1. Detection and Assessment of Abnormality in Medical Images MS Thesis Presentation Candidate: K Sai Deepak Adviser: Prof. Jayanthi Sivaswamy Center for Visual Information Technology IIIT Hyderabad India 31-March-2012

2. Agenda Computer Aided Diagnosis – Modes of Healthcare – CAD in Primary Care (examples) Disease Screening – CAD in Disease Screening – Challenges for existing CAD Proposed Methodology – Detecting Abnormality Instead of Disease – Detection of Lesions using Motion Patterns Detection and Assessment of Retinopathy – Diabetic Macular Edema – Method – Experiments and Results – Detection of Multiple Lesions Classification of Lesions in Mammograms – Mammographic Lesions – Experiments and Results Computer Aided Diagnosis Disease Screening Proposed Methodology -Showcase 1- Retinopathy -Showcase 1- Retinopathy -Showcase 2- Breast Cancer -Showcase 2- Breast Cancer Source of all the figures are explicitly mentioned in the MS Thesis

3. PART I – Computer Aided Diagnosis Computer Aided Diagnosis Disease Screening Proposed Methodology -Showcase 1- Retinopathy -Showcase 1- Retinopathy -Showcase 2- Breast Cancer -Showcase 2- Breast Cancer

4. Computer Aided Diagnosis (CAD) Aid of computers in the process of diagnosis Computer aided diagnosis (CAD) has become one of the major support systems assisting medical experts in diagnosis through images CAD tools are used for measurement, display and analysis of both the structural and functional aspects of the body from images Computer Aided Diagnosis

5. CAD with Images Computer Aided Diagnosis Visualization – enhancement for visual analysis (Ex. Windowing, MIP, MAP, AIP, Zoom, Contrast Inversion etc.) Detection – detect the presence of disease manifestation Localization and Segmentation – Localize or segment the spatial regions containing disease manifestation Other utilities can be used for measurement of various structures from images (length, volume etc. )

6. Healthcare – Primary Care and Disease Screening Computer Aided Diagnosis Secondary and Tertiary Care Centers – are where patients usually visit on referral for advanced care Point of Consultation in basic healthcare Patients with Symptoms arrive Undergo specialized tests if required for Diagnosis Treatment is planned based on Diagnosis Performed on Public health initiative Most patients have no disease symptoms Detection is performed by a trained professional Referred to expert on positive detection

7. CAD in Primary Care Computer Aided Diagnosis Traditionally CAD has been used in Primary Care

8. CAD in Primary Care Patient visits the doctor with a complaint If required, the patient is then referred by the doctor for specific imaging in order to diagnose the problem Acquired images are analyzed by the experts (Ophthalmologist, Radiologist) to arrive at a diagnosis The diagnosis report is used by doctor for planning treatment Computer Aided Diagnosis

9. PART II – Disease Screening Computer Aided Diagnosis Disease Screening Proposed Methodology -Showcase 1- Retinopathy -Showcase 1- Retinopathy -Showcase 2- Breast Cancer -Showcase 2- Breast Cancer

10. Disease Screening Disease screening is performed at specific community healthcare centers to prevent ensuing mortality and suffering from chronic ailments Challenges: Geographical reach, Disease awareness and Social barriers and Availability of experts are common in screening Tele-radiology provides significant help but the work load of a medical expert increases significantly due to large number of patients participating in population screening Diabetic Retinopathy and Breast Cancer screening are already conducted or being adopted in several countries and is the focus of this work Diabetic RetinopathyBreast Cancer Disease Screening

11. CAD in Disease Screening Existing CAD tools use a disease centric approach for disease detection It requires application of several methods/tools for detecting all the possible lesions in a disease – Multiple CAD tools are used for identifying different Diabetic Retinopathy (DR) manifestations Multiple CAD tools Existing CAD systems are not able to meet the needs of disease screening in Diabetic Retinopathy [1] – Poor sensitivity of disease detection – Large number of normal patients are detected as abnormal Disease Screening [1] M. D. Abramoff, M. Niemeijer, M. S. Suttorp-Schulten, M. A. Viergever, S. R. Russell, and B. van Ginneken. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Journal of Diabetes Care, 31:193– 198, 2007.

12. Summary of Challenges Existing CAD tools use a disease centric approach for detection and segmentation of disease – In Screening most of the patients are normal (80-90% for DR & BC) Multiple tools result in cascading effect of detected FPs Doctors spend a lot of time in rejecting normal patients – Other challenges in disease centric approach Illumination and Contrast Illumination and Contrast Tissue Pigmentation Tissue Pigmentation A disease centric CAD system has to robustly learn all possible manifestations of a disease which is challenging Patients with diseases outside the purview of screening are ignored – referral could be useful for a patient suffering non DR disease detected in DR screening Disease Screening

13. Other Challenges – Disease Vs Normal Background Disease Screening

14. PART III – Proposed Methodology Computer Aided Diagnosis Disease Screening Proposed Methodology -Showcase 1- Retinopathy -Showcase 1- Retinopathy -Showcase 2- Breast Cancer -Showcase 2- Breast Cancer

15. Non conformance to expected behaviour (normal) in the data is considered as abnormality Features of normal medical images can be used to model expected normal behaviour Abnormality detection is relevant in disease screening where detecting the presence of abnormality is of initial interest: – Retinal image screening for detecting Diabetic Retinopathy – Mammographic screening for detecting malignancy of lesions Normal CFIAbnormal CFI with lesions Proposed Methodology Detecting Abnormality instead of Disease X Y Normal Abnormal Feature Space Abnormal

16. Two Stage Methodology for CAD Proposed Methodology Stage 1- Detection of abnormality – Derive motion pattern for detection of lesions – Extract relevant features to represent normal sub-space – Detect outliers as abnormal Stage2-Assessment of abnormality – Derive relevant features based on domain knowledge from abnormal cases – Determine the severity of disease

17. Two Stage Methodology for CAD Proposed Methodology Stage 1- Detection of abnormality – Only normal cases are required for disease detection – Variations observed in the normal cases are captured by the normal feature sub-space – Single point of control on the permitted figure of false alarms Stage2-Assessment of abnormality – Fewer normal cases to be examined by experts

18. Motivation - Effect of motion on human visual system and detectors in camera – Spatial/temporal averaging of intensities in retina – Smearing of intensities corresponding to moving object is observed in images acquired with camera Inducing motion in images – Lesions can be observed as a set of localized pixels with contrast against background – A smear of pixel along the direction of motion can be observed in motion pattern – Spread and extent of lesions in motion pattern depends on the sampling rate at each location and duration of motion – Contrast of the spatially enhanced lesions in motion pattern relies on the coalescing function Motion pattern on Background – Uniformity in motion pattern for textured background can be observed Original Image (Uniform Background) Rotational Motion Pattern Motion Pattern – Detecting Localized Lesions Proposed Methodology Original Image (Textured Background) Rotational Motion Pattern

19. PART IV – Detection and Assessment of Macular Edema Computer Aided Diagnosis Disease Screening Proposed Methodology -Showcase 1- Retinopathy -Showcase 1- Retinopathy -Showcase 2- Breast Cancer -Showcase 2- Breast Cancer

20. Macular Edema Detection and Assessment Diabetic Macular Edema (DME) is a sight threatening condition that occurs due to diabetic retinopathy DME requires immediate referral to Ophthalmologists Presence of Hard Exudates is used as an indicator of DME during retinal disease screening Severe and moderate cases of DME Color Retinal Image -Showcase 1- Retinopathy -Showcase 1- Retinopathy

21. Existing Approaches in DME Detection Several local and global schemes have been proposed for DME detection Local Schemes – local schemes try to successfully segment or localize the exudate clusters – Techniques including adaptive intensity thresholding, background suppression (median filtering, morphology), color and edge detection have been proposed – several normal pixels are also detected as candidates in normal images increasing the number of false alarms in the system Global Schemes – global schemes try to ensure that at least the brightest pixels corresponding to HE in the image are detected – Techniques based on intensity thresholding, edge strength, and visual words using features on SIFT keypoints have been used to classify images -Showcase 1- Retinopathy -Showcase 1- Retinopathy

22. Proposed Workflow Steps Landmark Detection and Region of Interest Extraction Generation of Motion Patterns Feature Selection Abnormality Detection Abnormality Assessment -Showcase 1- Retinopathy -Showcase 1- Retinopathy

23. Detection of Landmarks in CFI -Showcase 1- Retinopathy -Showcase 1- Retinopathy Singh, J. and Joshi, G. D. and Sivaswamy, J. Appearance-based object detection in colour retinal images. In ICIP, pages 1432–1435, 2008. G. D. Joshi and J. Sivaswamy and K Karan and S. R. Krishnadas. Optic disk and cup boundary detection using regional information. ISBI, pp. 948–951, 2010.

24. Selection of ROI ROI around center of macula -Showcase 1- Retinopathy -Showcase 1- Retinopathy

25. Motion Pattern – Rotational Motion Effect of sampling rate on motion pattern (decreasing rotation steps)- Coalescing Function Mean - Arithmetic mean of all samples were taken Extrema – Maximum or Minimum of all samples are taken at each pixel location -Showcase 1- Retinopathy -Showcase 1- Retinopathy

26. Selection of Motion Pattern “effect of abnormality (lesion) on retinal background can be observed as change in local information with respect to the motion pattern of normal retina” -Showcase 1- Retinopathy -Showcase 1- Retinopathy normalabnormal A normal retinal image was created by averaging the green channel of 400 retinal images The abnormal retina is modeled by adding a bright lesion to emulate HE - motion pattern- Gradient magnitude of motion pattern- Shannon’s entropy

27. Selection of Parameters – Class Discriminability -Showcase 1- Retinopathy -Showcase 1- Retinopathy Size of normal retina – 150*150 Neighborhood size – 7*7

28. Motion Pattern for Edema Detection A circular ROI is determined around macula and the Optic disc is masked to avoid false positives Rotational motion is induced in the green channel image Maxima is used as the coalescing function Features derived on motion pattern are used for learning the normal sub- space and detecting abnormality Sample ROI and Motion Pattern (S- Subtle Hard Exudates) -Showcase 1- Retinopathy -Showcase 1- Retinopathy

29. More Motion Patterns Sample ROIs and Motion Pattern (S- Subtle Hard Exudates) Normal ROI Abnormal ROI -Showcase 1- Retinopathy -Showcase 1- Retinopathy

30. Feature Extraction -Showcase 1- Retinopathy -Showcase 1- Retinopathy To effectively describe motion pattern, we use a descriptor derived from the Radon space The desired feature vector is obtained by concatenating 6 projections (0-180 degrees) Each projection has 6 bins resulting in a feature vector of length 36 Integral of motion pattern along a line

31. Abnormality Detection PCA Data Description The eigenvectors corresponding to the covariance matrix of the training set is used to describe the normal subspace Feature vector for a new case is projected to this subspace (first 6 eigen vectors) Residual e is defined as, Classification between normal and abnormal cases is then performed using an empirically determined threshold on e -Showcase 1- Retinopathy -Showcase 1- Retinopathy

32. Detection Performance (ROC Curves) DMED - 122 images o Normal - 68 o Abnormal – 54 o Normal images used for training - 18 MESSIDOR – 400 images o Normal - 274 o Abnormal – 126 o Immediate referral - 85 o Normal images used for training – 74 Diaretdb0 & db1 – 122 images o Normal – 25 o Abnormal - 97 Combined Dataset – 644 images o Normal – 367 o Abnormal - 277 DMED MESSIDOR -Showcase 1- Retinopathy -Showcase 1- Retinopathy Receiver Operating Characteristic curve

33. Comparison against Disease Centric Methods DMED Normal - 68 Abnormal – 54 Normal images used for training - 18 -Showcase 1- Retinopathy -Showcase 1- Retinopathy MESSIDOR Normal - 274 Abnormal – 126 Normal images used for training – 74 [23] L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y. Li, K. W. Tobin Jr, and E. Chaum. Automatic retina exudates segmentation without a manually labelled training set. IEEE ISBI, pages 1396 – 1400, April 2011. [2] C. Agurto, V. Murray, E. Barriga, S. Murillo, M. Pattichis, H. Davis, S. Russell, M. Abramoff, and P. Soliz. Multiscale am-fm methods for diabetic retinopathy lesion detection. IEEE TMI, 29(2):502 –512, feb. 2010.

34. Detection of subtle hard exudates -Showcase 1- Retinopathy -Showcase 1- Retinopathy

35. Assessment of Severity Macula is devoid of significant vasculature It is characterized by rough rotationally symmetry -Showcase 1- Retinopathy -Showcase 1- Retinopathy - Abnormal image - Symmetry measure on abnormal macula and are the minimum and maximum symmetry values for normal cases

36. Assessment of Severity -Showcase 1- Retinopathy -Showcase 1- Retinopathy Dataset: MESSIDOR The threshold is expressed as a percentage (p) of the symmetry measure S of normal ROIs used in the abnormality detection task

37. Detection of Multiple Abnormalities -Showcase 1- Retinopathy -Showcase 1- Retinopathy Normal Cases - 362 Abnormal Cases - 302 Dataset: DMED,MESSIDOR and Diaretdb0 Abnormalities: Hemorrhage, Hard Exudates, Drusen

38. PART V – Classification of Lesions in Mammograms Computer Aided Diagnosis Disease Screening Proposed Methodology -Showcase 1- Retinopathy -Showcase 1- Retinopathy -Showcase 2- Breast Cancer -Showcase 2- Breast Cancer

39. Assessment of Mammographic Lesions Breast cancer is responsible for about 30 percent of all new cancer cases with a high mortality rate in women Screening for its early detection with mammograms has been explored for more than 3 decades now with moderate success Correct classification of anomalous areas in the mammograms through visual examination is challenging even for experts Sample Benign and Malignant lesions in Mammograms -Showcase 2- Breast Cancer -Showcase 2- Breast Cancer

40. Existing Approaches in Mammogram Analysis 1- Lesions are first detected from mammograms 2- Malignancy of detected lesions are identified using several texture and shape features Typical features used – size – shape – density – Smoothness of borders – Brightness and contrast – local intensity distribution The feature space is very large and complex due to the wide diversity of the normal tissues and the variety of the abnormalities -Showcase 2- Breast Cancer -Showcase 2- Breast Cancer

41. Classification of Mammographic Lesions Given a lesion, its malignancy is of question Features derived over motion pattern is used for learning the behavior of benign class Any deviation in lesion property is identified as a sign of malignancy Benign lesions Malignant lesions -Showcase 2- Breast Cancer -Showcase 2- Breast Cancer

42. Motion Pattern – Class Discriminability Three sample benign and malignant lesions were selected Motion pattern was applied using rotation and translation to analyze class discriminability between benign and malignant class Maximum and Mean are the coalescing functions used -Showcase 2- Breast Cancer -Showcase 2- Breast Cancer

43. Classification Performance (ROC Curve) An evaluation of the proposed scheme for learning normal subspace was conducted using KNN classifier The value of K was considered as 3 for computing the sensitivity and specificity values in the classification tasks An ROC curve is drawn by varying the normalized Euclidean distance from [0-1] Mini-MIAS Benign - 68 Malignant – 51 Benign lesions for training - 20 -Showcase 2- Breast Cancer -Showcase 2- Breast Cancer

44. We identified and listed the challenges in image based disease screening for diabetic retinopathy and breast cancer We proposed and evaluated a method for abnormality detection and assessment – a hierarchical approach to the problem of abnormality detection Evaluation of the proposed hierarchical approach has been performed – on several publicly image datasets of CFI and mammograms – improvement in the disease detection performance over methods in literature Conclusion

45. Acknowledgement This work is dedicated to my Parents and Teachers Extremely grateful to Prof. Jayanthi Sivaswamy for giving me the opportunity to pursue MS by research Thankful to all lab mates in CVIT for their support Guidance of Gopal and Mayank was extremely valuable Debates and discussion with Sandeep, Kartheek and Saurabh were always insightful

46. Publications 1. Patents (a) Jayanthi Sivaswamy, N V Kartheek Medathati, K Sai Deepak, A System for generating Generalized Moment Patterns, Submitted to Indian Patent Office, 2010 (Application Number 3939-CHE-2010) 2. Papers Conference (a) K Sai Deepak, Gopal Datt Joshi, Jayanthi Sivaswamy, Content-Based Retrieval of Retinal Images for Maculopathy, ACM International Health Informatics Symposium, November, 2010 Journal (a) K Sai Deepak, N V Kartheek Medathati and Jayanthi Sivaswamy, Detection and Discrimination of disease related abnormalities, Elsevier Pattern Recognition 2011 (In Press) (b) K Sai Deepak, Jayanthi Sivaswamy, Automatic Assessment of Macular Edema from Color Retinal Images, IEEE Transactions on Medical Imaging 2011

47. Supplementary Slides

48. Imaging Modalities Computer Aided Diagnosis Computer Aided Diagnosis Optical Imaging - Ophthalmology X-ray Imaging - Mammography High resolution optical camera Pupil may be dilated before imaging Pixel resolutions typically range from 0.5K to ~2K*2K Radiometric resolution is typically 8 bits per channel Low energy X-ray scanner Displays change of density among tissues Pixel resolutions can range from 1K 2 to 3K 2 Radiometric resolution 8-12 bits

49. CAD in Disease Screening – Diabetic Retinopathy Disease Screening Hemorrhage Detection Exudate Detection Neovascularization Detection Microaneurysms Detection FP1 FP2 FP3 FP4 Maximum False alarms in disease centric approach – FP1 + FP2 + FP3 + FP4

50. CAD – Retinopathy (Color Fundus Image) Disease Screening

51. CAD – Breast Lesions (Mammograms) Benign Lesion Malignant Lesion Disease Screening

52. Illumination and Contrast Disease Screening Presence of one or more of additive bias, multiplicative bias and difference in brightness These variations often increases the complexity of modeling the normal background especially when there can be several other structures present in the normal image

53. Tissue Variation (Pigmentation & Density) Disease Screening Tissue characteristics for the same structure can vary across race and often across patients, within a race. This variation manifests as differences in intensity, hue and/or pigmentation These variations can be significant enough for an automated disease detection technique to classify an image as abnormal

54. CAD with Images - Visualization Computer Aided Diagnosis MAP of Sagittal view Bones appear bright in X-ray 52 year old Patient with Back Pain Windowing Tissues of varying densities can be examined

55. CAD with Images - Detection Computer Aided Diagnosis Normal RetinaAbnormal Retina

56. CAD with Images – Segmentation Computer Aided Diagnosis Original Image Vessels Segmented

57. Feature Extraction -Showcase 1- Retinopathy -Showcase 1- Retinopathy To effectively describe motion pattern, we use a descriptor derived from the Radon space - is the integral of motion pattern along a line oriented at and distance from the origin The desired feature vector is obtained by concatenating projections from each bin at different orientations

58. PCA DD -Showcase 1- Retinopathy -Showcase 1- Retinopathy W d*k is a matrix of first k eigen vectors X proj = W(W T W) -1 WX Vector X is projected on the new sub-space Re-construction error e(X) is computed as, e(X) = || X - X proj || 2