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BRAIN TUMOUR DETECTION USING BOUNDING BOX SYMMETRY

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CONTENTS

OBJECTIVE

INTRODUCTION

METHODOLOGY

RESULTS

ADVANTAGES

CONCLUSION

FUTURE SCOPE

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OBJECTIVE

To detect the size and location of brain tumors and

edemas from the Magnetic Resonance Images.

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INTRODUCTION

Brain tumor is an abnormal mass of tissue in which

cells grow and multiply uncontrollably seemingly

unchecked by the mechanisms that control normal

cells.This change detection process uses a novel score

function based on Bhattacharya coefficient computed

with gray level intensity histograms. The score function admits a very fast search to

locate the bounding box.

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METHODOLOGY

MRI IMAGE AS INPUT

HPF&MEDIAN FILTERS

SEGMENTATION OF IMAGE

MORPHOLOGICAL OPERATION

TUMOR REGION DETECTED

ALGORITHM:

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D

Locating a Bounding Box:

1.Axis of symmetry on an axial MR slice is found which divides brain in two halves left (I) and right (R).

2. One half serves as test Image and the other half supplies as the reference image.

Image I Reference Image R

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3. Novel score function is used which identify the region of change with two searches – one along the vertical direction and other along the horizontal direction.

4. Novel score function uses Bhattacharya coefficient to detects a rectangle D which represents the region of interest between images I and R

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RESULTS

This method has been tested on 12 brain MRI images. MRI image is taken as input image.

SKULL DTECTED

To extract better results edge detection has been performed.

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SEGMENTATION

• Comparing right and left axis of the brain is done by performing segmentation.

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TUMOUR REGION

• Output image is obtained where the tumour region is highlighted in a bounding box.

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• Maximum size of the tumour detected by bounding box method in pixels-5035

• Minimum size detected-1190

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• This technique has also been applied to detect edema regions

EDGE DETECTION AND SEGMENTATION

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EDEMA REGION

• Size of the edema region in pixels displayed in command window

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ADVANTAGES

1. Uses region-based left-right symmetry, rather than point-wise symmetry

2. Uses single MR image

3. No training data required

4. No image registration needed

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CONCLUSION

•The current method uses a computer aided system for brain MR

image segmentation for detection of tumour location using

bounding box symmetry.

•The resulting method is very fast, robust and reliable for indexing

tumour or edema images for both archival and retrieval purposes

and it can use as a vehicle for further clinical investigations.

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FUTURE SCOPE

•In future, this technique can be developed to classify the tumours based on feature extraction.

•This technique can be applied for ovarian, breast, lung, skin tumours.

•Instead of rectangular boxes, can work with general boundaries: level set based framework.

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