01-02-2013, 09:45 AM
Brain Tumour Extraction from MRI Images Using MATLAB
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Abstract
Medical image processing is the most challenging
and emerging field now a days. Processing of MRI images is one
of the part of this field. This paper describes the proposed
strategy to detect & extraction of brain tumour from patient’s
MRI scan images of the brain. This method incorporates with
some noise removal functions, segmentation and morphological
operations which are the basic concepts of image processing.
Detection and extraction of tumour from MRI scan images of the
brain is done by using MATLAB software.
INTRODUCTION
Tumour is defined as the abnormal growth of the tissues.
Brain tumor is an abnormal mass of tissue in which cells grow
and multiply uncontrollably, seemingly unchecked by the
mechanisms that control normal cells. Brain tumors can be
primary or metastatic, and either malignant or benign. A
metastatic brain tumor is a cancer that has spread from
elsewhere in the body to the brain
Epilepsy is a brain disorder in which clusters of nerve cells,
or neurons, in the brain sometimes signal abnormally.
Neurons normally generate electrochemical impulses that act
on other neurons, glands, and muscles to produce human
thoughts, feelings, and actions. In epilepsy, the normal pattern
of neuronal activity becomes disturbed, causing strange
sensations, emotions, and behavior or sometimes convulsions,
muscle spasms, and loss of consciousness [3].
Magnetic Resonance Imaging (MRI) is an advanced
medical imaging technique used to produce high quality
images of the parts contained in the human body MRI
imaging is often used when treating brain tumours, ankle, and
foot. From these high-resolution images, we can derive
detailed anatomical information to examine human brain
development and discover abnormalities. Nowadays there are
several methodology for classifying MR images, which are
fuzzy methods, neural networks, atlas methods, knowledge
based techniques, shape methods, variation segmentation.
MRI consists of T1 weighted, T2 weighted and PD (proton
density) weighted images and are processed by a system
which integrates fuzzy based technique with multispectral
analysis [2].
METHODOLOGY
The algorithm has two stages, first is pre-processing of
given MRI image and after that segmentation and then
perform morphological operations. Steps of algorithm are as
following:-
1) Give MRI image of brain as input.
2) Convert it to gray scale image.
3) Apply high pass filter for noise removal.
4) Apply median filter to enhance the quality of image.
5) Compute threshold segmentation.
6) Compute watershed segmentation.
7) Compute morphological operation.
8) Finally output will be a tumour region.
All above steps are explained here in detail.
Grayscale Imaging
MRI images are magnetic resonance images which can be
acquired on computer when a patient is scanned by MRI
machine. We can acquire MRI images of the part of the body
which is under test or desired. Generally when we see MRI
images on computer they looks like black and white images.
In analog practice, gray scale imaging is sometimes called
"black and white," but technically this is a misnomer. In true
black and white, also known as halftone, the only possible
shades are pure black and pure white. The illusion of gray
shading in a halftone image is obtained by rendering the
image as a grid of black dots on a white background (or viceversa),
with the sizes of the individual dots determining the
apparent lightness of the gray in their vicinity.
RESULT AND DISCUSSION
Next figures show the images as an output. i.e grayscale
image, high pass filtered image , threshold image, watershed
segmented image, Finally input image and extracted tumour
from MRI image. For this purpose real time patient data is
taken for analysis.As tumour in MRI image have an intensity
more than that of its background so it become very easy locate
it and extract it from a MRI image.