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IIIT H
yderabad
Analysis of Stroke on Brain Computed Tomography Scans
Adviser:Prof. Jayanthi Sivaswamy
4rd October 2013
Saurabh Sharma200502024
IIIT H
yderabad
Outline
Introduction– Problem Description
Part I : Automatic detection of stroke
Part II : Contrast enhancement of stroke tissues
Region basedPixel based
Conclusions Future Directions
IIIT H
yderabad
Introduction
• Stroke, a.k.a cerebrovascular accident is loss of brain function due to disturbance in blood supply.
15 Million people are affected from stroke worldwide.
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yderabad
• Stroke, a.k.a cerebrovascular accident is loss of brain function due to disturbance in blood supply.
• Stoke can be:
Hemorrhagic Ischemic
Introduction
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yderabad
• Both the hemorrhage and ischemic stroke are fatal in nature.
• Complete recovery possible in hemorrhage but less so in case of ischemic stroke
• Most of the damage in case of ischemic stroke occurs within four hours of onset.
• Each hour of untreated stroke ages the brain by ~3.6 years.
Introduction
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yderabad
Treatment
• Hemorrhage and ischemic stroke have conflicting treatments.
• Physiological changes in hemorrhage can be detected much earlier than stroke.
• Lack of tissue information in CT, cannot detect ischemic stroke in most cases before the damage is done.
• The golden rule is first use CT to rule out hemorrhage and then go for MRI to detect ischemic stroke.
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yderabad
Why choose CT?
• CT imaging is relatively quick, provides better spatial resolution
• CT is more widely available than MR scanners in developing countries
• Cost differential between CT and MRI scans
• Moreover, if infarct can be detected at the first scan ( CT ) itself then it would save valuable time
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yderabad
Problem Statement
To aid in detection of stroke from brain CT scans during all stages of pathology.
Hemorrhage Chronic Acute Hyperacute Normal
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Track 1
• Hierarchical symmetry based automatic stroke detection framework.• Stroke is characterized as an aberration in the otherwise symmetrical distribution
of tissues between the left and right hemispheres.
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yderabad
Preprocessing
• Mid-Sagittal plane detection and rotation correction.
• Most of the existing methods used tissue symmetry or center of mass based solutions.
• We devised a novel technique making use of physical structure of the nose to detect the rotation angle.
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yderabad
Level 1 Classification
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yderabad
Level 1 Classification
• Quantize the histograms of both the hemispheres into 5 bins, 0-50, 50 -
100,…,200-250
• Compare the 50-100 and the 200-250 bins from the left and right
hemispheres.
• If the dissimilarity observed is greater than a particular threshold assign
the case to hemorrhage to chronic (50-100) , hemorrhage (200-250) and
normal* (otherwise) bins.
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Level 2 Classification
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yderabad
Level 2 Classification
• Need for a finer symmetry comparison to sort out the acute from the normal + hyperacute cases.
• Wavelet decomposition of the histogram is done and the energy distribution is computed up to 5 levels in scale-space.
• A threshold value, computed empirically, is then used to separate out the acute cases based on the energy values.
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yderabad
Level 3 Classification
• At hyperacute stage, very subtle changes take place in the affected tissues.
• Most of these changes (~2-3 gray scale levels) are very difficult to identify.
• As a result, we turn to some of the specific signs demonstrated by hyperacute infarct.
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yderabad
Level 3 Classification
• The best bet : detect the blurring of gray \ white matter.
• Difficult to achieve in case of CT imaging due to the image quality, noise etc.
• We propose using a rough segmentation of the brain tissues into gray \ white matter to determine the presence of stroke.
Rough segmentation image.
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yderabad
Level 3 Classification
IIIT H
yderabad
Level 3 Classification
*H. Demirel, C. Ozcinar, and G. Anbarjafari. Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE GRS Letters, 7(2):333 –337, april 2010.
• The input CT image is first striped of the skull.
• In the next step, the input image is subjected to SVD based image contrast enhancement technique proposed by Demirel et al*.
Wavelet based Image Enhancement
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yderabad
Level 3 Classification
MRF - MAP based Tissue Segmentation
Assuming I.I.D Gaussian distribution at each location
Where, L is a random variable denoting the class and S is the site location (x,y)
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yderabad
Level 3 Classification
MRF - MAP based Tissue Segmentation
• To obtain the final mappings, we iteratively find the configuration which has the lowest energy.
• The method employed is called Modified Metropolis Dynamics (MMD) as it is generally faster and provides a lower energy output.
M. Berthod, Z. Kato, S. Yu, and J. Zerubia. Bayesian image classification using markov random-fields.Image and Vision Computing, 14(4):285–295,May 1996.
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Level 3 Classification
Candidate Selection
Infarct Decision
• Weed out false positives using size and confidence constraints
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Qualitative Results
Input Image Pre Processed Rough Segmentation
Final Result
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yderabad
Qualitative Results
Input Image Preprocessed Final Output Follow – up
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yderabad
Quantitative Results
Dataset Details.•The dataset contains 42 volume CT scans.•Out of 42, we have 19 normal, 5 hemorrhagic and 6 each of chronic, acute and hyperacute.•In addition, we have the follow up scans of the hyperacute cases.•For robust testing, the test data was collected from a wide range of age groups. (7, 15, 20 datasets in age groups 0-30, 30-50, 50 and above respectively)
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yderabad
Quantitative Results
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yderabad
Quantitative Results
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yderabad
Quantitative Results
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Failure Cases
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Track 2
Enhancement of Early Infarct through Auto-Windowing
• Early automatic detection difficult.
• Current detection process used by doctors.
• Issues with existing tissue contrast enhancement techniques.
• Propose a novel auto-windowing technique which aims at finding the windowing setting which maximizes the contrast between the normal and stroke affected tissues.
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yderabad
Manual Windowing
• The process of mapping the 16-bit CT image to the 8-bit display monitors.
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yderabad
Manual Windowing
• The process of mapping the 16-bit CT image to the 8-bit display monitors.
• Can bring about either contrast stretching or compression.
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yderabad
Manual Windowing
• Stroke under different window settings.
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yderabad
Auto Windowing
• We propose two different approaches for auto windowing.Region based Pixel based
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yderabad
Auto Windowing
• We propose two different approaches for auto windowing.
• Use the automatic detection of Track 1 to identify the window settings.
• Plot the histograms of the stroke affected tissues and their counter-parts in the other hemisphere.
• Find the gray scale value which best separates the two histograms and use this as the window center.
• Now choose any window width based on how much tissue information is required.
Region based Pixel based
IIIT H
yderabad
Auto Windowing
• We propose two different approaches for auto windowing.Region based Pixel based
IIIT H
yderabad
Auto Windowing
• We propose two different approaches for auto windowing.Region based Pixel based
IIIT H
yderabad
Auto Windowing
• We propose two different approaches for auto windowing.
• Inspired by binary thresholding mechanism• The optimum window setting is defined as one
which maximizes the difference in distribution of pixels in the left and right hemispheres.
• Operation is carried out on two separate images, left and right hemisphere, unlike one in case of thresholding.
• Several techniques exist but difficult to model two image problem using those techniques.
Region based Pixel based
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yderabad
Auto Windowing
• We propose two different approaches for auto windowing.
• We modeled our two-image thresholding on the parzen window based thresholding proposed by wang et al.
• Parzen window is a technique to estimate the probability density P(x, y) at a point (x, y).
Region based Pixel based
S.Wang, F. lai Chung, and F. Xiong. A novel image thresholding method based on parzen window estimate.Pattern Recognition, 41(1):117 – 129, 2008
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yderabad
Auto Windowing
• We propose two different approaches for auto windowing.Region based Pixel based
Ωl and Ωr are the set of pixels in left and right hemispherical image
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yderabad
Auto Windowing
• We propose two different approaches for auto windowing.Region based Pixel based
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yderabad
Qualitative Results
Experiment Details•A set of 15 slices each of hyperacute and normal cases were selected•The slices were shown to the radiologists under normal, region-based (Wr) and pixel-based (Wp) automated window settings.•Each slice by rated by 4 radiologists, of varied experience, in a blinded review for the presence of hyperacute infarct.•Their response and the time taken for decision was recorded.
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yderabad
Qualitative Results
•Average sensitivity increased from 59.95% (Ws) to 79.97% (Wr) and 84.97% (Wp). (P = 0.034 for Wp, P = 0.040 for Wr)
•Average specificity increased from 83.3% (Ws) to 98.34% (Wr) and 98.34 % (Wp). (P = 0.032 for Wr)
•Overall accuracy of the radiologists increased from 71% (Ws) to 91.6% (Wp, p = 0.024) and 89.16% (Wr, p = 0.034)•The performance of younger radiologists show much more improvement though still not statistically significant.
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yderabad
Summary
• Presented an unified hierarchical approach for automatic detection and classification of stroke.
• Our approach models the stroke as a disturbance in the otherwise similar distribution of brain tissue with respect to the mid-sagittal plane
• The method gives very good recall and sensitivity on hemorrhage, chronic and acute stroke and appreciable performance on hyperacute or early infarct.
• The hyperacute infarct detection can be used to aid the radiologists in clinical environment.
IIIT H
yderabad
Summary
• We also presented an auto-windowing approach to aid the radiologists in detection of early infarct.
• The perception experiment results show that auto-windowing approach could be applied in clinical settings.
• The method also hinted at bridging the experience divide by bringing the accuracy of inexperienced radiologists to a very good level.
IIIT H
yderabad
Future Directions
• Application to similar problems where early detection of diseases is difficult.
• One such case is the early detection of brain tumors.
• Need to test on a larger dataset.
IIIT H
yderabad
Questions?
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yderabad
Thank You.