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Research Article
LOCATING BRAIN TUMOUR AND EXTRACTING THE FEATURES
FROM MRI IMAGES
Abhishek Saxena and Suchetha .M
Abstract
The seriousness of brain tumour is very high among all types of cancers. So immediate detection and
proper treatment can save a person’s life. In this paper a system for brain tumour extraction is designed.
In the pre-processing stage, noise is removed and the texture features are extracted from it. Then with the
help of the obtained features classification of images into normal and abnormal is done using
morphological classifier. Then on abnormal image bounding box algorithm is used to locate the tumorous
part. After this morphological is used to separate out the tumorous region from the abnormal image.
Key Words: Melasma MRI, bounding box, clustering, Morphological operators
INTRODUCTION
An abnormal growth of the tissue in brain is called
brain tumour. This abnormal brain tissue keeps on
growing and keep on multiplying without any control.
Brain tumour are of metastatic and malignant or
benign type. The primary or metastatic is a type of
tumour that had come from another cancer cells
contained body part to the brain.The brain tissues
can be diagnosed by using the scans like Computed
Tomography (CT) scans ,Magnetic Resonance Imaging
(MRI) scans,Positron Emissions Tomography (PET) scans
Biopsy (tissue sample analysis)
But Magnetic Resonance Imaging (MRI) is an excellent
method which give us high quality images for the
check up of the cancer cells.MRI image will give us an
opportunity to determine the abnormality present in
the brain without any risk to patient’s health.
One of the major stage in the every image
processing system is the process of classification. The
classification of the given input image should be done
under two classes i.e. normal and abnormal class.
Classification is done with the help of features
contained the tumour containing image and normal
image. In feature extraction ,the transformation of
input image data into sets of features is done. If the
precise features are extracted from MRI then the
further processing can be done quickly. Feature
extraction plays an crucial role in determining the
performance of the classifier. For classification of
normal and abnormal ,ensemble based classification
is used[1].After classification the partitioning is
performed on the tumorous image for extracting the
tumour region.
Considering the fact that most of the time MR image
have less contrast therefore these segments can be
imbricate on eachother.The location of the timorous
part in the malignant image is done by bounding box
method.
LITERATURE SURVEY
There are many stages in the image processing
system. Initially In the pre-processing stage the MRI
image has to be filtered if any noise is present in
it.MRI image mainly consist of Gaussian noise and this
gaussain noise can be filtered by using, mean filter,
K-Means filter Bilateral filter, Trilateral filter. But our
proposed method is using wiener filter. In the past
few years there are many methods used for
classification which are mainly of two basic types:
a) Supervised learning: Artificial neural network
(ANN),
Support vector machine (SVM), Knearest neighbor (KNN)
b) Unsupervised learning: k-means clustering, Self
organizing Map (SOM).
Features are extracted for image classification which
is indeed a complex task. The features can be
extracted by wavelet transform, decision boundary
features, spectral mixtures analysis. Datta et al
(2011) introduced color-based
VIT Univesity, Tamil Nadu, India
Correspondence and Reprint Requests: Abhishek Saxena
Received: March 11, 2015 | Accepted: March 18, 2015 | Published Online: March 28, 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (creativecommons.org/licenses/by/3.0)
Conflict of interest: None declared | Source of funding: Nil
Abhishek Saxena and Suchetha.M
segmentation using k-means clustering for brain tumor
detection. The developed algorithm shows better
result than Canny based edge detection.
Morphological operations are performed for
extracting the tumorous part. After segmenting out
the tumour part the area of the tumour is found out.
METHODOLOGY
PROPOSED METHOD
Classification phase
Pre-processing Stage
The input image is taken and passed through wiener
filter, then textual features are extracted such as
GLCM features. After that classification of image is
done.
Most of the time MR images are distorted due to the
noise added to the different phases from image
acquisition to the transfer of the image into the digital
form. So noise removal is necessary part before any
further processing on images. Gaussian noise mostly
appeared into MR images. Proposed system first
adds Gaussian noise in the brain MR images. Wiener
filter is used is used for noise removal.
Database Input Image
Weiner Filtering
Feature Extraction
Textual feature extraction
The transformation of an image into its set of features
is known as feature extraction. Useful features of the
image are extracted from the image for classification
purpose.
Classify
First-order histogram based features
Normal
Tumorous
Image
Image
Thus, normal and abnormal i.e. tumorous image is
segmented
Locating and Segmentation
The classified tumour image is now processed through
boundary box algorithm for the location of tumour.
Tumorous Image
Locating the tumour
Edge detection
Histogram of the image gives summary of the
statistical information about the image. So first order
statistical information of the image can be obtained
using histogram of the image. Direct command is used
for the mean, variance ,entropy in matlab.
Co-occurrence matrix based features
Histogram based features are local in nature. These
features does not consider spatial information into
consideration. So for this purpose gray-level spatial
co-occurrence matrix based features are defined
which are known as second order histogram based
features. Contrast, Correlation, Energy, Homogeneity
are found using matlab inbuilt function.
Gray-Level Co-occurrence Matrices (GLCMs) based
features are extracted in this method. These features
are extracted using the following equations:
a)
b)
c)
d)
Dilate and fill holes
Clear border
Segmented tumour
1+[x-y]
(1)
(2)
(3)
(4)
Problems associated with the co-occurrence matrix
methods
1.
14
Contrast: Ʃx Ʃy(x-y)2 Cxy
Entropy: -Ʃx ƩyCxy logCxy
2
Energy: Ʃx Ʃy
xy
Homogeneity: Ʃx Ʃy cxy
They require a lot of computation i.e. so many
International Journal of Current Innovation Research, Vol. 1, Issue1, pp 13-18, March 2015
Locating Brain Tumour and Extracting the Features from MRI Images
2.
matrices to be computed.
Features are not invariant to rotation or scale
changes in the texture.
Classification
Classification is that the procedure for classifying the
input patterns into set of classes. Classification
classifies the unknown information samples. Selection
of an appropriate classifier needs thought of the
many factors like process resources it used, accuracy
of the classifier for many datasets, and performance
of the formula. Figure 1 shows a system used for
Ensemble base classifier for the classification
purpose.
subscripts indicate whether this histogram is of image
I or of template R, and the superscripts denote
whether this histogram is computed within the region
T(s) or within the region B(s).
Figure 2 Finding D from image I, using a reference
image R
Figure 3 A typical score function plot.
Figure 1 Ensemble based classification
This Ensemble base classifier uses support vector
machine for classification. This system divides
knowledge (the information) set arbitrarily into
coaching and testing data. K-fold testing is applied
on coaching information. To avoid over fitting hold
out testing is applied on testing information. Hold out
testing ensures non-overlapping of coaching and
testing information. simple linear support vector
machine
Identifying the location of tumor
Figure 2, with a horizontal dotted line drawn at a
distance s from the top of the images. Now consider
the regions:
A1 (s) = [0,wd]×[0, s], and A2 (s) = [0,w]×[s, ht],
(4)
where wd and ht are respectively the width and the
height of both the images I and R. Thus A1(s) and
A2(s) are the portions of image domain respectively
above and below the aforementioned horizontal line.
Let E(s) denote the following score function:
E(s)=(√PIA(s),√ P RA(s)) –(√P IB(s),√ P RB(s))
(5)
Where P’s denote normalized intensity histograms
(probability mass functions of image intensities), the
The inner product between square roots of two
normalized histograms is known as Bhattacharya
coefficient (BC),] which is a real number between 0
and 1 that measures the correlation between two
histograms. When both normalized histograms are
identical, their BC value is 1; whereas once the
histograms aren't identical, then BC price is zero. Note
that the score function E(s) measures the difference of
correlations between the upper histograms and the
lower histograms. We therefore expect a high score
when the upper histograms match very well, while the
lower histograms have high mismatch. On the other
hand, a low value of E(s) denotes a low correlation
between upper histograms, and a high correlation
between lower histograms. Based on these
observations, we note that a plot of E(s) vs. s should
look like the plot shown in Figure 3. The important
observations in Figure 3 are that the plot has three
distinct regions – increasing, decreasing, then
increasing – where the decreasing segment begins at
l and ends at u, where l and u respectively denotes
the lower and the upper bound for the rectangular
region D, measuring from top of the image. In fact we
can prove these statements rigorously with some mild
assumptions about the data, i.e., about the image I
and the template R. Essentially, we require that the
correlation between the image histogram outside D
and the template histogram is much larger than that
between the image histogram inside D and the
template histogram.
International Journal of Current Innovation Research, Vol. 1, Issue 1, pp 13-18, March 2015
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Abhishek Saxena and Suchetha.M
SIMULATION RESULTS
Original Image: The image for the proposed system
is taken
MR Image
Boundary and line of symmetry
50
50
100
100
150
150
200
200
250
250
50 100 150 200 250
Score plot for vertical direction
0.15
Figure 4 Original Image
Added gaussain noise: Gaussian noise is added in
the input image
50 100 150 200 250
0.4
0.1
0.2
0.05
0
0
-0.2
-0.05
0
100
200
300
-0.4
Score plot for horizontal direction
0
50
100
150
Figure 7 Finding a bounding box on brain MRI.
Segmentation of tumour
Sobel operated
In the classified image, sobel operator is used to
detect the edge which uses mask as given below
Figure 5 Noisy Image
Filtered Image:
Note that this operator is placed on associate
emphasing pixelsthat ar nearer to the middle of the
mask,which made us to choose this operator.
Figure 6 Filtered Image
Textual features:
Classification of image is done by textual feature so
seven features are extracted.
I. Root mean square error (RMSE) : 39.9990
II. Mean = 132.2461
III. Entropy= 6.6534
IV. Contrast: 0.3947
V. Correlation: 0.9353
VI. Energy: 0.2956
VII. Homogeneity: 0.9170
Locating the tumor
The proposed bounding box algorithm only provides
a rough estimate of the abnormal region. However,
after finding the bounding box, we can fine tune the
segmentation boundary as shown in Figure 7.
16
Figure 8 Edge detected
Dilation operation
To convert the edge detected output to an binary
image dilation operation is done in Figure 9.
Filled holes
The binary image is filled with holes wherever
empty holes are present
International Journal of Current Innovation Research, Vol. 1, Issue1, pp 13-18, March 2015
Locating Brain Tumour and Extracting the Features from MRI Images
MR images. After that texture features are extracted
from these noise free MRI images. These extracted
features are used for classification. After
classification into normal and tumorous image the
location of the tumor in the image is found out. Then
edge
detection
is
done
using
sobel
operator,dilated,filled holes in it, cleared out the
boundaries and finally segmented out the tumor. Also
the area is calculated.
Figure 9 Binary conversion
FUTURE WORKS
In future ensemble based classification will be
performed. and segmentation will be done using
support vector machine(SVM) technique as it is more
efficient technique than other segmentation
techniques.
References
Figure 10 Binary image with filled holes
Final segmented image
After clearing the border image is segmented out
with the diamond based mask.
Figure 11 Final Segmented tumour
Area of tumour
Area of objects in binary image is calculated by
area an image using inbuilt matlab function, which
estimates the area of the objects in binary image.
Given image can be numeric(for this input, any pixels
having nonzero values are considered to be on) or
logical. The results has the class of double.
Here the area of tumor = 1.4199e+03
CONCLUSION
The planned system is developed for the diagnosing
of tumour from magnetic resonance imaging pictures
of the brain. This method makes the diagnosing in
many phases.In the preprocessing stage,noise
elimination using wiener filter is performed on brain
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