1. Carmen Alina Lupaşcu Dipartimento di Matematica e Informatica Università degli Studi di Palermo June 8, 2010 Gjøvik, Norway

2. 2 The human visual system has the ability to assimilate information from visible light which reaches the eye. The human eye has a spherical shape and it is wrapped in three distinct layers of tissue:  the outer layer – the sclerotic coat;  the middle layer – the choroid coat;  the inner layer – the retina. Movement of the eyeball produces two slightly differing views of the same scene which our brain is able to fuse into a single, three-dimensional image. June 8, 2010 Gjøvik, Norway

3. 3 The retina - capture light rays and convert them into electrical impulses that travel along the optic nerve to the brain where they are turned into images. The macula - small part of the retina responsible for detailed central vision (such as reading). The fovea - the center of the macula. The optic nerve – transmits electrical impulses from the retina to the brain. The optic disc is the point where the optic nerve enters the retina and it is not sensitive to light. The blood vessels of the retina radiate from the center of the optic nerve. June 8, 2010 Gjøvik, Norway

4. 4 Diseases of the retina can be responsible for partial or total loss of vision. Some retinal pathologies are caused also by the presence of other diseases such as diabetes. The symptoms presented by patients affected by retinal diseases may be:  peripheral vision or side vision deteriorated - in the presence of glaucoma (disease that causes injury to the eye’s optic nerve); Normal visionSame scene viewed by a person with glaucoma June 8, 2010 Gjøvik, Norway

5. 5  reduced visual acuity. Blurring of vision may affect sight in the presence of diseases such as papilledema (optic disc swelling due to intra cranial pressure) or age-related macular degeneration AMD (is a disease associated with aging that slowly destroys central vision which is needed for seeing objects clearly);  poor color vision. Patients with optic atrophy (disease which affects the optic nerve by loosing part of optic disc nerve fibers) may have difficulties with color vision. Color vision is perceived mainly by the macula. Thus any pathology affecting the macula may cause also difficulties with color vision; June 8, 2010 Gjøvik, Norway

6. 6  obscured visual field. Vision may be obstructed by spots of blood caused by swelled and leaky blood vessels of the retina like in the case of diabetic retinopathy. Normal visionSame scene viewed by a person with diabetic retinopathy  image distortion. Straight lines may appear broken or distorted in the presence of macular pathologies, like for example AMD. Scene viewed by a person with macular disease June 8, 2010 Gjøvik, Norway

7. 7 Fluorescein angiography A camera equipped with special filters that highlight the dye is used to photograph the fluorescein as it circulates though the retinal blood vessels. If there are any circulation problems, swelling, leaking or abnormal blood vessels, the dye and its patterns will reveal these in the photographs. Digital retinal fundus photography. A fundus camera is a type of "ophthalmoscope" or “funduscope". Some of the retinal cameras available require pupillary dilation, while others do not. The ones requiring pupillary dilation are called mydriatic cameras, the others non-mydriatic cameras. June 8, 2010 Gjøvik, Norway

8. 8 Courtesy of Clinica Oculistica, Policlinico Universitario Paolo Giaccone, Palermo, Italy June 8, 2010 Gjøvik, Norway

9. 9 Healthy optic disc http://www.isi.uu.nl/Research/Databases/DRIVE/ Healthy vessels Healthy macula June 8, 2010 Gjøvik, Norway

10. 10 Optic disc disease (optic pit) Optic disc disease (optic disc hypoplasia) DUETODISEASESDUETODISEASES http://www.revoptom.com/HANDBOOK/ June 8, 2010 Gjøvik, Norway

11. 11 Uneven illumination DUETOPHOTOGRAPHICDUETOPHOTOGRAPHIC QUALITYQUALITY Dust and dirt artifacts http://eyephoto.ophth.wisc.edu/ResearchAreas/Hypertension/ LBox/LTBXPROT_995.html June 8, 2010 Gjøvik, Norway

12. 12 Advantages:  the retina can be directly analyzed non-invasively in vivo  possibility to analyze a large number of images with time and cost savings  objective measurements  improved repeatability Disadvantages:  image quality (may be improved using preprocessing techniques) June 8, 2010 Gjøvik, Norway

13. 13 Initial point – the center of mass of the region with maximum intensity within the green plane (black ’+’) Points of interest – 3 for each 45° in counter-clockwise direction starting from the initial point (black ’.’) in the left image; excluded points marked with a dark circle (’o’) in the right image June 8, 2010 Gjøvik, Norway

14. uniformity < mean(uniformity all circles) entropy > mean(entropy all circles)third moment > mean(third moment all circles)smoothness > mean(smoothness all circles) 575 31518388 913  From the set of the remaining points of interest, all possible sets of three such points were formed. If we have such a set of points, through these points one circle passes. All three points from the set are different and in one set there are not two points from the same ’direction’, thus we are sure that the points are not all collinear.  For each circle we consider the grey level of the pixels inside the disk and on the circumference. For this region we compute some descriptors of texture based on the intensity histogram of the region, in order to describe better the circle and in order to choose the best circle that fits the OD. 14 June 8, 2010 Gjøvik, Norway

15.  Each remained circle is mapped using the bilinear filtering into the polar coordinates space. The circle which best fits the OD For the circle which best fits the OD A circle which doesn’t fit the OD For a circle which doesn’t fit the OD Mapping images 15 June 8, 2010 Gjøvik, Norway

16. The regression line that best fits the set of the derivatives The strip of the mapping image For a circle which doesn’t fit the OD For the circle which best fits the OD  To the set of the points of maximum derivatives in the y direction we apply the linear least squares fitting technique (linear regression). We try to find the best fitting straight line passing through the set of points described above. In order to quantify the quality of the fitting to the original data we compute the correlation coefficient. For a circle which best fits the OD  In only one central strip of the mapping image we look for the maximum derivatives in the y direction. For the circle which doesn’t fit the OD The maximum derivatives in the y direction 16 June 8, 2010 Gjøvik, Norway

17. 17  In order to compute the correlation coefficient of a set of n data points (x i ; y i ), we first consider the sum of squared values ss xx, ss xy and ss yy about their respective means, and where and. June 8, 2010 Gjøvik, Norway

18. 18  The correlation coefficient is  The equation of the regression line will be where and  We expect to have a correlation coefficient big (close to 1) for the circle that best fits the OD (as the maximum derivatives in the y direction are expected to be at the OD boundary).  The circle that best fits the OD is chosen as the circle with the maximum correlation coefficient associated. June 8, 2010 Gjøvik, Norway

19. 19  In order to evaluate if the circle obtained by our algorithm fits the OD as good as the ground truth (set manually by an ophthalmologist) we consider the ratio where GT is the ground truth circle and C is the circle obtained by the methodology.  A ratio S => 0,7 indicates an excellent result (E), 0,5 <= S < 0,7 denotes a good (G) result which requires improvement and S < 0,5 a fair (F) result. We have two bad results (B) because of the wrong positioning of the initial point (the initial point was chosen as the center of mass of the pixels with the maximum intensity within the green plane of the image). In these bad quality images the initial point is not within the OD area and that’s why we have false detection of the OD.  For the best circle which fits the OD, we consider successful a circle which has associated a ratio S >= 0,5, hence our algorithm achieved a success rate of 70%. June 8, 2010 Gjøvik, Norway

20. 20 Examples of detected optic discs. The detected optic disc (bright contour) overlaps the ground truth (dark contour). EXCELLENT RESULTEXCELLENT RESULT June 8, 2010 Gjøvik, Norway

21. 21 GOODRESULTGOODRESULT Ground truth Detected OD Examples of detected optic discs. The detected optic disc (bright contour) overlaps the ground truth (dark contour). June 8, 2010 Gjøvik, Norway

22. 22 FAIRRESULTFAIRRESULT Detected OD Ground truth Examples of detected optic discs. The detected optic disc (bright contour) overlaps the ground truth (dark contour). June 8, 2010 Gjøvik, Norway

23. 23  Advantages of the methodology: lack of training we don’t use Hough transform for detecting the best circle that fits the OD and this because we don’t want to fix a priori a range for the radius of the circle or to fix a threshold value on the number of pixels of the circle  Improvements that can be made: the way we choose the initial point. This point is chosen as the center of mass of the pixels with the maximum intensity within the green plane. In some bad quality images this choice cause a false detection of the OD.  Success rate: 40 testing images (DRIVE database http://www.isi.uu.nl/Research/Databases/DRIVE/) 95% for the localization of the OD position 70% for the circle that is best fitting the OD June 8, 2010 Gjøvik, Norway

24. FABC trains an AdaBoost classifier with manually labeled images. After the classifier is trained, it is applied to classify pixels as vessel or non-vessel in images not included in the training set. FEATURE VECTOR Features are extracted from the green plane of the retinal images. The total number of features composing the feature vector at each image pixel is 41. Four scales are used to detect vessels of different width: √2, 2, 2√2 and 4. 24 June 8, 2010 Gjøvik, Norway FABC(w/o feature selection) leave-one-out tests using all 40 images in the DRIVE database 864,697/39 pixels from each image, keeping the same proportion as in the ground-truth image between vessel and non-vessel pixels. The sample size was computed with a Z-test, considering a confidence level of 95% and a margin of error of 0.1%.

25. 25 June 8, 2010 Gjøvik, Norway

26. 26 Is an iterative boosting algorithm constructing a strong classifier as a linear combination of weak classifiers. Input: is a n-dimensional feature vector (here n=41) labels T=100 how many times the AdaBoost growing classifier runs when t=1 (the round index) D 1 (i)=1/m, D t (i) denotes the weight of the distribution Task: find a weak hypothesis suitable for the distribution D t (i) (the weak learner searches for by checking all possible thresholds for all features) weak learner algorithm for a fixed feature j (j=1,…,n) June 8, 2010 Gjøvik, Norway

27. 27 The performance of the binary classifier are measured using Receiver Operating Characteristic (ROC) curves. ROC curves are represented by plotting true positive fractions versus false positive fractions as the discriminating threshold of the AdaBoost algorithm is varied. The area under the ROC curve (Az) measures discrimination, that is the ability of the classifier to correctly distinguish between vessel and non-vessel pixels. An area of 1 indicates a perfect classification. The accuracy (ACC) is the fraction of pixels correctly classified. June 8, 2010 Gjøvik, Norway

28. June 8, 2010 Gjøvik, Norway 28 Image 21_training.tif (DRIVE database) FABC segmentationground truth ROC curve

29. June 8, 2010 Gjøvik, Norway 29 Image 19_test.tif (DRIVE database) FABC segmentationground truth ROC curve

30. June 8, 2010 Gjøvik, Norway 30 Image 34_training.tif (DRIVE database) FABC segmentationground truth ROC curve

31. June 8, 2010 Gjøvik, Norway 31 Segmented images along with their corresponding ground truth images, having area and accuracy values spaced approximately even in the worst case (A z =0.8979, ACC=0.9201) best case (A z =0.9725, ACC=0.9670) Image 25_training.tif A z =0.9248, ACC=0.9384 Image 30_training.tif A z =0.9374, ACC=0.9531 Image 10_test.tif A z =0.9537, ACC=0.9630 FABC S E G M E N T A T I O N S G R O U N D T R U T H

32. June 8, 2010 Gjøvik, Norway 32 The mean of the areas under the ROC curves generated by FABC over all DRIVE images is 0.9536 and the accuracy is 0.9575. Segmentation method DRIVE set AzAz ACC FABC (w/o feature selection) 0.95360.9575 Soares et al.0.96140.9466 Human observer-0.9473 Staal et al.0.95200.9442 Niemeijer et al.0.92940.9416 Zana et al.0.89840.9377 Jiang et al.0.91140.9212 Martinez et al.-0.9181 Chaudhuri et al.0.78780.8773

33. 33  Advantages of the methodology: a rich feature vector (41-D), a collection of shape and structural information, in addition to local information at multiple scales  Disadvantages of the methodology: supervised methods are notoriously time expensive  Improvements that can be made: a post-processing stage for improving the vessel map obtained after applying FABC  Success rate: the runner-up (Soares et al.) outperforms FABC by 8.2% FABC achieves the overall best accuracy (improvement of 11.5% with respect to the runner-up) June 8, 2010 Gjøvik, Norway

34. 34 automatic extraction of main vessels find the axis of symmetry of the main vessels (useful for locating the macula as one anatomical constraint says that the macula must be near the approximate axis of symmetry of the main vessels) involve thickness in order to determine the main vessels automatically improve optic disc detection algorithm improve the vessel map and more… Thank you for your attention! June 8, 2010 Gjøvik, Norway