Annals of RSCB
Vol. XVII, Issue 2/2012
COMPUTERIZED MORPHOMETRIC ASSESSMENT OF THE
HUMAN RED BLOOD CELLS TREATED WITH CISPLATIN
Cristina Bischin1, Ş. Ţălu2, R. Silaghi-Dumitrescu1, M. Ţălu3,
S. Giovanzana4, Carmen Alina Lupaşcu5
1”BABES-BOLYAI” UNIVERSITY, DEPARTMENT OF CHEMISTRY AND CHEMICAL
ENGINEERING, CLUJ-NAPOCA, ROMANIA; 2TECHNICAL UNIVERSITY OF CLUJNAPOCA, FACULTY OF MECHANICS, DEPARTMENT OF AET, DISCIPLINE OF
DESCRIPTIVE GEOMETRY AND ENGINEERING GRAPHICS, CLUJ-NAPOCA,
ROMANIA; 3UNIVERSITY OF CRAIOVA, FACULTY OF MECHANICS,
DEPARTMENT OF APPLIED MECHANICS, CRAIOVA, ROMANIA; 4UNIVERSITY OF
MILANO-BICOCCA, MILANO, ITALY; 5UNIVERSITÀ DEGLI STUDI DI PALERMO,
DIPARTIMENTO DI MATEMATICA E INFORMATICA, PALERMO, ITALY
Summary
The objective of this study is to perform an evaluation of representative size and shape
parameters that characterise the human red blood cells either exposed to cisplatin or
exposed to control solutions containing no cisplatin. A set of fourteen digital images
corresponding for the human red blood cells were evaluated. Image processing and analysis
of digital images were performed with ImageJ and MRI Cell Image Analyzer (MRI-CIA)
softwares. We found for the human red blood cells either exposed to cisplatin or exposed to
control solutions containing no cisplatin, a set of representative size and shape parameters
for quantification. The central tendency and dispersion measure of the parameters were
expressed by the mean value and standard deviation. The computerized geometric
morphometric analysis of the human red blood cells is an efficient noninvasive prediction
tool that provides important insights into cell states.
Keywords: image analysis, human red blood cells, cisplatin, morphometry, shape,
shape descriptor
[email protected]
Introduction
Cell shape is a large-scale
expression of many subtle biological
processes, controlled by interactions
between the cytoskeleton, the membrane
and membrane-bound proteins and the
extracellular environment (Pincus and
Theriot, 2007).
Over the last few decades different
methods to analyze the structure of red
blood cells (or erythrocytes), and their
structure-function
relationships
were
performed (Canham, 1970; Evans and
Celle, 1975; Evans, 1983; Ruberto et al.,
2002; Dao et al, 2003; Suresh, 2006).
The red blood cell has a relatively
simple structure and possesses a discoid
form. It does not contain a nucleus. The red
The modern computerized geometric
morphometric
methods
have
been
established as efficient tools to quantify
differences in the cell shape or in particular
morphological structures and can provide a
better characterisation in describing the
complexity of anatomical structures (Grizzi
and Chiriva-Internati, 2005; Russ, 2007;
Rosioru et al. 2012).
Computational tools for the analysis
of cell shape allow to quantify the similarity
or difference between images of
homologous
anatomical
structures
containing multifactorial information, as
well as the relationship between cell shape
and experimental conditions (Pincus and
Theriot, 2007; Talu, 2012).
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Annals of RSCB
Vol. XVII, Issue 2/2012
cell membrane is considered uniform over
its surface. The normal biconcave shape of
the red blood cell corresponds to the shape
of minimum electrostatic energy. The
existence of a shape memory implies that
the elastic energy has a minimum when the
cell membrane is in static equilibrium
(Canham, 1970; Adams, 1973; Fischer,
2004; Gov and Safran, 2005).
These methods and measurements
prove sufficient for some studies, however
they are less well suited for quantifying the
red blood cells structure concerning to the
cell shape and size.
Cisplatin
(cis-DDP),
cisdiamminedichloroplatinum(II) is one of the
most
effective
and
widely
used
chemotherapeutic agents, into general
oncology practice. Cisplatin’s mode of
action involves the binding of the drug to
DNA and non-DNA targets and the
subsequent induction of cell death through
apoptosis, necrosis, or both (Fuertes et al.
2003).
The binding of this drug to red
bloods cells induce structural and functional
changes, most probably due to the
interaction with phospholipids located in
the inner monolayer of the red blood cell
membrane (Suwalsky et al., 2000; Mahmud
et al., 2008; Florea and Büsselberg, 2011).
To describe complex biological
structures many descriptions are used.
Shape representation and description is a
difficult task (Grizzi and Chiriva-Internati,
2005; Zhang and Lu, 2004).
Shape descriptors are one of the key
computational tools used for biological and
medical image processing applications.
In the literature, several shape
descriptors have been proposed for 2D and
3D objects.
Shape descriptors are mathematical
functions which are applied to an image and
produce numerical values which are
representative of a specific characteristic of
the image.
These numerical values can then be
processed in order to provide some
additional information about the image. The
shape descriptors can be classified into two
groups: contour-based shape descriptors
and region-based shape descriptors.
Contour based shape descriptors only
exploit shape boundary information,
ignoring the shape interior information.
Therefore, these descriptors cannot
represent shapes for which the complete
boundary information is not sufficient or
not available. On the other side, regionbased descriptors exploit both boundary and
internal pixels within patterns, and therefore
are applicable to generic shapes. Regionbased descriptors are more computationally
intensive and most methods need
normalization steps.
For generic purposes, both types of
shape descriptors are necessary (Zhang and
Lu,
2004;
Martinez-Ortiz,
2010;
Amanatiadis et al., 2011).
In
biological
and
medical
applications, contour-based descriptors are
more popular than region-based descriptors.
The shape descriptors depend on the
methodological
and
experimental
parameters involved as: diversity of
subjects, image acquisition, type of image,
image quality, its processing, analysis
methods, including the algorithm and
specific calculation used (Russ, 2007; Talu,
2012).
In our study we have investigated
the red blood cells either exposed to
cisplatin or exposed to control solutions
containing no cisplatin, using computerized
geometric morphometrics.
Material and methods
The human blood was extracted on
citrate from a health donor without any
known chronic medical conditions. After
incubation with cisplatin (400 µM) at 37 ºC
for 20 hours, the red blood cells were fixed
on the glass slides according to the protocol
May-Grünwald-Giemsa,
described
by
Alteras (Alteras and Cajal, 1994). Cisplatin
powder (obtained from Sigma-Aldrich,
Germany) was dissolved in saline buffer
(1% NaCl). The control solution also
contains saline buffer (1% NaCl).
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Annals of RSCB
Vol. XVII, Issue 2/2012
The SEM images were obtained by
the use of a scanning electron microscope
(FEI Quanta 3D FEG dual beam) in high
vacuum work mode using EDT (Everhart
Thornley detector).
Figure 1 shows the morphology of
the red blood cells either exposed to control
solutions containing no cisplatin or exposed
to cisplatin.
(http://www.mri.cnrs.fr/index.php?m=38)
(Bäcker and Travo, 2006).
Let us consider a set of fourteen
digital images corresponding for the human
red blood cells. After correction of the
digital images, by contrast adjustment and
spatial filtering, shape outlines of cell
membrane were extracted from binary
images with a classical contour-extraction
method.
With area selections, the following
geometrical parameters and numerical
descriptors were determined: Area, Center
of Mass, Perimeter, Bounding Rectangle,
Fitted Ellipse, Feret’s Diameter, Skewness
and Kurtosis.
Details of the representative size
and shape parameters, as well as the
intensity statistics, used to obtain
information on the red blood cells
characteristics complexity are given in the
Appendix.
(a)
Statistical analysis
After image processing and analysis
with ImageJ and MRI Cell Image Analyzer,
all the raw data were statistical analyzed.
Descriptive
statistics
were
calculated for the controls and cisplatintreated cells in each group and for the two
different groups (controls and cisplatintreated cells). It was found that the average
values of the size and shape parameters
followed a normal distribution.
Statistical comparison between
groups was made by one-way analysis of
variance (ANOVA) (p < 0.01 statistical
significance).
(b)
Fig. 1. SEM images of red blood cells:
(a) control and (b) cisplatin-treated.
Results and discussions
We evaluated the representative size
and shape parameters by the three criteria:
fidelity, capture of biologically relevant
details and human interpretability.
The obtained average results were
expressed as (average ± standard deviation).
A summary of the obtained results
are presented in the tables given below.
Geometric morphometric analysis
The
computerized
geometric
analysis of binary images was made using
the Image J software (Wayne Rasband,
National Institutes of Health, in Bethesda,
Maryland, USA) (http://imagej.nih.gov/ij)
together with MRI Cell Image Analyzer
(MRI-CIA) software, developed by the
Montpellier RIO Imaging facility (CNRS)
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Annals of RSCB
Vol. XVII, Issue 2/2012
Table 1. Results of the evaluation for the
representative size and shape parameters of the
red blood cells (average ± standard deviation).
Some remarks can be obtained
concerning the results from Tables 1 and 2:
- the red blood cells are morphologically
changed from control group when are
treated with cisplatin;
- the average values for circularity,
roundness and solidity parameters of the
control group are lower than that of the
treated group.
- the average values for aspect ratio
parameter of the control group are higher
than that of the treated group.
- the geometrical parameters are interrelated
and provide equivalent information about
the size and shape geometry.
- the geometric morphometric analyses
were in agreement with the histological
observations.
These analyses were performed
using 2D representations of 3D biological
shapes.
Red blood cell shape and values
Parameters
Area [µm2]
Perimeter [µm]
Circularity [-]
Aspect
ratio
(AR) [-]
Roundness [-]
Solidity [-]
Major [µm]
Minor [µm]
Angle [°]
Feret [µm]
FeretAngle [°]
MinFeret [µm]
Width [µm]
Height [µm]
Control cells
10.846± 1.150
16.292 ± 1.603
0.518±0.056
1.241±0.073
Treated cells
11.554 ± 1.477
15.843 ± 1.492
0.585±0.087
1.157±0.104
0.808±0.045
0.822±0.037
4.129± 0.164
3.339± 0.247
76.162± 61.628
4.726± 0.239
102.832±54.658
3.747±0.304
4.339± 0.206
4.221± 0.623
0.870±0.075
0.846±0.040
4.109± 0.236
3.573± 0.326
83.106± 57.667
4.759± 0.321
71.912±52.344
3.860±0.296
4.380± 0.372
4.269± 0.501
In intensity statistics, each pixel has
a brightness value that ranges between 0
(black) and 255 (white). Higher values
usually mean lighter pixels and lower
values mean darker pixels.
Conclusions
Computerized
geometric
morphometric method for shape analysis is
a useful tool for investigation of the red
blood cells in a more realistic and
integrative way and allows a numeric
evidence of cell shape complexity. The
relationship between the shape of the red
blood cells and cisplatin is complex. Our
results suggest that the red blood cell shape
complexity can be performed using by a set
of size and shape parameters in an accurate
and statistically powerful way. These size
and shape parameters allows a more
sensitive characterization of very subtle
variations in red blood cells form that could
remain undetected when using traditional
particle sizing techniques.
Table 2. Results of the intensity statistics
evaluation for red blood cells corresponding of
the digital images from Table 1 (average ±
standard deviation).
Red blood cell shape and values
Parameters
Mean Gray
Value
Standard
Dev. 1
Modal Gray
Value
Min
Gray
Level
Max Gray
Level
XM
YM
Integrated
Density
Median
Skewness
Kurtosis
Control cells
80.598 ± 3.614
Treated cells
78.742 ± 5.625
16.293 ± 1.836
15.311 ± 2.516
86.833 ± 6.494
84.417 ± 6.748
30.333 ± 3.141
34.750 ± 5.643
140.833 ±
4.167
11.963 ± 5.027
13.737 ± 5.359
874.113±
99.322
82.667±3.615
-0.399±0.126
-0.310±0.244
140.583 ±
12.638
13.149 ± 4.237
13.411 ±4.587
906.591±
103.740
80.417±5.744
-0.299±0.350
-0.266±0.498
Acknowledgements
This research has been financially
supported by the Romanian Ministry for
Education and Research (grants ID
565/2007 and PCCE 140/2008). I would
like to thank Prof. Corina Rosioru (“BabesBolyai” University, Department of Biology,
Cluj-Napoca) for helpful discussions. I
would also like to thank Ph.D. Adriana
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Annals of RSCB
Vol. XVII, Issue 2/2012
Vulpoi and Prof. Simion Simon (“BabesBolyai” University, Department of Physics,
Cluj-Napoca) for their assistance in SEM
technique. A Ph.D. scholarship from
Contract POSDRU/88/ 1.5/S/60185 –
“Innovative doctoral studies in a knowledge
based society” is gratefully acknowledged
by C.B.
known as maximum caliper. The length of
the object’s projection in the X (FeretX)
and Y (FeretY) direction is also displayed.
10. FeretAngle: The angle of the Feret
diameter with the x-axis of the image (0°180°).
11. MinFeret: This is a measure of the
particle's width. It is called the minimum
caliper diameter as well. It is defined as the
shortes distance between two parallel
planes touching the particle on opposite
sites, for any orientation of the particle.
12. AR: The aspect ratio of the particle.
This is the length of the major axis divided
by the length of the minor axis of the fitted
ellipse.
13. Round: The roundness of the particle,
defined as: 4·area/[π·(major axis)²]. The
roundness is 1 for a circle and approaches 0
for very alongated objects.
14. Solidity: The area of the particle
divided by the area of the convex hull of the
particle. The convex hull is a boundary
enclosing the foreground pixels of an image
using straight line segments to each
outermost point.
The intensity statistics parameters
are defined as following:
1. Mean Gray Value: The average gray
value within the ROI. This is the sum of the
gray values of all the pixels in the selection
divided by the number of pixels.
2. Standard Dev. 1: The standard deviation
of the mean gray value within the ROI.
3. Modal Gray Value: Most frequently
occurring gray value within the ROI. This
corresponds to the highest peak in the
histogram of the ROI.
4. Min & Max Gray Level: Minimum and
maximum gray values within the ROI.
5. XM: The x-coordinate of the center of
mass, that is the brightness weighted
average of the x-coordinates of the pixels in
the ROI. The coordinates (XM and YM) are
the first order spatial moments.
6. YM: The y-coordinate of the center of
mass, that is the brightness weighted
average of the y-coordinates of the pixels in
the ROI. The coordinates (XM and YM) are
the first order spatial moments.
Appendix
In our study, MRI Cell Image
Analyzer (MRI-CIA) software, developed
by the Montpellier RIO Imaging facility
(CNRS)(http://www.mri.cnrs.fr/index.php?
m=38) was used to determine the size and
shape parameters of the given shape data.
With area selections, the following
geometrical parameters and numerical
descriptors were determined: Area, Center
of Mass, Perimeter, Bounding Rectangle,
Shape Descriptors, Fitted Ellipse, Feret’s
Diameter, Skewness and Kurtosis.
The size and shape parameters are
defined as following:
1. Area: The surface of the region of
interest (ROI), measured in [µm2].
2. Perimeter: The length of the outside
boundary of the ROI, measured in [µm].
3. Width: The width of the bounding box of
the ROI (the smallest rectangle enclosing
the selection).
4. Height: The height of the bounding box
of the ROI.
5. Major: The length of the major axis of
the best fitting ellipse. The ellipse has the
same area, orientation and centroid as the
original selection.
6. Minor: The length of the minor axis of
the best fitting ellipse.
7. Angle: The angle of the major axis of the
best fitting ellipse against the x-axis of the
image.
8. Circularity: the ratio is given by 4πA/p2,
where A is the area of the shape and p is the
perimeter. A value of 1 indicates a perfect
circle. As the value approaches 0, it
indicates an increasingly elongated
polygon.
9. Feret: The longest distance between two
points on the boundary of the ROI, also
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Fuertes M.A., Alonso C., Perez J.M.,
Biochemical
modulation
of
cisplatin
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Martinez-Ortiz C., PhD. Thesis: 2D and 3D
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***http://imagej.nih.gov/ij
***http://www.mri.cnrs.fr/index.php?m=38
7. Integrated Density: The integrated
density is the sum of the gray-values of all
pixels within the ROI.
8. Median: The median gray value of the
pixels within the ROI.
9. Skewness: A measure of the asymmetry
of the distribution of the gray values around
the mean within the ROI. The third order
moment about the mean.
10. Kurtosis: A measure of the
"peakedness" of the distribution of the gray
values around the mean within the ROI.
The fourth order moment about the mean.
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