Detection of Cervical Cancer Using Image
Processing Tools
1
Monika Jain, 2Shipra Roy, 3Naina Jain
Department of Electronics and Instrumentation Engineering,
Galgotias College Of Engineering & Technology, Greater Noida.
monikajain.bits@gmail.com,shipraroy1710@gmail.com,jainnaina05@gmail.com
Abstract—this paper access to the detection of cervical cancer
cells which is based on cell nuclei distribution and shape and size
analysis. PAP smear test is useful and easy method to detect
abnormalities in cervical cells. An automated detection system
of cervical cancer cells has been developed. Output of the test
shows that by using structure based segmentation and shape
analysis the system is able to differentiate between normal and
cancerous cells. The proposed approach is implemented in
MATLAB®, multi-paradigm numerical computing environment
and fourth-generation programming language that allows
implementation of algorithms. The MATLAB Image Processing
Toolbox was used to segment the digital images and calculate
various statistical data. By taking into account cell nuclei
distribution and considering the shape and size features
MATLAB® can be programmed to distinguish normal cervical
cell from abnormal ones.
Keywords—Pap Smear, Cervical Cancer, Image Processing and
MATLAB
INTRODUCTION
Cervical cancer is a disease that happens when cells
in the cervix area begin to grow out of control and invade
nearby tissues or spread throughout the body. Cancer or tumor
can be divided into two groups i.e. benign and malignant
where benign is described as tumor that does not invade and
destroy the tissue in which it originates or spread to distant
sites in the body (non-cancerous tumor) while malignant is
described as tumor that invades and destroys the tissue in
which it originates and can spread to other sites in the body
via blood-stream and lymphatic system (cancerous tumor).
The automated detection and segmentation of cell
nuclei in PAP smear images is the most interesting fields in
cytological image analysis as observed by Plissiti et. al.[1].
There is a high degree of cell overlap in such images, the
presence of more than one nucleus in a cell and the lack of
homogeneity in image intensity. These confront any method
to overcome the complexity of conventional cervical cell
images and to accomplish a correct segmentation. In addition,
the nucleus is a very important structure within the cell and it
presents significant changes when the cell is affected by a
disease and thus the exact definition of the nucleus boundary
is a crucial task. The recognition and quantification of these
changes in the nucleus morphology and density contribute in
I.
differentiating between normal and abnormal cells in PAP
smear images.
The segmentation of nuclei in cytological images has
been studied by several researchers [2-8]. In this paper we
present a method for analysis of PAP smear images based on
histogram and structuring element based segmentation and
shape and size analysis of the cell nuclei. The methodology
includes segmentation, calculation of cell nuclei distribution
and shape and size analysis of the cell nuclei.
The first order statistics of an image is its histogram
that gives information about the distribution of the gray level
value in its dynamic range [0, L-1], where L is the number of
gray levels. The histogram gives information of an image as a
whole and hence it can be considered as global statistics of the
image.
Morphological operations are based on shapes. In a
morphological operation, each pixel value in the output image
is based on a comparison of the corresponding pixel in the
input image with its neighbours. Dilation and Erosion are the
two most common morphological operations. Dilation adds
pixels to the object boundary of an image and erosion removes
pixels on the object boundary of an image. The number of
pixels added or removed from the object boundary of an
image depends in the size and shape of the structuring
element. An image, A and a structuring element B, dilation
and erosion are defined as A+B and A-B respectively.
Another morphological operation is filling operation which is
carried out on binary or grayscale images. For binary images,
filling operation changes connected background pixels to
foreground pixels. For grayscale images, filling operation
brings the intensity values of dark areas that are surrounded by
lighter areas up to the same intensity level as surrounding
pixels. This work is based on Marroquin et. al. [9] and
Hernandez et. al.[10]. The aim of this study was to provide
assistance to pathologists and to enhance the accuracy of
statistical data.
II.
METHODOLGY
The features to be extracted from the pap smear
images include
1. Size of the cell nuclei
2. Shape of the cell nuclei
3. Cell nuclei distribution
The methodology is arranged into three steps:
simplification and image enhancement, segmentation and
feature extraction.
1) Simplification and image enhancement
The digitized images are coloured in RGB mode. Matrix
corresponding to colour image is three dimensional and hence
it is difficult to process. The images in RGB mode have three
colour components and therefore is a tedious task to segment
using those colour component. The fact that gray level image
are easy to process so we converted the images to gray level
by using the rgb2gray unction in MATLAB. Then we enhance
the contrast of the images using histogram analysis using
imadjust function in MATLAB.
2) Segmentation
The cell nuclei are darker than the surrounding cytoplasm and
all the cell nuclei tends to have same gray level. We created
the histogram for each image to view the density distribution
of the different shades of gray. As the cell nuclei are darker,
we filtered out the light areas and created a uniform
background. Then using structuring element function, we
created binary images containing only the cell nuclei. We
considered only round shape for segmenting cell nuclei, but
all the nuclei are not round. So, the binary images are again
processed with dilation and filling function. As a result of
applying dilation and erosion, extra parts which were not part
of the nuclei were removed and the boundary of the cell nuclei
became prominent. Then we apply filling operation to create a
uniform intensity level inside the cell nuclei.
3) Feature Extraction
a) Cell nuclei distribution
The number of normal and abnormal cell in a cytological
image is good criteria for identifying abnormality. The size of
the cell nuclei is an important factor for determining whether
they are normal or not. We calculated the area of each
nucleus. The area was calculated in pixels. We then calculated
the cell nuclei distribution per images and presented the result
in tabular from with one column having the area and the other
having the number of nuclei with that area.
b) Shape and size analysis
Shape is another important feature for classification of
cervical cells in PAP smear images. Generally, the shape of
cell nuclei are round and elongation occurs when abnormality
occurs. The degree of change in shape is a good measure to
analyse the shape of the nuclei. We considered two factors.
First one is Compactness. It is a dimensionless shape feature
which measure compactness. It is defined by Sheng et. al. [11]
as
D = M2/P
where M is the perimeter and P is the area of the cell
nuclei. The calculation was done in pixel.
Second one is Eccentricity. The cell nuclei are round
in shape in normal condition. The roundness is not perfect and
the shape can be considered as ellipse. We calculate the
semimajor and semiminor axes’ lengths and the eccentricity
for each nucleus using the following formula
E= {(a2 – b2)/b2}1/2
where a and b are the semimajor and semiminor axes of the
ellipse respectively. The eccentricity value 0 corresponds to a
circle and with an increasing value the deviation from circle
becomes more significant as observed by Marroquin.
III.
RESULTS AND DISCUSSION
The pre-processing step excludes all the background
and leaves for further processing the parts of the image which
contain isolated cells or cell clusters results in the reduction of
the region of interest in the image. This method has been
applied in several PAP smear images defined by an expert
observer. The step for the detection of the cell nucleus
centroid has exposed that the resulted points of the image
indicate the area of the nuclei, as it is confirmed by the expert
observer.
Two images are shown here, one showing cancerous
cells in initial stage that is mild dysplasia and the other
showing normal cervical cell. The statistics were then
obtained and compared to confirm the differences.
The results are shown below.
1) Simplification and Image Enhancement
Figure 1-2 show the results after applying the techniques
mentioned above for simplification and enhancement of two
images.
Figure 1 - Original image showing cancerous cells in initial
stage that is mild dysplasia is converted to gray level and then
enhanced image.
Figure 2 - Original image showing normal cervical cell is
converted to gray level and then into enhance image.
2) Segmentation:
The images were then segmented for the region of interest,
which is the cell nuclei. They are shown below:
Figure 5 – Compactness and eccentricity histogram for
normal cell
Figure 3 - Segmented image showing only the cell Nuclei
3)
b) Compactness
Compactness is the dimensionless shape measure of the cell
nuclei. A normal nucleus has a well-formed and a compact
shape in normal condition. Cells with abnormality gradually
deform and the compactness decreases. We found out the
compactness of the cell nuclei and also normal cells have
higher value of compactness then that of the abnormal nuclei.
Feature Extraction
c) Eccentricity
Eccentricity is the measure of roundness of the cell nuclei.
Generally, the eccentricity can be said to be calculated from
the width and height of the cell nuclei. The normal nuclei have
a minimal proportion between the width and height and thus
have greater roundness. Uncontrolled growth of the nuclei
does not keep this uniform proportion and as result their
eccentricity deviated farther away from zero (0).
IV.
Figure 4 – compactness and eccentricity histogram of
abnormal cervical cell
CONCLUSION
An effective method to identify and classify cervical
cancer is becoming increasingly needed due to the fact that
early detection and a decision of correct therapy may save the
patient. Medical images have various limitations such as low
quality, presence of noise and human error in interpretation.
Digital image processing can help the pathologists to a great
extent.
The statistical data can be used to differentiate
normal or questionable sample while the pathologist looks at
the slide under a microscope which will be highly time saving.
Some ideas for future enhancement includes: to design an
interactive system where a pathologist can feed his own
grayscale threshold or to automate the process by computer
using histogram or fuzzy logic. Another enhancement can be
to establish cutoff values between normal and abnormal
values and to classify the abnormal values according to stage
of the cancer. The images processed are magnified and the
calculations are done in pixels. So, if a relation among
magnification, pixels and actual size is established, the
analysis will be more efficient. Previously CT images were
used to detect cervical cancer, but now MRI images can be
used for future works due to its high resolution. Artificial
neural network, contour methods and wavelets are also some
of the methods used to detect cervical cancer at an early stage
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