1. Abstract - Cellular Neural Networks is implemented to
identify and extract abnormalities from an input image
in real time on the basis of pixel values that in the input
image are different from those in the standard image of
unaffected tissue. The background tissue is removed by
the algorithm which only considers values that come un-
der the abnormal range. Once extracted the image of the
abnormality is cleaned and missing pixels are given
neighboring pixel values.
Keywords: component; tumor detection; real time; cellular
neural networks; inpainting.
I. INTRODUCTION
Every field varies in its method of handling and analyzing
complex datasets generated on performing various experi-
ments. In case of medical data in the form of images generat-
ed by various diagnostic imaging machines such as MRI's,
CT, X-ray's to name a few need to be handled such that pro-
cessing them yields good results within stipulated time. In
various diagnoses, image processing tasks and computational
analysis procedures play an essential role ranging from a
simple change in contrast to applying complicated filters and
algorithms.
One of the most essential tasks in treatment strategies is
proper diagnosis of the test data. This purpose requires some
computational approaches that are fulfilled by various ma-
chine learning strategies like artificial neural networks, a
variant of which is the Cellular Neural Networks (CNN)
consisting of locally connected processors and the ability to
perform parallel processing. Using pixel values of the sup-
plied images, the area with the abnormality is differentiated
from the regions with normal tissue. On acquiring biomedi-
cal images, the algorithm would detect abnormalities in real
time and help speed up diagnosis of the patient.
This manuscript is divided into seven sub-sections, namely,
image processing, real time image processing, biomedical
image analysis, cellular neural networks, inpainting, case
study and discussion.
II. IMAGE PROCESSING
Image processing is any form of signal processing where the
input signal is that of an image and the output signal is the
modified image or parameters related to the input signal. It
involves image formation i.e. capture and digitization, image
visualization, which involves working on the perception of
the acquired image, analysis, that involves performing cer-
tain functions on the acquired image to get a desirable result
and then studying it to reach a deduction and finally image
management, which entails involves all aspects of the image
related to storage and transmission. Major aspects of image
processing involve image restoration, where an image of
degraded quality is worked on to bring it back to its original
quality; image enhancement, this involves making the image
clearer or more appealing to look at a basic example of
which is enhancing an image by increasing the contrast; and
image compression, which involves working on the image to
make it smaller in size with as little data loss or no data loss.
III. REAL TIME IMAGE PROCESSING
Real time image processing differs from other forms of im-
age processing as the performance of the system depends on
“correct and timely outputs”, i.e., the correct output should
be produced within the deadline. A real time system is one
that must satisfy explicit bounded response time constraints
to avoid failure [1].
Real time image processing, though a promising field, has
significant requirements when it comes to processing. Even a
simple situation to examine products of size 64 x 64 pixels
moving at rates of 10 to 20 per second along a conveyor
amounts to a requirement to process up to 100,000 pixels per
second—or typically four times this rate if space between
objects is taken into account, even a basic process such as
edge detection generally requires a neighbourhood of at least
9 pixels to be examined before an output pixel value can be
computed. Thus, the number of pixel memory accesses is
already 10 times that is given by the basic pixel processing
rate. Developing an algorithm which processes in real time
requires one to consider the operations that will be per-
formed on the image, and to employ a simple and efficient
method. The basic operations performed on an image are
noise removal, binary representation and edge detection. The
algorithm is required to be simple, the amount of data as
small as possible and a reduction in the number of opera-
tions.
IV. BIOMEDICAL IMAGE ANALYSIS
In the field of modern medicine, the most effective manner
of diagnosis and treatment planning is medical imaging. As
more and more direct digital imaging systems are used, there
is a newfound importance of image processing in the field of
medicine. In addition to the original digital diagnostic meth-
ods such as CT and MRI, the initially analogue systems such
as endoscopy or radiography are now equipped with digital
sensors that give rise to digital images. These images are
composed of pixels that can be efficiently processed. We
require biomedical image processing for image enhance-
ments, colour corrections in images, contour detection, im-
age smoothing and restoration, construction of 3D images
from 2D images and for the detection of abnormalities [2].
Implementing Cellular Neural Networks to Identify Abnormalities
in Brain MRI Images
1
Apaala Chatterjee, 2
Somnath Tagore
1,2
Department of Biotechnology and Bioinformatics,
Padmashree Dr. D.Y. Patil University, CBD Belapur, Navi Mumbai, 400614, India
e-mail: apaala@gmail.com1
, somnathtagore@yahoo.co.in2
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2. The main issue of concern when working with biomedical
images is that due to the complexity and nature of these imag-
es, creating a generalized algorithm could lead to discrepan-
cies between the results of the automatic algorithm and that of
the physician. Contributing factors to this problem are: Heter-
ogeneity in images where the images of human body and its
tissues not only changes from person to person, but also
changes within the person themselves from time to time and
the identification of normal tissue and abnormal tissue is
sometimes hard to do and also requires the developer to have
a biological background. The algorithm needs to be robust,
efficient and correct as due to processing errors, retaking the
image might be a problem not only when considering conven-
ience but also the exposure to harmful radiation that certain
imaging techniques require.
V. CELLULAR NEURAL NETWORK
Artificial Neural Networks (ANN) is composed of intercon-
necting artificial neurons for solving artificial intelligence
problems without creating a model of a real system. Neural
network algorithms abstract away the biological complexity
by focusing on the most important information. A neural net-
work can be defined as “a massively parallel distributed pro-
cessor that has a propensity for storing experiential knowledge
and making it available for use” [3]. A Cellular Neural net-
work (CNN) is a grid of chips that act as processors. The cell
grid can be a planar array with rectangular, triangular or hex-
agonal geometry, a 2-D or 3-D torus, a 3-D finite array, or a 3-
D sequence of 2-D arrays forming layers. Each cell is a Chip
that acts as a multiple input-single output processor.
Illustration 1: Cellular Neural Network
The circuit in the cell of the network comprises of linear and
non linear components, mainly the linear capacitor, linear
resistors and linear and non linear control sources and inde-
pendent sources [4]. A CNN is a system whose future state
depends upon its past state, meaning it is a non - markovian
system. The system consists of parametric functions that influ-
ence the output. We consider two parameters C, a Control and
F, a feedback function from the neighbouring cells. The input
is supplied to every cell of the CNN. Along with this input,
there is also an input from the neighbouring cell which acts as
a bias onto the state. All the bias from the various functionals
is summed up by a function. This function acts on the state
variable. The system could be mathematically represented by
the equation:
x(t+1) = x(t)+I(t)+u*C(t)+Fj (1)
and the output y is given as a function of x(t)
y(t) = f(x(t)) (2)
Here, t is the time variable; I is the bias; u is the external input
fed to the control C and j represents the intercell connection
weights. The cells of this network are capable of direct inter-
actions only with their neighbouring cells however, the infor-
mation is exchanged globally. Communications between re-
motely connected units are obtained passing through other
units. Every processor in the network is capable of processing
3 trillion equivalent digital per operations per second, which
brings it at par with the processing capabilities of a super
computers and it is precisely this nature of the CNN that al-
lows it to perform heavy image processing tasks such as
inpainting in real time.
VI. INPAINTING
Illustration 2: Damaged Image (A) and Inpainted Image (B)
Inpainting is the technique of modifying an image in an unde-
tectable form. The application of inpainting is numerous,
ranging from the restoration of damaged paintings, photo-
graphs as well as removal and replacement of selected objects
[6].
Inpainting consists of filling in the missing areas or modifying
the damaged ones in a manner non-detectable by an observer
not familiar with the original images. The goal of inpainting
algorithms varies, depending on the application, from making
the inpainted parts look consistent with the rest of the image,
to making them as close as possible to the original image. This
is followed by image restoration which is done automatically
by filling these regions in with new information coming from
the surrounding or cell in our case [7].
To perform inpainting, the objects are checked to identify
those requiring inpainting. A threshold value decides whether
a call to the inpainting algorithm is required. This threshold
value is a percentage of colour variance. The inpainting algo-
rithm works by receiving blocks of the image as an input. A
recursive call is made to the algorithm if the damage is greater
than the threshold value. If the colour variance of image
block is greater than threshold value the algorithm is called,
otherwise the image block is further divided into pixel blocks
within the CNN cell. The damaged pixels are painted by as-
signing them the values of their neighbours. Apart from being
useful in providing clearer images, inpainting also performs
spot healing and abnormality enhancement.
VII. CASE STUDY
Every cell in the cellular neural network is fed the image in-
formation. This information is processed according to the
rules laid out by the algorithm. The algorithm chosen should
be simplistic and efficient as that is a requirement when deal-
ing with data in real time. Every cell in the network is pro-
grammed to check the grey level pixel values of the image
data and to remove all values that are not relevant, i.e., that
▪ Implementing Cellular Neural Networks to Identify Abnormalities
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3. belong to normal tissue. This is done by comparing the input
to an pre-existing image of unaffected tissue. Theoretically,
the difference should leave us with the abnormalities, but due
to differences in biomedical images from person to person,
some noise remains in the form of normal tissue.
To extract the abnormalities from this “difference” image, we
use an algorithm that detects masses of pixels, i.e. looks for an
image within the image. It does so by considering the number
of neighbours the pixel in consideration has, and whether the
neighbours have neighbours. The mass has to be larger than a
threshold to be considered. The images acquired from bio-
medical imaging systems were subject to image processing so
as to extract the abnormalities. These abnormalities are viewa-
ble as anything different from a normal image.
The algorithm works by comparing the image in question
(Illustration 4) to a standard image data set (Illustration 2).
The differences in the two are stored (Illustration 5) and used
to improve the clarity and quality of the image containing the
tumour. Then an algorithm that considers pixel values is used
to extract the brightened abnormality (Illustration 6). A medi-
an filter is employed to remove noise and unwanted pixels.
Once the abnormalities are identified, the algorithm moves to
heal the image using inpainting technique. This inpainting
repair damaged pixels and assigns missing pixels the value of
neighbouring pixels thus healing the spot.
Illustration 3: A standard image to compare input with the abnormal
image
All the slices from the set are fed to the CNN, single image to
each cell and each cell compares its input to the standard nor-
mal tissue image.
Illustration 4: Image with abnormality
Each cell in the neural network works to find the difference
between the standard and the image provided as input. The
differences found, if greater than a threshold are saved as
“difference” images. These are then used to improve the visi-
bility of the tumour in the input.
Illustration 5: A difference image
The next step is to remove noise from the “difference” image
to avoid enhancement of unaffected tissue. The noise can be
removing using a median filter. The image is then enhanced
using the difference image, which leads to the brightening of
the abnormalities.
Illustration 6: The brightened abnormality
Next we remove unaffected tissue from the image. The idea
behind this algorithm is that the abnormality would have pixel
values different from the normal neighbouring tissue, there-
fore, removes all pixel values falling under the colour of nor-
mal tissue, leaving behind just the abnormalities and some
noise. To remove this noise we apply the median filter.
Illustration 7: Abnormality and noise
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4. One can observe in illustration 7, that there is a small round
shape in the extracted abnormality, that region represents and
area in the tumour that absorbs the radiation completely, re-
flecting none back, it can be repaired using inpainting where
missing pixels will be given a value on the darker side.
VIII. DISCUSSION
Applying CNN in diagnostics would allow for the processing
of images in real time, resulting in smaller amount of time
spent in diagnosis. On applying the simple algorithm to every
cell in the CNN, the object of interest identified and extracted.
This object could be any abnormality in tissue. Due to the
nature of CNN the application of the algorithm can be done
immediately and the output can be observed on a screen. The
simplicity of the algorithm allows fast processing and the
inpainting helps make the image clearer. Applying the algo-
rithm in the field of medical diagnosis allows for a faster diag-
nosis.
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in Brain MRI Images
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