Rasipuram , India. University, Mettu, Ethipio.

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1 ISSN: X CODEN: IJPTFI Available Online through Research Article DETECTION OF GLAUCOMA USING NOVEL FEATURES OF OPTICAL COHERENCE TOMOGRAPHY IMAGE T. R. Ganesh Babu 1, Pattanaik Balachandra 2, K.Vidhya 3 1 Professor, Department of Electronics and communication Engineering, Muthayammal Engineering College, Rasipuram , India. 2 Professor, Department of Electrical and Computer Engineering,Faculty of Engineering and Technology, Mettu University, Mettu, Ethipio. 3 Associate Professor, Department of Electronics and communication Engineering Velammal Engineering College, Ambattur, Chennai Received on: Accepted on: Abstract: Aim: To determine the features namely CDR (Cup to Disc Ratio),cup depth and retinal thickness in Optical Coherence Tomography (OCT) images. Settings and design: We include 50 normal subjects and 50 subjects affected by glaucoma to this study. Subjects and methods: The retinal-vitreal (RV) boundary is segmented by the application of wavelet transform, the retinal-choroid (RC) boundary is detected using multi-level Thresholding techniques and the extents of the optic cup and disc are identified from the results. From these detected regions, the optic cup to disc ratio is computed. Apart from the said effects, the disease also affects the cup depth and retinal thickness in the optic disc region. The detected RV and RC boundaries are used in the automatic determination of cup depth and retinal thickness as the novel and new parameters for glaucoma indication. Results: Thus three features are extracted in OCT images namely CDR, cup depth and retinal thickness. These features are validated by classifying the normal and glaucoma OCT images using Back Propagation Neural network (BPN) and Support Vector Machine (SVM) classifiers. The experimental result shows sensitivity, specificity and accuracy of the SVM classifier as 94%, 93.33% and 93.83% respectively which are better than the result obtained using BPN. Conclusion: Combining all these features such as CDR cup depth and retinal thickness produced satisfactory results for detection of glaucoma. Keywords: Optical coherence tomography image, cup to disc ratio, cup depth, retinal thickness, glaucoma. 1. Introduction OCT can discriminate with high resolution the cross-sectional morphological features of optic disc, the layered structure of the retina, and normal anatomical variations in retinal nerve fibre layer thickness. [1] In glaucoma, retinal IJPT April-2017 Vol. 9 Issue No Page 28344

2 nerve fibre thickness is measured at standardized locations around the optic nerve head. Additional radial scans through the optic nerve head afford the evaluation of cupping and juxta-papillary retinal nerve fibre layer thickness. In ophthalmic applications, OCT has been used for a variety of purposes ranging from measuring corneal thickness in the anterior region of the eye to measure the retinal thickness in the posterior. [2] 3. Materials and methods OCT will provide better information about the optic nerve head region. Figure 1 shows a typical labeled structure of OCT cross-sectional nerve head scan. The original image size is 689x329 pixels. RV boundary Choroid end point Optic cup Optic disc Figure 1. OCT image through optic nerve head. The CDR is a popular measure used by ophthalmologists for assessing the health of the optic nerve head. Typically, this ratio is small for normal patients. However, there is a slight variation found in the normal eyes; direct and more extensive characterization of nerve head geometry, and its change over time, can mitigate this limitation. As glaucoma progresses, more the nerve tissue dies and the optic nerve shrinks causing the optic cup to expand. This leads to increase in CDR and the cup deviates from its natural shape. Overviews for the basic steps of the system are as follows: Identifying the Retinal Vitreal (RV) boundary Identifying the Region of Retinal Choroid (RC) boundary. Identifying the edges of the RC boundary. Calculation of CDR. Determination of Cup depth. Determination of Retinal thickness. Classification based on the obtained features. 4. Detection of Retinal Vitreal Boundary using Wavelet Transform In this method, wavelet transform [3] is used to segmnt the retinal vitreal boundary. It is most important to identify the areas in which input image changes its characteristics abruptly. In order to identify the areas, the high frequency sub- IJPT April-2017 Vol. 9 Issue No Page 28345

3 bands of wavelet transformed image are used as they will have the high frequency components namely the edges of the image. Bezier curve method is used for smoothing the RV boundary. 4.1 Retinal vitreal boundary detection In this paper R-plane is consider for edge detection because of the R is free from details of vitreous humor in non retinal area. The algorithm for detecting the retinal vitreal boundary is as follows. 1. Detection of retinal vitreal edges from R-plane. 2. Edge smoothening to get retinal vitreal boundary Detection of retinal vitreal edges In this method, Haar wavelet transform is used to detect the edges, in the OCT images. The edge image is constructed by using obtained wavelet coefficients of LL HH and HL Sub-band. If an element in the LH sub-band is V m, n, an element in HH sub-band is d m,n and an element in the HL sub-band is h m, n then the corresponding element e m,n in the edge image is given by Equation 1 [4]. V mn d Em, n 2 2 m, n h 2 (1) This value of E m,n is calculated for all coefficients in the sub bands. Hence this forms a sub band of enhanced edge coefficients along with other less dominant coefficients pertaining to smooth regions. By analyzing 50 OCT images, a threshold of 25 is identified and applied on this sub band of E m, n s. The resulting edge image is shown in Figure 4 (a). m, n Figure 4. ( a) Edge image computed using Figure 4. ( b) Reconstructed images LH, HL and HH sub-bands using inverse wavelet transform The edge image has been placed in the LL sub band and the coefficients of all other sub bands LH, HL and HH are set as zero. Then the inverse wavelet transform is applied by using the sub bands to obtain the desired edge image. Figure 4 (b) shows the reconstructed image, where in the edges are enhanced. Bezier curve fitting algorithm is applied to processed image (shown in Figure 4 (b))to smooth the RV boundary. Figure 5 shows the smoothed edges of RV boundary obtained by Bezier curve [5]. IJPT April-2017 Vol. 9 Issue No Page 28346

4 RV boundary Figure 5. Smoothed edges of RV Boundary obtained by Bezier curves. 4.2 Extracting the retinal choroid boundary The input RGB OCT nerve head image is converted into gray scale image and then median filter is applied to remove the noises in the gray scale image. A multi-level thresholding is applied to the median filtered image to obtained binary image [6]. After multi-level thresholding, morphological opening is used to remove the small objects. Then canny edge detection is applied to detect the edges as it detects edges in noise conditions also and control the amount of detail in the edge image. The 8-connected neighborhood in the canny edge detected image is then labeled and the area of each connected components is estimated. If the area of each 8-connected neighborhood is less than 10 pixels, then the 8-connected neighborhood of the edge detected image is removed because the area of the choroid region is approximately more than 10 pixels. This value is obtained using trial and error method by analyzing 20 images. Finally, the RV boundary has been also removed. Later, Bezier curve is applied for column wise first true pixels and it is called as RC boundary. The labeled image and upper boundary of choroid region image are shown in Figure 6 and 7 respectively. Upper boundary of choroid Figure 6. Labeled edge detected image. IJPT April-2017 Vol. 9 Issue No Page 28347

5 Figure 7. Upper boundary of choroid region obtained by Bezier curve. 4.3 Optic Disc and Optic Cup Measurement From the first column of the left side of RC boundary, the distance between each point is calculated using this formula. 2 2 Distance [( X X ) ( Y ) ] (2) Y2 Where, x 1, y 1 = first pixel positions and x 2, y 2 = adjacent pixel position When the calculated distance value is greater than a predefined threshold, that point becomes the edge of the left side of RC boundary. By repeated experiments, the threshold is set to three. Similarly, from the last column of the right side of RC boundary, the distance between each point is calculated using the above formula. When the calculated distance value is greater than the same predefined threshold, that point becomes the edge of the right side of the RC boundary. The distance between the two edge points(c and D) is the optic disc. Figure 8shows the end points of the choroid. Choroid end points C D Optic disc Figure 8. End points of choroid. A horizontal line is drawn from the two edges of the RC boundary. That horizontal line cuts the RV boundary making two intersection points (A and B). The distance between the two intersection points is called the optic cup. Figure 9 shows the extent of the optic cup. IJPT April-2017 Vol. 9 Issue No Page 28348

6 C A B D Figure 9. Optic cup diameter calculations. The OCT image of a normal subject is shown in Figure 10 and the OCT image of glaucoma is shown in Figure 11. The distances calculated are used to compute the CDR. Cup to disc ratio Figure 10. OCT image of Normal subject (CDR = ) Distance between C and D Distance between A and B Figure 11. OCT image of a subject affected by glaucoma (CDR = ) (3) 5. Detection of New Features of Oct In the fundus image of diabetic patients the regions of cup and disc are merged. Hence, CDR cannot be computed in fundus image for such patients. In such cases, we have only the CDR value computed from OCT images available for diagnosis of glaucoma. But this alone will not be enough because for some subjects, larger cup in a small optic disc will show higher CDR value and hence gives a false alarm. In order to overcome these conditions, novel features namely cup depth and retinal thickness are proposed in this work for OCT images. Irrespective of the sizes of the cup and disc, the cupping takes place in glaucoma condition due to the thinning of nerve fiber in the optic disc region and hence this cupping or the cup depth is considered here as a novel feature for diagnosis of glaucoma. This feature can be identified only in OCT images since it gives the depth information which is not available in fundus image. 5.1 Determination of Cup Depth The procedure to calculate the new feature namely the cup depth is as follows: from the OCT image, the RV boundary is obtained as in section 4.1 and the edges of the choroid are identified as discussed in section 4.2. Two horizontal lines are drawn from these edges of choroid. These lines are intersecting the RV boundary at two points IJPT April-2017 Vol. 9 Issue No Page 28349

7 and the distance between these intersecting points at RV boundary and row wise last true pixels in the RV boundary is called as cup depth. The OCT image of a normal subject is shown in Figure 12 and the subject affected by glaucoma is shown in Figure13.Cup depth is shown as number of pixels and the extent of this depth is also shown. The resolution factor for the collected OCT image is found to be 10 micron/pixel. The cup depth thickness measurement is given by Equation 4. Thickness in microns = Resolution factor Cup depth in number of pixels (4) 94 Cup depth 200 Cup depth Figure 12 OCT Normal image Figure 13 OCT abnormal image 5.2 Retinal Thickness Due to deeper cupping, the retinal thickness reduces in the disc region and this novel feature is also an indicator of glaucoma apart from CDR and cup depth in OCT images. Figure 14 and 15 shows the retinal area of a normal subject and image affected by glaucoma. The thickness of the retinal area in the disc region is marked in the Figures 14 and 15 and it can be inferred that the retinal thickness is much reduced in the glaucoma condition. Retinal thickness in cup region Figure 14. Retinal area in a normal image. Retinal thickness cup region Figure 15. Retinal area in a glaucoma image Detection of Retinal Thickness The retinal thickness can be computed as follows: - The input RGB OCT nerve head image is converted into gray scale image and then median filter is applied to remove the noise in the gray scale image. A multi thresholding is applied to gray scale image to obtain binary image and it is shown in Figure 16 for normal image and in Figure 17 for glaucoma condition. In the binary image, true pixels consist of retinal area and black pixels consist of non-retinal area. The obtained binary image is divided into four equal regions vertically and the regions are marked as I, II, III and IV in Figures 16 and 17. This sort of division makes the optic disc region to fall in the II and III division. The number of true pixels in these two regions will give the indication about the retinal thickness in the disc region. IJPT April-2017 Vol. 9 Issue No Page 28350

8 Retinal thickness is determined by calculating the number of true pixels in the second and third quadrant of the retinal regions in Figure 16 and Figure 17. In order to determine the average retinal thickness in micron, the total number of true pixels in the regions marked as II and III are multiplied with the resolution factor of the corresponding OCT camera. The resolution factor of is 10 micron/pixel for OCT camera used to capture the OCT images used in this work. Then the thickness measurement is given by Equation (5). resolution factor * number of pixel sin each column Re tinal Thickness in microns Number of column (5) I II III IV I II III IV 6. Result and Discussion Retinal thickness in cup region Figure 16. Retinal area normal image. Retinal thickness in cup region Figure 17 Retinal area glaucoma image. The developed algorithm has been tested on 50 normal OCT images and 50 glaucoma OCT images based on CDR and the proposed two novel features namely cup depth and retinal thickness. 6.1 Analysis of CDR The result of the developed algorithm shows high percentage of CDR coincidence with Gold Standard Value. Table 1 shows the CDR ranges and mean error of the proposed method. The mean error is calculated by comparing CDR values obtained by the proposed system with gold standard values. Table 1. CDR ranges and mean error of the proposed method. S.No Parameters Range for Normal Condition Range for Glaucoma condition 1 CDR range (proposed method) to to % of Mean Error 2.75% Figure 18 shows the scatter plot computed with observed CDR and gold standard value as the parameters. The equation relating these two parameters is given by GOLD STANDARD = OBSERVED VALUE (6) Correlation coefficient between Observed Value and Gold Standard = IJPT April-2017 Vol. 9 Issue No Page 28351

9 Figure 18. Scatter plot of computed CDR and gold standard value. 6.2 Analysis of Cup Depth The cup depth in normal images lies between 740 to 1120µm and for glaucoma, it lies between 1190 to 2120 µm. It can also be observe from the results that are difficult to find any correlation between CDR and cup depth. Hence the newly proposed feature, cup depth is an important feature for glaucoma detection. 6.3 Analysis of Retinal Thickness Analysis of the result shows that retinal thickness value for set of normal images ranges from µm to µm and for glaucoma images; it ranges from µm to µm. This shows that there is an overlap of values between normal and glaucoma condition. It is also inferred that in glaucoma condition, the retinal thickness is reduced which well coincides with the actual condition prevailing in optic nerve head. From the results, it can be inferred that is difficult to predict any relation between CDR and retinal thickness value. 6.4 Detection of Glaucoma by Classifier In this work, totally 100 OCT images namely 50 from normal and 50 from glaucoma patients are used. Features, such as CDR, cup depth and average retinal thickness are computed for both the set of samples using the proposed algorithm. Due to overlapping problem in features and due to difficulty in obtaining any relation between the features, initially BPN classifier is used for classification. To test the validity of the feature we used BPN network [7] for the classification of OCT images between normal and glaucoma cases. A table 2 shows the results of the BPN. From the table, it is inferred that the classification accuracy is only 89.33% Table 2. Data set that are correctly classified for BPN classifier. Type of image Number of data sets used for training Number of data sets used for testing Correctly classified test data Percentage correctly classified (%) Normal % IJPT April-2017 Vol. 9 Issue No Page 28352

10 Glaucoma % Average 89.33% To improve the accuracy, SVM Classifier is used on the same set of data. Here a linear kernel is used to map the training data into the kernel space [8]. The results of the classifier are shown in table 3. It is inferred from the table 3 that classification accuracy is improved to Table 3. Data set that are correctly classified for SVM, Type of image Number of samples Number of samples Correctly classified Percentage correctly used for training used for testing test data classified (%) Normal % Glaucoma % Average 93.66% Table 4. Analysis of classification results. Classifier TN TP FP FN Sensitivity Specificity Positive value Predictive Accuracy BPN % 80% 93.87% 89.29% SVM % 93.33% 97.91% 93.83% TP Sensitivity = *100% TP FN TN Specificity = *100% TN FP TP Positive Predictive Value = *100% TP FP TP TN Accuracy = *100% TP TN FP FN (7) (8) (9) (10) Sensitivity refers to the percentage of abnormal OCT image classified as abnormal, Specificity refers to the percentage of normal OCT image classified as normal, Positive predictive value refers to how best it can detect the normal and accuracy refers to the ability of the classifier to classify correctly. Sensitivity, Specificity, Positive Predictive value and Accuracy are calculated by the equations 7, 8, 9 and 10 respectively. IJPT April-2017 Vol. 9 Issue No Page 28353

11 7. Conclusion T. R. Ganesh Babu*et al. /International Journal of Pharmacy & Technology In this paper, OCT image features are used to detect glaucoma. The RV boundary is segmented by using wavelet transform. The RC boundary is segmented by using multilevel thresholding. Using the obtained boundaries, CDR value is determined. The mean error of CDR for the proposed system is 2.75%. Apart from CDR, two novel features namely cup depth and retinal thickness are used to strengthen the glaucoma examination. The features namely CDR, cup depth and retinal thickness are computed automatically and the performance of the proposed algorithm is tested with two different classifiers namely BPN and SVM. The SVM gives the better results compared to BPN classifiers.the experimental result shows sensitivity, specificity and accuracy of the SVM classifier as 94%, 93.33% and 93.83% respectively. References 1. Bouma, B &Tearney, GJ, Handbook of Optical Coherence Tomography, (2001) 1st Edn., Dekker, New York. 2. R.Wang, D. Koozekanani, C. Roberts, and S. Katz, Reproducibility ofretinal thickness measurements using optical coherence tomography, Invest. Ophthalmol. Vis. Sci., (1999) vol. 40, pp. S125 S125,. 3. Michael Unser, Ten good reasons for using spline wavelets, Proc. Spie Wavelets Applications in Signal and Image Processing, (1997) vol. 3169, pp Avijit Sur, AS, Chakraborty, N &Saha, PI, A new wavelet based edge detection technique for Iris imagery, Proceedings of the IEEE International Advance Computing Conference, Patiala, India, (2009),pp TetsuzoKuragano& Akira Yamaguchi, A Method to generate freeform curves from a hand drawn sketch, The Journal on Systemics, Cybernetics and Informatics (JSCI), (2007), vol. 5, no. 2, pp Papamarkos, N & Gatos, B, A New Approach for Multilevel Threshold Selection, Academic Press, (1994), vol. 56, no. 5, pp Hecth-Nelsen, R, Neurocomputing (Addison-Wesley, Menlo Park, CA. (1990). 8. Smola, Alex, J, Bernhard Schölkopf& Klaus-Robert Müller, The connection between regularization operators and support vector kernels, Neural Networks(1998), vol.11, no. 4, pp IJPT April-2017 Vol. 9 Issue No Page 28354