50120140503014

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 122-131 © IAEME
122
BRAIN TUMOR AND EDEMA DETECTION USING MATLAB 7.6.0.324
Nidhi1
, Poonam Kumari2
1, 2
UCIM/CIL/SAIF, Panjab University, Chandigarh
ABSTRACT
Advanced techniques of medical image processing and analysis find widespread use in
medicine. Various imaging modalities like CT scan, MRI, ultrasound are being used for imaging
brain tumors. In recent years, MRI has emerged as the best for clear identification of cancer and
other anomalies in breast, prostate, liver, brain etc. The tumor detection becomes more complicated
for the huge image database especially when edema is present with the tumor. So a software
approach is needed to aid the accurate, faster clinical diagnosis.
Proposed work focuses on the detection of brain tumor and edema from MRI images using
MATLAB 7.6.0.324 and clear distinction between tumors and edema. The objective is to provide
advanced image processing tools in a format that is user friendly and is inexpensive too. The study
aims to introduce an algorithm which incorporates useful operations on the MRI brain image
including filtering, enhancement, arithmetic operations, segmentation, extracting region of interest,
and morphological operations. Here we detect the tumor and edema, segment them and the final
image having clear boundary between edema and tumor is superimposed on the original image to
highlight the tumor and edema boundaries. Our study aims to help the physician for surgical
planning.
KEYWORDS: Edema, MRI images, co-resemblance, Histogram Equalization, Segmentation, Sobel
Edge Detection Filter, Image Superimposition.
1. INTRODUCTION
Brain is the central processing unit of world’s most complicated machinery, that is, human
being. Brain acts as the in charge of human thoughts, feelings, speech, and memory and also plays a
pivotal role in controlling muscle movements. Brain helps in the interpretation of sensory
information. A tumor is an abnormal new mass of tissue that serves no purpose. The term brain
tumor is used to describe any tumor growing within the skull, though a more accurate term might be
intercranial tumor. Brain tumor is defined as any intercranial tumor created by abnormal and
uncontrolled cell division, normally either in the brain itself(neurons, glial cells, lymphatic blood
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
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ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 3, March (2014), pp. 122-131
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vessels), in the cranial nerves(myelin), in the brain envelopes(meninges), skull, pituitary and pineal
gland or spread from cancers primarily located in other organs [1].
The symptoms of brain tumor depends on tumor size, type and location. Some common
symptoms of brain tumor are-
• Headaches.
• Nausea and vomiting.
• Changes in speech, vision or hearing.
• Problems in walking.
• Seizures or convulsions.
• Changes in mood, personality or ability to concentrate.
• Problems with memory.
1.1 Common types of brain tumor
A brain tumor may be of primary or secondary type depending on its location of origin.
Primary tumors originates in the brain itself while the secondary tumors originates in some other part
of body and then spread to brain. There are two categories of brain tumors according to the most
commonly used classification-
Benign- Benign tumors are non-cancerous mass of cells that grows slowly in the brain. It usually
stays in one place and does not spread. These tumors can be removed and they seldom grow back.
Most of the benign brain tumors are detected by Computed Tomography (CT) and Magnetic
Resonance Imaging (MRI) scans. Benign tumors, however, can be life threatening because they can
compress brain tissues and other structures inside the skull. The following are the most frequently
diagnosed benign brain tumors- Meningioma, Schwannoma, Pituitary adenomas,
Hemangioblastomasa, Craniopharyngioma [2][3].
Malignant- A malignant brain tumor is a rapidly growing cancer that spreads to other areas of the
brain and spine. Most of the malignant brain tumors are secondary but can be primary too. These
tumors are life threatening. Common malignant brain tumors are- Gliomas, Ependymomas,
Oligodendrogliomas, Mixed gliomas [2][3].
1.2 Edema
Edema is commonly known as brain swelling which can occur in specific location in vicinity
of the brain tumor or throughout the brain. It is the “extra fluid” within the tissue of the brain. Edema
increases intercranial pressure which can prevent blood from flowing to the brain, thus depriving it
of the oxygen it needs to function. Damage or death of brain cells may result [4].
1.3 Diagnosis of brain tumor and edema
One or more of the following methods may be used to detect the presence of a brain tumor
having edema and if it has spread-
• Biopsy.
• Sterotactic Biopsy.
• Surgery.
• Lumbar Puncture.
Imaging methods
• Computed Tomography (CT) scan.
• Magnetic Resonance Imaging (MRI).
• Positron Emission Tomography (PET) scans.
• Diffusion Tensor Imaging (DTI).

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The MRI method is the best in detecting brain tumors due to its high resolution and ability to
show clear brain structures, tumor’s size and location. MRI makes use of the property of Nuclear
Magnetic Resonance to image nuclei of atoms inside the body. It provides a good contrast between
different soft tissues of the body which makes it useful in imaging brain cancers. MRI image of brain
having tumor with edema is used in the proposed work [1].
3. PROBLEM FORMULATION
Detection of brain tumor having edema as its most prominent feature is a serious issue in
imaging science. Generally, the grading and analysis of brain tumors having edema is done by
doctors by simply viewing the image scans which is a very difficult task due to very minute
variations. Based on the expert’s analysis and detection, the tumor excision in surgery is performed
and it can have chances to get the false detection. The problem of manual detection becomes more
severe when the database is too large. An important step in analysis of MRI brain images is to extract
the boundary of the tumor part which becomes more complicated when tumor have edema in its
vicinity. Due to co-resemblance between tumor part and edema, the biological analysis thus becomes
prediction of affects.
To solve the problem, the proposed work describes the strategy for detection, segmentation
and feature extraction of brain tumor part and edema in an easy to use, inexpensive format using
MATLAB software. This software based approach aims to introduce an algorithm for detecting and
segmenting the brain tumor and edema from normal brain using basic image processing operations(
preprocessing, enhancement, segmentation, morphological operations, feature extraction) in
MATLAB 7.6.0.324.
4. METHODOLOGY
The proposed algorithm follows the following sequence of steps-
Image Acquisition
Data Import
Convert original image to grayscale image.
Preprocessing
Image Enhancement
Image Segmentation
Morphological Operations
Final image is superimposed on original Image
Brain tumor and edema detected

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4.1 Image Acquisition- Image acquisition in image processing can be broadly defined as the action
of retrieving an image from source, usually a hardware based source, so it can be passed through
whatever processes need to occur afterward[5]. Image acquisition is the first step in the proposed
workflow sequence. MRI brain images are used in the proposed system. MRI uses magnetic field
and radio waves to provide detailed information about brain tumor with edema anatomy, cellular
structure and vascular supply, making it an important tool for the effective diagnosis, treatment and
monitoring of the brain diseases [6]. The images used in the present study are acquired from
Satyakiran Hospital, Sonepat, Haryana.
4.2 Importing data in MATLAB- Image processing toolbox in MATLAB supports images
generated by a wide range of devices, including, digital cameras, satellites, airborne sensors, medical
imaging devices, microscopes, telescopes and other scientific instruments. Image processing toolbox
shows compatibility with a number of specialized image file formats. For medical images, it supports
DICOM files. There are several ways to import the data into MATLAB 7.6.0.324 environment for
processing. In the present study the database is in CD-ROM and stored on the desktop of computer,
then the desktop path is entered in the address area of the MATLAB 7.6.0.324.
4.3 Grayscale Imaging- Generally, grayscale imaging is sometimes called “black and white” but in
technical terms it is a misnomer. The true black and white is known as halftone which consists of
only possible shades of pure black and white. A grayscale image only consists of shades of gray
without apparent color. When MRI images are viewed on computer screen, they look like black and
white but in actual they contain some primary colors (RGB) content. So, for further processing of
MRI brain image, it must be converted to perfect grayscale image in which the red, green and blue
components all have equal intensity in RGB space(The lightness of the gray is equal to the number
representing the brightness levels of primary colors. The brightness level of RGB is a number from
decimal 0 t0 255 or binary 00000000 to 11111111. For every pixel in a RGB grayscale image,
R=G=B where black is represented by R=G=B=0 and white by R=G=B=1)[6][7]. So it becomes easy
to process grayscale images (only single intensity value is necessary for each pixel) as compared to
full color image ( three intensity values are necessary, each for RGB, for a single pixel). The brain
image received from MRI is converted to grayscale image by eliminating hue and saturation
information while retaining the luminance. The original MRI brain image has properties 320x320x3
and conversion to grayscale image makes the properties 320x320. The grayscale brain image is then
converted to double data class type.
Fig.1: Original Image Fig.2: Grayscale Image

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4.4 Preprocessing- Preprocessing is the initial step for detecting brain tumor with edema. Basically,
this process involves denoising the image and increasing the signal to noise ratio using different
filtering techniques [8]. MRI images are bound to have some noise in them and white noise is one of
the most common problems in processing MRI images. Noise may also be introduced due to motion
artifacts (movement of patient during scan) in the MRI images. The most common methods used for
preprocessing of medical images till now are the low pass filtering( for sharpening), averaging
filters( for smoothening) , but these methods have certain drawbacks like LPF of MRI image may
blur the edges while averaging filters may blur the details as well as edges in an image. Moreover,
averaging filters are not as effective for impulse noise (salt and pepper noise) [5].
So to overcome above stated problems, the following preprocessing methods are used in the
present study-
4.4.1 High Pass Filtering- It is done to sharpen the image and a high pass filter forms the basis for
most of the sharpening methods. Sharpening filters are the most often and abused processing tools.
When applied properly, sharpening can often improve apparent image quality by making it look
crisper and more defined. However, not all sharpening methods are created equal. When performed
too aggressively, unseen sharpening artifacts may appear. A high pass filter preserves the high
frequency information within an image while reduce the low frequency information, thus
emphasizing the transitions in the image intensities. It works by analyzing the values of each pixel in
an image and changing it based on the values of its neighbors. In high pass filtering, the brightness of
the centre pixel is increased relative to its neighboring pixels by the kernel of the filter. The kernel
array consists of a single positive value at its centre, which is completely surrounded by negative
values.
4.4.2 Median Filtering-It is done for smoothening of MRI brain image. Median filtering is very
effective for removing “salt and pepper” noise (random occurrences of black and white pixels) [6]. It
is somewhat like mean filter. However, it often does a better job than the mean filter by preserving
useful detail in the image. The median filter considers each pixel in the image and looks at its nearby
neighbors to decide whether or not it is representative of its surroundings [5][6][7]. The value of the
output pixel is then determined by median of the neighborhood pixels. The median filter does not
create new unrealistic pixel values when the filter straddles an edge in the image. For this reason, the
median filter is much better at preserving sharp edges than other filters used till now ( like mean,
average filters etc.). Fig.3 shows the impact of “salt and pepper” noise on the grayscale image and
fig.4 depicts the noise free image of brain.
Fig.3: Salt & Pepper noise in image Fig.4: Median filtered image

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4.5 Image Enhancement- Image enhancement brings out the details that are obscured and highlight
certain features of interest in an image. The fundamental enhancement needed in the MRI images is
the contrast enhancement. Contrast is the main reason for the co-resemblance between the tumor part
and edema in the MRI brain image. Due to poor contrast, the tumor region and edema are considered
the same by the doctors by manually viewing the MRI scans. Contrast between the brain, tumor part
and edema may be present in the MRI image but below the threshold of human perception. Different
methods have been proposed till now for image enhancement like basic gray level transformations,
binarization etc. In the present approach two methods are used for enhancing the contrast of MRI
brain image.
4.5.1 Arithmetic Operations-Arithmetic operations are performed on a pixel by pixel basis between
two or more images. The actual mechanics of implementing arithmetic operations can be done
sequentially, one pixel at a time, or in parallel, where all operations are performed simultaneously.
There are total four arithmetic operations (addition, subtraction, multiplication and division) that can
be applied on an image [5].In the present paper, subtraction operation is performed between the
grayscale and the double class MRI brain image. The subtraction operation is expressed as-
G(x,y) = F(x,y)-H(x,y)
and the difference is obtained by computing the difference between all pairs of corresponding pixels
from F and H. The key usefulness of subtraction is the enhancement of differences between images
which is its main advantage over other methods used till now.
4.5.2 Histogram Equalization- Histogram equalization is a technique for adjusting image intensities
to enhance contrast of an image. Better contrast is obtained via the histogram of the image, then
using histogram equalization that allows the areas with low contrast to gain higher contrast by
spreading out the most frequent intensity values. As it can be seen in the subtracted image that edema
and tumor part have very close contrast values and histogram equalization then increases the global
contrast of the MRI brain image. This method is used because the intensities can be better distributed
on the histogram which represents the relative frequency of occurrences of various gray levels in the
image.
Histogram equalization is a three step process [8]-
• Formation of histogram.
• Calculation of new intensity values for each intensity level of image.
• Replace the previous intensity values of the image with new calculated intensity values.
The change in contrast between tumor part and edema is clearly shown in fig.5 and fig.6. The
black colored region is edema and the grayish smoky part is the tumor region in brain.

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Fig.5: Subtraction image Fig.6: Histogram equalization
4.6 Image Segmentation- It is the division of an image into meaningful structures. Segmentation
subdivides an image into its constituents regions or objects and it is an essential step in image
analysis, object representation, visualization and many other image processing tasks [5]. More
precisely, image segmentation is the process of assigning a label to every pixel in an image such that
pixels with same label share certain identical visual characteristics. The resultant image is a set of
segments that collectively cover the entire image or a set of contours extracted from the image.
Generally, the doctor always need to have keen observation of the anatomical structure during the
process of manual segmentation in MRI scans. But this process is too much time consuming and if
the initial segmentation result is not correct than other subsequent results also produces incorrect
measurement results.
A great number of segmentation methods has been employed in the past decades for brain
tumor segmentation like clustering methods, fuzzy logic approach, neuro-fuzzy approach, watershed
segmentation, random walk etc. but these all methods produces unsatisfactory results due to unsharp
edge boundaries and more time consumed to produce desired result [13]. Moreover, these methods
can only segment the tumor region but edema present in the vicinity of tumor region can not be
distinguished from the tumor part. In the present paper, we are using a filter to segment the tumor
region and edema from the normal brain. The filter used is sobel edge detection filter which is
commonly used in the computer vision. Sobel is a gradient operator which detects the edges by
looking for the maxima and minima in the first derivative of the image and the result is a vector
valued operator. Sobel operator applies gradient filter which average the image perpendicular in
gradient direction. The main advantages of using sobel operator over other methods used till now
are-
• Errors in magnitude and angle are smaller than with discrete differences.
• Smaller anisotropy.
• Clear segment tumor part from edema which proves helpful in tumor excisions during
surgery.
Fig.7 and fig.8 are showing the boundaries between different segments in the MRI brain image.

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Fig.7: Sobel filter on subtracted image Fig.8: Sobel filter on histogram equalized image
4.7 Morphological operations- Morphological processing deals with the tools for extracting image
components that are useful in the representation and description of shape. Basically, these operations
are the linear operations related to the shape of features in an image. These operations process
images based on shapes by applying a structuring element to the input image, creating an output
image of the same size. In a morphological operation, the value of each pixel in the output image is
based on a comparison of the corresponding pixel in the input image with its neighbors. Some
operations test whether the element “fits” within the neighborhood, while others test whether it “hits”
or intersects the neighborhood. By choosing the shape and size of neighborhood, a morphological
operation can be constructed that is sensitive to specific shapes in the input image. The
morphological operations used in the present paper are dilation (adds pixels to boundaries of objects
in an image) and erosion (removes pixels from object boundaries). Dilation and erosion depends on
size and shape of the structuring element used to process the image.
Fig.9 shows the thickening of boundaries of segments in the image and fig.10 depicts the
extraction of interested tumor and edema boundaries while removing all other unwanted boundaries.
Fig.9: Dilated image Fig: 10 Unwanted boudaries removed
4.8 Image superimposition- The final segmented image is then superimposed on the original image
which clearly distinguish between tumor and edema and the boundaries are detected which becomes
more visible when superimposed on the anatomical structure of brain MRI image.

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Fig.11: Final superimposed image
In this paper an algorithm in MATLAB 7.6.0.324 has been developed to detect the brain
tumor and edema from MRI brain images based on the sobel segmentation method. To summarize
the developed method, the operations are performed on gray scale image of brain and then
enhancement is done followed by segmentation and final superimposition of output image on
original image. The tumor part is then separated from the edema in its vicinity by clear boundaries
between the two which greatly aids the physicians in surgical procedures of tumor excisions.
5. FUTURE WORK
The proposed system can be extended for some other imaging modality like CT, PET-CT,
DTI etc, for different organs of human such as lungs, liver, breast and so on. This proposed work
finds its wide applications in the Medical Imaging Sciences and other related research areas.
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