing
methods have been applied in order to obtain high
quality data for consequent signal processing analysis.
Input images were captured using SEM equipped with
BSE detector producing grayscale images and using
high resolution flatbed scanner producing full colour
sRGB images. According to colour space, different
image segmentation algorithms have been developed.
Obtained results have been compared to experimen-
tally determined porosities assessed using mercury
porosimetry [3, 4]. It has been found out that optical
methods are suitable for volume fractions calculation
of constituting phases. In contrary, optical porosity
determination gives lower values as optical methods
are not able to distinguish micropores that are signifi-
cantly contributing to overall porosity. K-means clus-
tering method applied on colour data is a promising
approach for identification of dominating structural
directions. Employment of advanced image segmenta-
tion routines yields more reliable data in consequent
spectral analysis calculating RVE dimensions. This
maximizes probability that specimen with such di-
mensions extracted from historical building represents
the material at the level of constructional element
and leads minimal intervention to the built heritage
structures. However, given the stochastic nature of
their microstructure, large number of samples has
to be investigated for every type of stone to obtain
reasonable statistics of results.

Acknowledgements
This work was supported by the Czech Science Foundation
(project no. P105/12/G059) and by RVO: 68378297.

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60

http://dx.doi.org/10.1080/15583051003717788
http://dx.doi.org/10.4028/www.scientific.net/KEM.586.186
http://dx.doi.org/10.1109/TSMC.1979.4310076
http://dx.doi.org/10.1007/978-3-319-18720-4_60
http://dx.doi.org/10.1109/TPAMI.1986.4767851

	Acta Polytechnica CTU Proceedings 18:55–60, 2018
	1 Introduction
	2 Materials
	3 Methods
	3.1 Image acquisition
	3.2 Image processing
	3.2.1 Segmentation of grayscale image data
	3.2.2 Segmentation of colour image data

	3.3 RVE assessment

	4 Results
	5 Conclusion
	Acknowledgements
	References