J. Biomedical Science and Engineering, 2013, 6, 1029-1033 JBiSE
http://dx.doi.org/10.4236/jbise.2013.611128 Published Online November 2013 (http://www.scirp.org/journal/jbise/)
Carcinoma cell identification via optical microscop y and
shape feature analysis
Ahmad Chaddad, Camel Tanougast, Andrew Golato, Abbas Dandache
Laboratory of Design, Optimization and Modeling (LCOMS), University of Lorraine, Metz, France
Email: ahmad8chaddad@gmail.com, camel.tanougast@univ-lorraine.fr
Received 27 September 2013; revised 21 October 2013; accepted 29 October 2013
Copyright © 2013 Ahmad Chaddad et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Optical microscopy is commonly used for cancer cell
detection. Focusing on carcinoma cell identification
via optical microscopy, a proof-of-concept study was
performed at Laboratory of Design, Optimization and
Modeling (LCOMS) to determine the grade of cancer
cells. This paper focuses on three types of abnormal
cells; namely, Benign Hyperplasia (BH), Intraepithe-
lial Neoplasia (IN), which is a precursor state for can-
cer, and Carcinoma (Ca), which corresponds to ab-
normal tissue proliferation cancer. These types of
cells were used to assess the efficiency of using shape
features to identify carcinoma cells. A comparative
study based on performance indicator concludes that
three features, Area, Xor-Co n v ex, and Solidity, were
found to be effective in identifying the Carcinoma
grade of cancer cells.
Keywords: Cancer; Carcinoma; Detection; Shape
Cancer, unfortunately, is one of the most common dis-
eases affecting people. It is characterized by abnormal
and uncontrolled cell proliferation. Early cancer detec-
tion is of paramount interest for timely and effective di-
agnosis and treatment [1]. Colon cancer cells are well
studied and presented since many macroscopic and mi-
croscopic techniques can be used to detect colon cancer.
The current repertoire of tools, including medical imag-
ing techniques [2], such as MRI, CT scan, PET, SPECT
and Microscopy, are considered effective at detecting [3],
localizing and estimating the volume of certain cancers.
Nevertheless, these tools are not capable of detecting ear-
ly cancer on the cellular level. However, microscopic ob-
servations of biopsies of the colon display an ability to
detect irregular cells or non-natural contrasts. With this,
many recent works focus on developing automatic read-
ing procedures for such biopsies [4-6]. Automatic read-
ing fosters faster and precise readings of microscopic bi-
opsies and segmenting cells in order to classify them as
cancerous or non-cancerous cells [7,8]. Automatic read-
ing of microscopic images includes several consecutive
steps: The system must segment the image by detecting
and extracting cells from their surrounding medium us-
ing morphological image processing. This step requires
the careful selection of the approp riate segmentation tec h-
nique in order to process high resolution gray scale and
multispectral microscopic images. Following the detec-
tion of cells within an image, the system must extract
some characteristic parameters in order to distinguish the
three abnormal cell types [9,10]. To better our learning
and classification, we employed the nearest neighbor cl as -
sifier [11,12]. We used a texture of multispectral images
of cancer cells taken from the Anapat service of the CHU
hospital, Nancy-Brabois, France. Analysis of the textures
and structures present in the multispectral bio-images di-
agnoses different grades of cancer malignancy. Our pro-
ject operates on the interface between optical microscopy
and the control center “computer”, which takes the data
from optical microscopy including the Charge Coupled
Device (CCD) Camera and the Liquid Crystal Tunable
Filter (LCTF) and uses it to get a multispectral image.
Figure 1 presents the optical microscopy acquisition sys-
tem for cell detection and carcinoma cell identification.
The goal of this paper presents proper carcinoma cell
identification and clear distingu ishing of these cells from
other grades of cancer cells. The three types of cancer
cells considered in this study are shown in Figure 2.
This paper is organized as follows: Section 2 briefly
reviews the Snake method based on external and internal
energy, shape features, nearest neighbors classifier and
the cross validation. Experimental results and discussions
based on a comparative study are presented in Section 3.
A. Chaddad et al. / J. Biomedical Science and Engineering 6 (2013) 1029-1033
Figure 1. Block diagram of carcinoma cancer cells detection using optical microscopy.
(a) (b) (c)
Figure 2. Grade of cancer cells, (a) Benign Hyperplasia; (b) Intraepithelial Neoplasia and (c) Carcinoma.
Finally, conclusion is provided in Section 4.
There are many applications utilizing optical microscopy
including colon and prostate cancer cell detection. These
applications require high quality data for accurate cancer
cell interpretation and analysis. This paper uses images
provided by optical microscopy to identify pre-diagnoses
carcinoma cells using shape features in order to distin-
guish between the different grades of cancer cells. Before
discussing our shape features, we must employ an adap-
tive dynamic segmentation method to our image. We used
the “Snake” method, which consists of an active contour;
that is to say we utilized a dynamic curve that, through
an iterative process, processes toward and thereby de-
tects the contour of an object observed in a certain image.
The model we developed is able to detect the contour of
an image without calculating its gradient [13,14]. Each
curve developed carries with it an energy function de-
scribed by :
nakeinternal external
where Fsnake is the contour detection, Finternal is an energy
that depends on the physical properties of the contour
and Fexternal is an energy that depends on th e properties of
the image [15]. The correspond ing algorithm tries to find
a combination between different image points to mini-
mize the energy function Fsnake and thus detect the con-
tour. The energy Fsnake can be re-expressed in the follow-
ing equation:
internal interior C
external exterior C
Uxy c
Uxy c
where U0(x, y) is the pixel on the image having a 2D x
and y position , c1 and c2 are the average intens ities in the
regions respectively inside and outside of the real con-
tour C0. We are able to further enhance the real contour
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A. Chaddad et al. / J. Biomedical Science and Engineering 6 (2013) 1029-1033 1031
detection via a regularization term that leads to the final
equation of Snake Fsnake:
internal interior C
external exterior C
Uxy c
Uxy c
0 is a constant parameter and C is the contour.
Consequently, the detection of contours is minimized to
Fsnake, and is expressed according to
(,f,in ccC) (4)
We scale all images such that intensity values range
from “0” representing the darkest gray level and “255”
representing the brightest gray level in the image. The
enhancement step is automatically applied via LCTF
which allows adjustment to the lightness/darkness level
of gray scale. The goal of the segmentation step is ulti-
mately to provide accurate determination of cancer cells
and in particular carcinoma which is represented by com-
plex shape.
2.1. Shape Features Extracted from Segmented
Nine shape features were used to classify segmented
cells based on texture analysis as used in previous work
[7]. These parameters include the area and perimeter of
the cell, Xor cell-circle, Xor cell-convex, Xor cell-rec-
tangle, standard deviation of the positions of the contour
points, deviation sum, eccentricity, and solidity of the de-
tected cell. Xor cell-circle operator is applied between
the cell and a circle having the same area and center of
mass as the cell. Figure 3 shows the three steps required
to determine this parameter. The area of the white region
in Figure 3(c) illustrates the numerical value of the Xor
cell-circle parameter. The same methodology in Xor cell-
circle is used to estimate the Xor cell-convex (see Figure
4). The Xor operator here is between the cell and a con-
vex which covers the cell.
(a) (b) (c)
Figure 3. Shape features computed, (a) BH cell type; (b) circle with the same area and center of mass as the cell; (c) Xor
(a) (b) (c)
Figure 4. Shape features computed, (a) BH cell type; (b) convex covering the cell; (c) Xor cell-co nvex.
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A. Chaddad et al. / J. Biomedical Science and Engineering 6 (2013) 1029-1033
Similarly, the Xor cell-rectangle is an operator be-
tween the shape cell and a rectangle covering it. The
standard deviation of the positions of the contour points
Standard DeviationN
where N is the number of the contour, Xi is the distance
between a contour point i and the center of the cell and
is the mean value of Xi.
The distance from each point of the contour to the
mean contour is summed to determine the deviation su m
as follows:
Deviation SumN
To measure the eccentricity, we interpose onto the cell
an ellipse to cover it. The eccentricity p arameter is given
by the following equation:
Eccentricity distance,
fL (7)
where f1 and f2 are the two foci of the ellip se and L is its
major axis length .
Finally, the solidity is a scalar specifying the propor-
tion of the pixels in the convex hull that are also in the
region of the cell. It’s computed as follo ws:
 
SolidityArea cellArea convex (8)
Shape features provide promising results in distinguish-
ing and classifying the grade of cancer cell. The nearest
neighbor techniqu e adds robustness to this process.
2.2. Cross Validation and Performance Indicator
Nearest neighbours is one of the simplest methods in
identification and classification, wherein an object is
classified based on the “distance” of its features from
those of its neighbours, with the object being assigned to
the class most common among its k distance-nearest nei-
ghbors [11,12]. Due to th e limited data available, we em-
ployed cross-validation (CV), which uses a part of the
data as training samples ((n-1) BH, (n-1) IN, and (n-1)
Ca) to train the algorithm, and th e remaining part (1 BH,
1 IN and 1 Ca) for estimating the classification accuracy
of the algorithm [16]. For the multispectral images of
cancer cells under examination, we have 30 images from
each type of cancer cell. Performance indicators are used
to evaluate the accuracy of distinguishing between the
three grades of cancer cells. We propose one indicator:
classification accurate. This indicator is shown in the fol-
lowing equation:
Accuracy total number of sample cases
number ofsamples correctly classified
Results of the performance indicators reflect the value
of this study where the shape features will be more pro-
mising to detect carcinoma cancer cells.
Experimental results are carried out and obtained using a
32 bit PC platform running at 2.4 GHz (Core 2 processor)
based on the Matlab environment tool [17] and the opti-
cal microscopy system. This platform validates the cor-
rect operations of the bio-images analysis and structures
approaches starting from the capture of the multispectral
bio-images. Table 1 shows the nine parameter values
computed from three grades of cancer cells. These values
vary the of shape features depending on the cell type.
From these parameters, we choose three; namely, we
identify Area, Xor cell-convex and Solidity, as the domi-
nant features for carcinoma cancer cell detection. A
comparative study based on nine (9F) and three features
(3F) is showed in Table 2. Using three shape features, all
the cells were classified without error represented, which
is, of course, represented by 100% accuracy value. Mean-
while, using the nine shape features decreases the accu-
racy value by 2% - 3% to 97% - 98%. Texture features
can be extracted in several methods, in cluding: statistical,
structural, model-based, and transform information. The
automatic recognition and classification of cancer cells
was done to enhance work efficiency while also id entify-
ing inter-relationships among biological features. For in-
stance, three Haralicks features (Correlation, Entropy
and Contrast) were found to be effective to discriminate
between the three types of abnormal cells in textured
images [7]. For accurate extraction from very high reso-
lution images, object shape can to be taken into account
without the drawback of the prohibitive computation t i me .
Table 1. Simulation results of shape features.
Features BH IN Ca
Area 0.4 48,187 166,866
Perimeter 1610.36 1740.74 5650.10
Xor Cell-Circle 17036.37 25507.37 58254.87
Xor Cell-Convex 7017.75 38.321 58605.12
Xor Cell-Rectangle 44120.75 93987.37 87465.62
Standard Deviation 17.90 52.58 63.47
Deviation Sum 19297.46 65181.91 263492.51
Eccentricity 0.514 0.750 0.427
Solidity 0.947 0.557 0.740
Table 2. Simulation results of performance indicator.
% Accuracy
Cancer cells type 9F 3F
BH 97 100
IN 91 100
Ca 98 100
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A. Chaddad et al. / J. Biomedical Science and Engineering 6 (2013) 1029-1033 1033
Thereby, shape features can be used to make the classi-
fication of cancerous cells b ased on morphological image
processing, i.e. the shape features in this paper. Never-
theless, the cancer continuum is still a complex subject in
which most laboratory research focuses on distinguishing
between the cancer stages [18]. Unfortunately, the transi-
tion from one stage to the next is still unclear.
This paper proposed a method of carcinoma cancer cell
detection using shape features and the nearest neighbour
classifier technique. Shape features showed promising
results for carcinoma cells detection. The three dominant
features, Area, Xor Cell-Convex and Solidity, were found
to be effective in detecting the carcinoma cells from the
other grades of cancer cells, BH and IN. Performance in-
dicators clearly describe our model as having higher ac-
curacy and thereby lower false alarm. This proposed mo-
del can be adapted to several applications in the assess-
ment of both cancer and normal cells as shape features is
one of the best methods in discriminating between cancer
and normal cells.
Authors would like to acknowledge the service Anapat of the CHU
hospital of the Nancy-Brabois and the Architecture of Embedded Sys-
tems and Smart Sensors (ASEC) team.
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