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Analysis of Stroke on Brain Computed Tomography Scans

Adviser:Prof. Jayanthi Sivaswamy

4rd October 2013

Saurabh Sharma200502024

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Outline

Introduction– Problem Description

Part I : Automatic detection of stroke

Part II : Contrast enhancement of stroke tissues

Region basedPixel based

Conclusions Future Directions

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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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• 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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• 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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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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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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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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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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Level 1 Classification

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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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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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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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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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Level 3 Classification

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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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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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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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Qualitative Results

Input Image Preprocessed Final Output Follow – up

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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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Quantitative Results

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Quantitative Results

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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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Manual Windowing

• The process of mapping the 16-bit CT image to the 8-bit display monitors.

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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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Manual Windowing

• Stroke under different window settings.

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Auto Windowing

• We propose two different approaches for auto windowing.Region based Pixel based

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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

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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.Region based Pixel based

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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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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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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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Auto Windowing

• We propose two different approaches for auto windowing.Region based Pixel based

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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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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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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.

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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.

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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.

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Questions?

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Thank You.