tumour detection
TRANSCRIPT
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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.
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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
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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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