Analysis of Stroke on Brain Computed Tomography Scans

1. Analysis of Stroke on Brain Computed Tomography Scans Saurabh Sharma 200502024 Adviser: Prof. Jayanthi Sivaswamy 4rd October 2013

2. Outline • Introduction • Problem Description • Part I : • Automatic detection of stroke • Part II : • Contrast enhancement of stroke tissues • Region based • Pixel based • Conclusions • Future Directions

3. 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.

4. Introduction • Stroke, a.k.acerebrovascular accident is loss of brain function due to disturbance in blood supply. • Stoke can be: Hemorrhagic Ischemic

5. Introduction • 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.

6. 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.

7. 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

8. Problem Statement • To aid in detection of stroke from brain CT scans during all stages of pathology. Hemorrhage Chronic Normal Acute Hyperacute

9. 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.

10. 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.

11. Level 1 Classification

12. 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.

13. Level 2 Classification

14. 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.

15. 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.

16. 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.

17. Level 3 Classification MRF based Tissue Segmentation Skull based Symmetry detection Infarct Decision Candidate Selection Wavelet based Image Enhancement Input CT Image

18. Level 3 Classification Wavelet based Image Enhancement • 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*. *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.

19. Level 3 Classification MRF - MAP based Tissue Segmentation Where, L is a random variable denoting the class and S is the site location (x,y) Assuming I.I.D Gaussian distribution at each location

20. 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.

21. Level 3 Classification Candidate Selection Infarct Decision • Weed out false positives using size and confidence constraints

22. Qualitative Results Pre Processed Input Image Rough Segmentation Final Result

23. Qualitative Results Input Image Preprocessed Final Output Follow – up

24. 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)

25. Quantitative Results

26. Quantitative Results

27. Quantitative Results

28. Failure Cases

29. 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.

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

31. 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.

32. Manual Windowing • Stroke under different window settings.

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

34. 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. Pixel based Region based

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

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

37. 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. Pixel based Region based

38. 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). Pixel based Region 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

39. Auto Windowing • We propose two different approaches for auto windowing. Pixel based Region based Ωland Ωrare the set of pixels in left and right hemispherical image

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

41. 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.

42. 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.

43. 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.

44. 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.

45. 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.

46. Questions?

47. Thank You.