ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer
and Communication Engineering
(An ISO 3297: 2007 Certified Organization)
Vol. 3, Special Issue 2, March 2015
An Automated Computer Aided System for
Tumor Detection in Brain
Shanthi.U1, Sowmitha.R2, Sugapriya.P3, Vimalavani.U4, Johannahpersis.A5
Final year, Department of ECE, Narasu’s Sarathy Institute of Technology, Salem, Tamilnadu, India 1,2,3,4
Assistant professor, Department of ECE, Narasu’s Sarathy Institute of Technology, Salem, Tamilnadu, India5.
ABSTRACT: Image processing is an active research area in which medical image processing is a highly challenging
field. Brain tumor is a serious life altering disease condition which faces the challenge of detecting brain tumor
through Magnetic Resonance Image (MRI).The proposed fast bounding box method (FBB) using symmetry will be
very helpful for segmentation and detection. This algorithm consists of the three stages namely pre processing, region
of interest (ROI) and post processing. At last the post processed image undergoes feature extraction for classification of
tumorwith the help of database.
KEYWORDS: Segmentation, MR image, Detection, tumor, Feature extraction.
I.INTRODUCTION
Brain tumor is caused by an abnormal growth of Cell in brain. Normally brain tumor emerges from brain cells,
blood vessels or nerves that are present in the brain. Early detection of brain tumor is necessary as death rate is higher
among humans having brain tumor[1].
Techniques for brain tumor detection using processing has been present for few decades.
Researchers have proposed many semi-automatic and image processing technique to detect brain tumor but
most of them fail to give effective and precise result due to the presence of noise, inhomogeneity, poor images contrast
that occur usually in medical images. Brain tumor segmentation is very difficult due to complex brain structure but
early and accurate detection of tumor, edema and necrotic tissues is very important for diagnostic system.
Tumor can be damage normal brain cells by producing inflammation, exerting pressure on parts of brain and increasing
pressure within the skull[2]. Automatic brain tumor detection and segmentation face many challenges. Brain tumor
segmentation requires the efficient knowledge of pathology and understanding the intensity and shape of MRI image.
The main problem in tumor segmentation arises due each tumor being of different shape, location and
intensity. Manual detection of brain tumor requires human interaction and is time consuming. Also it depends on the
ability of observer to locate the location, shapeand size of the tumor. Thus ,a need of completely computer aided system
for brain tumor detection is inevitable. This paper is arranged in four parts.
II.RELATED WORK
Presently existing algorithms in medical image processing employ partial differential equations, curvature
motivate flows and diverse mathematical models. In 1991, weaver et al.[2] first presented the use of wavelet theory in
medical imaging processing by reducing noise in MRI images. H.B.Kekre et al have presented a vector quantization
segmentation method to detect cancerous mass from MRI images.[3] JueWu and Albert C.S Chung[4] developed a
frame work formality-object segmentation of deep brain structures in medical brain images. Manj6n et al. [5] have
analysed random noise removal filter and adapted to reduce this noise in MR magnitude images. V.Thavavela et al.[6]
have proposed agenetic algorithm –based wavelet domain denoising technique Murugavalli et al. Used high speed
parallel fuzzy c-mean and neuro fuzzy algorithm for brain tumorsegmentaion[7].[8]. There are many other semi
automatic and maual algorithms for brain tumor detection. A completed automated diagnosis system for brain
tumorsshould consist of multiple phases including noise removal ,braintumor detection and brain tumor extraction. This
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ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer
and Communication Engineering
(An ISO 3297: 2007 Certified Organization)
Vol. 3, Special Issue 2, March 2015
paper proposes a uses fast bounding box algorithm by BaidyaNathSaha et al[9], to developed an automated computer
aided system for brain tumor detection.
III.PROPOSED ALGORITHM
Given a MRI image, the first step enhances the image, in the second step fast bounding box algorithm is used to
detect region of interest and in third step post processing operation used for segmentation tumor. And then feature
extraction to detect its features. Atlast the exact tumor location can be finded as soon as possible.
Fig 1. Overview of brain tumor detection
A.Preprocessing:
Pre-processing is the first step in our technique. Preprocessing is done reduce noise and enhance the image for further
processingThis step improve image quality and increase surety and accuracy in detecting tumor. steps involved in
preprocessing are as follows:1) image is converted into grayscale
2) A 3x3 median filter is applied on the image using the equation 1 in order to remove noise(1)
3)The obtained image is then passed to a high pass edge detection filter.
4) The detected edges are added to the original image to enhance it edges. This makes tumor detection and
segmentation easy.
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ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer
and Communication Engineering
(An ISO 3297: 2007 Certified Organization)
Vol. 3, Special Issue 2, March 2015
Fig.2 preprocessing
a) original image
b) enhanced image
B.RegionOf Interest:
our proposed technique used fast bounding box algorithm to detect the region of interest (ROI). Bounding box
approach is based on unsupervised change detection method that searches for the most dissimilar region between the
left and right halves of a brain in an axial view MR slices. This change detection process uses a novel score function
based on bhattacharya coefficient computed with gray level intensity histogram. steps involved in detecting tumor
using boundary box method are as follows:1)Axis symmetry on axial MR slice is found which divides brain in two halves left(I)and right(R).
2) One half serves as test image and other half supplies as the reference image.
3) Novel score function is used which identify the region of change with two searches-one along the vertical
direction another along the horizontal direction.
4) Novel score function uses the bhattacharya coefficient to detect the rectangle D which represents the region of
interest between images I and R. boxtumor detectiontumor detection
fig.3 Region of interest a)original fast bounding
b)enhanced fast bounding box
C.Post processing:
After the ROI is detected, several post processing operations are performed to clearly locate the tumor part in
the brain. The purpose of post processing is to show only that part of the image D, which has the tumor. post processing
technique includes several mathematical operations and windowing techniques. The basic steps of post processing are
as follows:1) The rectangle box D ,detected by fast bounding box algorithm is stretched so that entire tumor is correctly
detected.
2) mean intensity (M) of pixels in D is calculated.
3) Threshold is applied to D above pixel intensity M.
4) Next blobs are detected in the rectangle D.
5) finally, windowing techniques are used to obtained brain tumor detected image by selecting the blob with
maximum area.
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Vol. 3, Special Issue 2, March 2015
fig.4 a) segmented
tumor
b)output of mathametical c)output of
d) tumor extracted
operation
windowing
image
IV.FEATURE EXTRACTION
In pattern recognition and in image processing , feature extraction is a special form of dimensionality reduction. When
the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant then the input
data will be transformed into a reduced representation set of feature (features vector). Feature extraction is helpful
identifying brain tumor where is exactly located and help in predicting next stage. Transforming the input data into the
set of features is called feature extraction. In this paper we are extracting some features. They are,
 Contrast
 Correlation
 Homogeneity
 Energy
 Entropy
 Shape
 Colour
 Texture
 Intensity
CONTRAST:
Contrast is defined as the separation between the darkest and brightest area.
n-1
𝐶𝑜𝑛𝑡𝑟𝑎𝑠𝑡 =Σ Pi,j (i-j)2
i,j=0
CORRELATION:
Correlation is computed into what is known as the correlation coeffecient, which ranges between -1
And +1
n-1
𝐶𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛 =Σ pi,j (i-μ)(j-μ)
i,j𝜎2
HOMOGENITY:
Homogenity is defined as the quality or state of being homogeneous.
n-1
𝐻𝑜𝑚𝑜𝑔𝑒𝑛𝑖𝑡𝑦 =Σ Pij / 1+(i-j)2
𝑖,=0
ENERGY:
It provides the sum of squared elements in the GLCM. Also the uniformity or the angular second moment.
N-1
𝐸𝑛𝑒𝑟𝑔𝑦 = Σ (Pij)2
𝑖,=0
ENTROPY
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ISSN (Print): 2320-9798
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Vol. 3, Special Issue 2, March 2015
Entropy is a measure of the uncertainty in a random variable.
N-1
𝐸𝑛𝑡𝑟𝑜𝑝𝑦 =Σ- (pij )2
𝑖,=0
SHAPE:
The term shape is commonly used to refer to the geometric properties of an object or its external boundary, as opposed
to other properties such as color, texture, material composition.
COLOR:
Colour is a component of light which is separated when it is reflected off of an object. Colours can be identified
numerically by their coordinates.
INTENSITY:
Intensity is a purity or strength of colour.
TEXTURE:
It is the visual characteristic of a surface. For example, a surface can be rough or smooth.
V.CONCLUSION
In this paper brain tumor segmentation and detection is done using MR images. The proposed method uses an easy and
completely automated way to detect and segmaenttumor with good accuracy. Finally the feature was extracted. The fast
bounding box method region based approximate segmentation technique can enable effective MRI data bases indexing
system. The resulting method is very fast, robust and reliable for indexing tumor are edema images for both archival
and retrievel purposes and it can use as a vehicle for further clinical investigations.
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