International Journal of Engineering Trends and Technology (IJETT) – Volume 7 Number 2- Jan 2014
Segmentation of Gray Matter, White Matter and
Brain Tumour from Brain MR Images
Ms. V. Kavitha#1, Mr. S. Rajesh Kumar Reddy*2
#1
PG student, #2 Assistant Professor,Dept. of Biomedical Engineering, Anna University,
Udaya School of Engineering, Ammandivilai, Kanyakumari, Tamil Nadu, India.
Abstract— Accurate segmentation of magnetic resonance (MR)
images of the brain is of interest in the study of many brain
disorders. In multiple sclerosis, for instance, quantification of
white matter lesions is necessary for drug treatment assessment,
while in schizophrenia and epilepsy, volumetry of gray matter,
white matter, and cerebrospinal fluid is important to
characterize morphological differences between subjects. Since
such studies typically involve vast amounts of data, manual
segmentation is too time consuming. Moreover, such manual
segmentations show large inter and intra observer variability.
Hence, there is a need for automated segmentation tools. A major
problem for automated MR image segmentation is the
corruption with a smoothly varying intensity inhomogeneity or
bias field. Although not always visible for a human observer,
such a bias can cause serious misclassifications when intensity
based segmentation techniques are used. The Pulse Couple
Neural Network (PCNN) was developed by Eckhorn to model the
observed synchronization of neural assemblies in the visual
cortex of small mammals such as a cat. In this work, PCNN
based automatic segmentation algorithm was developed to
segment Brain Magnetic Resonance Imaging (MRI). This
algorithm is compared with Fuzzy C Means (FCM) segmentation
algorithm and Fuzzy Local Gaussian Mixture Model (FLGMM)
algorithms. Results show that the proposed PCNN algorithm can
segment the brain MR image accurately than FCM and FLGMM
algorithm.
Keywords— Magnetic Resonance Image (MRI), Gray Matter
(GM), White Matter (WM), Fuzzy C Means (FCMs), Gaussian
Mixture Model (GMM), Fuzzy Local Gaussian Mixture Model
(FLGMM), Pulse Coupled Neural Network.
I. INTRODUCTION
Segmentation of major brain tissues, including
gray matter (GM), white matter (WM), and
cerebrospinal fluid, from magnetic resonance (MR)
images plays an important role in both clinical
practice and neuroscience research. However, due
to the nonuniform magnetic field or susceptibility
effects, brain MR images may contain a smoothly
varying bias field, which is also referred to as the
intensity inhomogeneity or intensity nonuniformity.
Therefore, bias field correction and segmentation
should be interleaved in an iterative process so that
they can benefit from each other and yield better
results. Many brain MR image segmentation
ISSN: 2231-5381
approaches with bias field correction are available
so far. Among them, those based on the
expectation-maximization (EM) algorithm and
fuzzy C-mean (FCM) clustering are the most
popular ones [24].
The conventional GMM implies the stochastic
assumption that throughout the image, intensities in
the same region are sampled independently from an
identical Gaussian distribution. This assumption,
however, is invalid for brain MR images due to the
existence of the bias field. Proposed fuzzy local
GMM (FLGMM) algorithm [24], assumes that the
local image data within the neighborhood of each
pixel follow the GMM, in which the mean of each
Gaussian component is approximated as a tissue
dependent constant multiplied by the bias field
estimated at this pixel for brain MR image
segmentation. The objective function of this
algorithm is defined as the integration of the
weighted GMM energy functions over the entire
image. In the objective function, a truncated
Gaussian kernel function is used to impose the
spatial constraint, and fuzzy memberships are
employed to balance the contribution of each GMM
to the segmentation process. Pulse-coupled
networks or pulse-coupled neural networks (PCNNs)
are neural models proposed by modeling a cat’s
visual cortex and developed for high-performance
biomimetic image processing [8]. A PCNN is a
two-dimensional neural network. Each neuron in
the network corresponds to one pixel in an input
image, receiving its corresponding pixel’s color
information (e.g. intensity) as an external stimulus.
Each neuron also connects with its neighboring
neurons, receiving local stimuli from them. The
external and local stimuli are combined in an
internal activation system, which accumulates the
stimuli until it exceeds a dynamic threshold,
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International Journal of Engineering Trends and Technology (IJETT) – Volume 7 Number 2- Jan 2014
resulting in a pulse output. Through iterative
computation, PCNN neurons produce temporal
series of pulse outputs. The temporal series of pulse
outputs contain information of input images and can
be utilized for various image processing
applications, such as image segmentation and
feature generation. Compared with conventional
image processing means, PCNNs have several
significant merits, including robustness against
noise, independence of geometric variations in
input patterns, capability of bridging minor
intensity variations in input patterns, etc.
Filtering will be carried out after this step by using
MATLAB coding for median filter which can filter
the noises from the images with preserving the
edges. Finally segmentation of brain MR images
will be carried out using FCM, FLGMM and PCNN
algorithms.
II. PROPOSED SYSTEM
A. System overview
The proposed work is divided into two main
stages. In the first stage preprocessing is done. In
the second stage segmentation of Brain MR Images
is done by using FCM, FLGMM and PCNN
algorithms. Overall architecture of this work is
shown in figure 1.
Load brain MR
Images
Preprocessing
Segmenting the brain
MR Images using
FCM
Segmenting the brain
MR Images using
PCNN
Segmenting the brain
MR Images using
FLGMM
Fig. 2 Work Flow Diagram
Fig. 1 Overall Architecture
B. Work Flow Diagram
III. MODULES AND MODULE DESCRIPTIONS
The work flow of proposed work is shown in
Following modules are presents in this work.
figure 2. The brain MR images were collected from
 Image size estimation
Johnson MRI, Erode for this work. Collected image
 Back ground normalization
was loaded for further processing using simple
 Filtering
MATLAB get file coding. If the image contains
 Segmentation of brain MR images
unwanted texts such as patient name, age, image
size etc.., then, it will be removed by using the
simple MATLAB coding given in the demos for
removing unwanted objects from the image.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 7 Number 2- Jan 2014
A. Image size estimation
During segmentation process, only the brain image
should be segmented. Thus by using back ground
normalization process, the unwanted message
present in the MRI image is eliminated.
C. Filtering
Image filtering is used to Remove noise,
Sharpen contrast, Highlight contours and Detect
edges [17]. Image filters can be classified as linear
or nonlinear.
Fig. 3 First module
This module is for estimating the image size
of the MR image. This is useful for segmenting the
correct level of the image. This is a checking
process of whether the image size is perfect for the
segmentation process. If not this will be corrected
to the valuable image size. Since, there are many
images in the datasets which are not in the constant
size. Thus, the non-similar image sizes are
corrected.
Fig. 5 Third module
Linear filters are also know as convolution
filters as they can be represented using a matrix
B. Back ground normalization
multiplication.
Thresholding
and
image
equalization are examples of nonlinear operations,
Since, there are so many unwanted data’s as is the median filter. Median filtering is a
are present in the MRI image. The unwanted nonlinear method used to remove noise from
information like,
images. It is widely used as it is very effective at
1. Patient’s name,
removing noise while preserving edges. It is
2. Size of the image,
particularly effective at removing ‘salt and pepper’
3. Date, etc
type noise. The median filter works by moving
through the image pixel by pixel, replacing each
value with the median value of neighbouring pixels.
The pattern of neighbours is called the "window",
which slides, pixel by pixel over the entire image.
The median is calculated by first sorting all the
pixel values from the window into numerical order,
and then replacing the pixel being considered with
the middle (median) pixel value.
D. Segmentation of brain MR images
Fig. 4 Second module
These are must be eliminate or to prevent
the confusion made during image segmentation.
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In this module “Fuzzy C Means and Fuzzy Local
Gaussian Mixture model algorithms” are used for
Brain image segmentation. Image segmentation is
typically used to locate objects and boundaries in
images.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 7 Number 2- Jan 2014
The objective function of Fuzzy Local
Gaussian Mixture Model algorithm is defined as the
integration of the weighted GMM energy function
over the entire image. In the objective function, a
truncated Gaussian kernel function is used to
impose the spatial constraint, and fuzzy
memberships are employed to balance the
Fig. 6 Final module
By using Fuzzy based image segmentation, contribution of each GMM to the segmentation
we can segment the brain MRI image and can find process [24].
the Gray matter (GM), White matter (WM), and
Step 1: Initialization.
Brain tumour.
Initialize the number of clusters, standard deviation,
E. Fuzzy C Means algorithm
and neighborhood radius of the truncated Gaussian
kernel, cluster centroids, and bias field at each
The Fuzzy C-Means (FCM) algorithm is voxel.
commonly used for clustering. The performance of
the FCM algorithm depends on the selection of the Step 2: Updating parameters.
initial cluster center and/or the initial membership
value. If a good initial cluster that is close to the Step 2.1: Updating membership function
actual final cluster center can be found, the FCM
algorithm will converge very quickly and the
(5)
processing time can be drastically reduced [15].
Step 1: Initialize the membership matrix U with Step 2.2: Updating covariance matrix
random values between 0 and 1 such that the
constraints in Equation (1) are satisfied.
(1)
(6)
Step 2.3: Updating bias field
Step 2: Calculate c fuzzy cluster centers, ci, i=1…...
c using Equation (2).
(2)
(7)
Step 2.4: Updating mixture weight
(8)
Step 3: Compute the cost function according to
Equation (3). Stop if either it is below a certain
tolerance value or its improvement over previous
iteration is below a certain threshold. Step 2.5: Updating centroids
(3)
Step 4: Compute a new U using Equation (4). Go to
step 2.
(4)
F. Fuzzy Local Gaussian Mixture Model Algorithm
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(9)
Step 3: Checking the termination condition.
If the distance between the newly obtained
cluster centers and old ones is less than a user-
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International Journal of Engineering Trends and Technology (IJETT) – Volume 7 Number 2- Jan 2014
specified small threshold ε, stop the iteration;
otherwise, go to step 2.
G. Pulse Coupled Neural Network Algorithm
The Pulse-Coupled Neural Nets (PCNN)
algorithm is based on the neurophysiologic models
evolving from studies of small mammals. Shown in
Fig.1, the PCNN will receive both stimulus by
feeding and also inhibitory linking [11]. These are
combined in an internal activation system. Which
accumulates the signals until it exceeds a dynamic
threshold, resulting in an output. This alters the
threshold as well as linking and feeding neurons, as
will be described below.
a) Let tmax=20; t=1;
b) Let the adjusted threshold
Ta(t)=T(n);
c) The first iterative t=1, from
(10)~(14),calculate each PCNN internal and
output part, then put the result in Yij (t)(n);
(10)
(11)
(12)
(13)
(14)
Fig. 7 PCNN neuron
d) Find out the pixels pulsed in this sub-iterative,
and calculate NIF of their neighbors;
e) Calculate the linking of these neighbors with
Eq.(15);
The PCNN produces a temporal series of outputs.
Depending on time as well as the parameters, this
(15)
dynamic output contains information, which makes
f) Calculate the internal activity of these
it possible to detect edges, do segmentation,
neighbors and compare with Ta( t) , then
identify textures and perform other feature
affiliate the output into Yijt (n) .
extractions. The PCNN can operate on different
g) Ta(t) = Ta(t) - N (ε) , where N(ε) is the
types of data since it is very generic to its nature.
normalization of ε.
The algorithm is performed by continual iterations
h) t=t+1;
of the input and the output using the following steps.
i) If t<tmax then move to step c); otherwise
output Yij(n).
Step1: Initialization
• Let the unitary image greyhound value as the
Step4: n=n+1. Tsave= Yij(n).
impulse signal Sij;
• Initialize the parameter of the net;
Step5: If n < nmax, then move to step 3; otherwise
• Set the maximum iterative times nmax= 30.
end and output Tsave, which is the result of
segmentation.
Step2: Let iterative variable n = 1
Step3: Fastlinking processing: t is the iterative
variable
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International Journal of Engineering Trends and Technology (IJETT) – Volume 7 Number 2- Jan 2014
IV. RESULTS
Fig. 11 Segmented images using FLGMM algorithm
Fig. 8 Input images (first row) and unwanted text in that images (second row)
Fig. 12 Segmented images using PCNN algorithm
Fig. 9 Unwanted text removed images (first row) and preprocessed images
(second row)
V. CONCLUSION
The PCNN belongs to a unique category of
neural networks, in that it requires no training
unlike traditional models where weights may
require updating for processing new inputs. Specific
values of PCNN Coefficients used in this work
were derived from Johnson and Padgett [11] and
Waldemark et al [21]. MATLAB coding developed
for Fuzzy Local Gaussian Mixture Model
(FLGMM), Fuzzy C Means (FCM), Pulse Coupled
Neural Network (PCNN) algorithms to segment the
brain MR images. Results show that the PCNN
algorithm is capable of producing more accurate
segmentation results than FCM and FLGMM
algorithms.
Fig. 10 Segmented images using FCM algorithm
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International Journal of Engineering Trends and Technology (IJETT) – Volume 7 Number 2- Jan 2014
ACKNOWLEDGMENT
I am very thankful to Dr. N. A.
MURUGESAN, M.B.B.S., DMRD, Director of
Johnson MRI, Erode, for contributing MR images
for this work.
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