1. IIIT Hyderabad A Hierarchical System Design for detection of Glaucoma from Color Fundus Images Madhulika Jain, Jayanthi Sivaswamy CVIT, IIIT Hyderabad, India

2. IIIT Hyderabad What is glaucoma? Glaucoma is an eye disorder that causes irreversible loss of vision It affects the Optic Nerve in retina

3. IIIT Hyderabad Why glaucoma detection needed? –Leading cause of blindness worldwide In India – 2 nd leading cause of blindness It is estimated to affect 79 million people in the world by the year 2020 –Prevalent in aging population  India alone hosts 20% of glaucoma cases –Irreversible loss of vision Thus timely detection required –lack of manpower in terms of skilled technicians Thus computer aided solutions for screening

4. IIIT Hyderabad Color Fundus Images It provides 2-D projection of retina –structural information of optic disc(OD) –Information regarding retinal structures such as cup, rim and blood vessels In a retinal image, the region of interest is the Optic Disk (OD) Disk – is marked by the outer boundary of OD (white) Cup – is marked by the inner boundary of OD (black) Glaucoma manifests primarily as structural deformations in OD

5. IIIT Hyderabad Why color fundus images are used? low cost non-invasiveness ease of use higher penetrability to every section of society

6. IIIT Hyderabad Visual Symptoms of Glaucoma Global Rim Thinning – is caused by enlargement of cup with respect to optic disk (arrow) Peripapillary Atrophy (PPA) – is atrophy of retinal cells around optic disk (yellow) – a change in intensity is observed adjoining the disk boundary Retinal Nerve Fiber Layer (RNFL) Defect – occurs due to the loss of the respective layer in retina (green) – most subtle indicator of glaucoma RIM THINNING PERIPAPILLARY ATROPHY RNFL DEFECT Local

7. IIIT Hyderabad More Examples of Atrophy More Examples of rim thinning

8. IIIT Hyderabad Glaucoma Detection – Background Local Approaches Aim at measuring the cup to disc ratio after segmenting the cup and disk regions  Joshi et al. (2011)  Chan Vese model (CV model) with no shape constraints to segment disk  R-bends (relevant bends) and pallor information for cup segmentation  Liu et al. (2009)  level set method followed by ellipse fitting for disk segmentation  level set based cup region segmentation followed by ellipse fitting  Tao et al. (2013)  Superpixel based classification for cup segmentation + Morphological changes (rim thinning) are captured well, provided segmentation is accurate − Accurate identification of these ill-defined boundaries is a difficult task Global Approaches + Need to accurately identify boundaries is eliminated − Difficult to achieve robustness to significant intra-class variations Aim at deriving global image features  Bock et al. (2007) compare and select from  pixel intensity values  texture using Gabor filters  spectral features - FFT coefficients  histogram model  Bock et al. (2010)  pixel intensity values, FFT and B-spline coefficients to derive probabilistic output  Meier et al. (2010)  uses same features as [Bock 2007]  additional pre-processing to remove disease independent variations  Annu et al. (2013)  Uses texture features

9. IIIT Hyderabad Glaucoma Detection – Background Local Approaches Aim at measuring the cup to disc ratio after segmenting the cup and disk regions  Joshi et al. (2011)  Chan Vese model (CV model) with no shape constraints to segment disk  R-bends (relevant bends) and pallor information for cup segmentation  Liu et al. (2009)  level set method followed by ellipse fitting for disk segmentation  level set based cup region segmentation followed by ellipse fitting  Tao et al. (2013)  Superpixel based classification for cup segmentation + Morphological changes (rim thinning) are captured well, provided segmentation is accurate − Accurate identification of these ill-defined boundaries is a difficult task Global Approaches + Need to accurately identify boundaries is eliminated − Difficult to achieve robustness to significant intra-class variations Aim at deriving global image features  Bock et al. (2007) compare and select from  pixel intensity values  texture using Gabor filters  spectral features - FFT coefficients  histogram model  Bock et al. (2010)  pixel intensity values, FFT and B-spline coefficients to derive probabilistic output  Meier et al. (2010)  uses same features as [Bock 2007]  additional pre-processing to remove disease independent variations  Annu et al. (2013)  Uses texture features

10. IIIT Hyderabad Proposals Exploring global motion pattern based features for glaucoma detection Hierarchical system design using global analysis only Hierarchical system design using both global and local analysis Extending this global approach to the detection of atrophy

11. IIIT Hyderabad Review of the GMP representation Original Image On rotation denotes a location in the 2-D ROI is a rigid transformation applied to generate a sequence of transformed images is the coalescing function Result Earlier used for detecting bright lesions present in diabetic macular edema by Deepak et al. [6] [7]

12. IIIT Hyderabad GMP for Glaucoma Detection Challenges capturing subtle structural deformation relative contrast of cup and disc not consistent Strategy Coalescing function  Pixelwise maximum Exploring different motion variable  Pivot of motion  Type of motion  Extent of motion  Step size of motion Translation Rotation Rotation + Translation

13. IIIT Hyderabad Translation motion based GMP Aim :- To capture deformation in superior-inferior and nasal-temporal direction (primary interest)

14. IIIT Hyderabad Translation motion based GMP

15. IIIT Hyderabad Translation motion based GMP

16. IIIT Hyderabad Translation motion based GMP Projection profile ; : projection of GMP in direction Variation in profile Shape of profile

17. IIIT Hyderabad Rotation motion based GMP Aim :- To accentuate the subtle deformation (local notching) and the presence of atrophy

18. IIIT Hyderabad Rotation motion based GMP GMP is obtained using following equation For every pivot  one GMP image For each GMP  feature extracted – histogram after it is rebinned to 7 bins (figure in next slide)

19. IIIT Hyderabad Fig :- Extracted feature fr for the normal(blue) and glaucomatous(red) case

20. IIIT Hyderabad Combination of translation and rotation based GMP Aim :- To capture variation of neuroretinal rim thickness along optical disc in all directions

21. IIIT Hyderabad Combination of translation and rotation based GMP

22. IIIT Hyderabad Classification

23. IIIT Hyderabad Experiments and Results Dataset details Total test set -1845 images :1272 normal & 573 glaucomatous cases Training images – 800 images : 400 normal & 400 glaucomatous cases Groundtruth : opinion of 3 glaucoma experts as normal, suspect and confirm case (gold standard – majority) Size of original image = 1494 * 1996 ROI = 401*401 (all processing with a circular mask of radius 200 pixels) Evaluation Scheme using ROC curve -- area under ROC curve

24. IIIT Hyderabad Results For analyzing contribution of each features, variants are generated combining features one by one. Table of results for each variant : ROC plot for stage -1

25. IIIT Hyderabad Proposals Exploring global motion pattern based features for glaucoma detection Hierarchical system design using global analysis only Hierarchical system design using both global and local analysis Extending this global approach to the detection of atrophy

26. IIIT Hyderabad Atrophy detection - Background Kolar et al. : Using active contours in Heidelberg retinal angiography images Joshi et al. (2011) : Using dissimilarity measure of regions adjoining disc Cheng et al. : Using biologically inspired feature Muramatsu et al. : Using texture analysis (only on 26 images ) Previous work largely based on local feature based detection Our strategy :- GMP based global features approach

27. IIIT Hyderabad GMP based features for atrophy Aim :- To capture perceived intensity difference in the regions adjoining optic disc. Divide images into 18 patches of 20 o each For each patch, GMP is computed as follows: Feature computed on annular sector of width 40px

28. IIIT Hyderabad Dataset Details Results Training images = 55 990 patches Testing images = 59 1062 patches Classifier Used Ensemble of decision trees SensitivitySpecificityAccuracy Joshi et al. (2011) 0.820.720.78 Proposed0.8120.570.687 Performance Figures

29. IIIT Hyderabad Proposals Exploring global motion pattern based features for glaucoma detection Hierarchical system design using global analysis only Hierarchical system design using both global and local analysis Extending this global approach to the detection of atrophy

30. IIIT Hyderabad Hierarchical system Aim :- To analyze if cascaded system helps in improving detection performance using global features in both stages Proposed PPA System (threshold at previous stage taken so as to have lesser false negatives)

31. IIIT Hyderabad Results Dataset details Stage 1 – same as earlier Stage 2 – Training images = 114 images Test images = left from 1 st stage (1040) Evaluation Scheme Sensitivity Specificity Accuracy SensitivitySpecificityAccuracy Proposed PPA0.8120.570.687 Cascade0.8170.650.73 Performance Figures help us to remove 10% more images

32. IIIT Hyderabad Proposals Exploring global motion pattern based features for glaucoma detection Hierarchical system design using global analysis only Hierarchical system design using both global and local analysis Extending this global approach to the detection of atrophy

33. IIIT Hyderabad Hierarchical System This uses Stage 1 same as used in previous system ( global feature based) –Aim – to remove as many as true negatives –Enables tuning second stage for higher specificity without compromising sensitivity Stage 2 is borrowed from a work within our group (local feature based) –A clinical paper of this work is under review –Explained next So far no attempts to assess glaucoma using capabilities of both

34. IIIT Hyderabad Stage - 2 Local features used in this stage are :- –Cup-to-disk vertical diameter ratio (CDR) –Cup-to-disk area ratio (CAR) –Atrophy presence decision in the inferior and superior directions –RNFL presence decision in the inferior and superior directions –Relative distributions of image structures using structural clustering

35. IIIT Hyderabad Final System

36. IIIT Hyderabad Experiments & Results Two experiments are done –Using same training set of 800 images (as used earlier) in both the stages (Expt 1) –Using the left over images from stage 1 to train the stage 2 (Expt 2) 1040 (from stage 1) Dataset details –In Expt 1 – testing set similar as used before –In Expt 2 – 800 new images (added to 1040)  652 normal and 148 glaucomatous Evaluation scheme –AUC –Sensitivity –Specificity

37. IIIT Hyderabad Result of Expt 1 Comparison of Stage 2 and combined system (below) SensitivitySpecificityAUC Tao et al.0.650.440.558 Bock et al. (2010)0.700.460.615 Stage-2 CDR0.700.480.636 Stage-2 multifactorial0.790.620.8012 Hierarchical System0.81280.76580.8353 Comparison with state of the art methods (table)

38. IIIT Hyderabad Result of Expt 2 The dataset details (given earlier) Comparison of whole system with and without retraining the second system using leftover images (table below) SensitivitySpecificityAUC Hierarchical System 0.8010.7340.8123 Hierarchical System (retrained) 0.85390.79350.844

39. IIIT Hyderabad Conclusion  A global feature based approach for glaucoma detection from retinal images was proposed  The Generalized Moment Pattern representation is extended for detecting structural distortions in Optic Disk  Hierarchical design (using both global and local analysis) posited to be best to avoid a trade off between sensitivity and specificity  Evaluation of glaucoma detection is performed on a large retinal image dataset

40. IIIT Hyderabad Note Computing cost may be an issue but alternative ways can be explored for that (especially for Stage-2) Given the variabilities that occur in screening scenarios, the parameters may need to be retrained for desired results

41. IIIT Hyderabad Possible Future Directions Exploring more efficient global feature for RNFL detection. –Performance presented in literature not great –Due to subtleness –Adding this as new stage or in the stage-1 of the proposed system will boost performance Automatic detection of parameters of GMP –will be a major step forward in generalization of detection system using GMP –Can be trained automatically as and when new data arrives Optimizing the computational cost.

42. IIIT Hyderabad References [1] G. D. Joshi, J. Sivaswamy, and S. R. Krishnadas. Optic disk and cup segmentation from monocular colour retinal images for glaucoma assessment. IEEE Trans on Medical Imaging, 30(6):1192-1205,2011. [2] J. Liu, D. Wong, J. Lim, H. Li, N. Tan, and T. Wong. Argali- an automatic cup-to-disc ratio measurement system for glaucoma detection and analysis framework. In Proc. SPIE, Medical Imaging, pages 72 603k- 8, 2009. [3] R. Bock, J. Meier, G. Michelson, L. Nyul, and J. Hornegger. Classifying glaucoma with image-based features from fundus photographs. Proc. DAGM, pages 355-364, 2007. [4] R. Bock, J. Meier, L. Nyul, and G. Michelson. Glaucoma risk index: automated glaucoma detection from color fundus images. Medical Image Analysis, 14(3):471-481, 2010. [5] J. Meier, R. Bock, G. Michelson, L. Nyul, and J. Hornegger. Effects of preprocessing eye fundus images on appearance based glaucoma classification. Proc. CAIP, pages 165-172, 2007. [6] K. S. Deepak, N. K. Medathati, and J. Sivaswamy. Detection and discrimination of disease-related abnormalities based on learning normal cases. Pattern Recogn., 45(10):3707-3716, Oct. 2012. [7] K. S. Deepak and J. Sivaswamy. Automatic assessment of macular edema from color retinal images. Medical Imaging, IEEE Trans on, 31(3):766 -776, march 2012. [8] D. Tao, F. Yin, D. Kee, Y. Xu, T. Yin, J. Cheng, and J. Liu. Superpixel classification based optic cup segmentation. Medical Image Computing and Computer-assisted Intervention (MICCAI), 8151:421–428, 2013. [9] N. Annu and J. Justin. Automated classification of glaucoma images by wavelet energy features. International Journal of Engineering and Technology, 2013. [10] R. Kolar, J. Jan, R. Laemmer, and R. Jirik. Semiautomatic detection and evaluation of autofluorescent areas in retinal images. Proc. EMBS, pages 3327–3330, 2007

43. IIIT Hyderabad References [11] G. D. Joshi, J. Sivaswamy, R. Prashanth, and S. R. Krishnadas. Detection of peri-papillary atrophy and rnfl defect from retinal images. ICIAR, 2011. [12] C. Muramatsu, Y. Hatanaka, A. Sawada, T. Yamamoto, and H. Fujita. Computerized detection of peripapillary chorioretinal atrophy by texture analysis. 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, 2011. [13] J. Cheng*, D. Tao, J. Liu, D. W. K. Wong, N.-M. Tan, T. Y. Wong, and S. M. Saw. Peripapillary atrophy detection by sparse biologically inspired feature manifold. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2012.

44. IIIT Hyderabad We gratefully acknowledge Aravind Eye Hospital, Madurai (for data and expert diagnosis)

45. IIIT Hyderabad THANK YOU

46. IIIT Hyderabad