Bridging Quantitative and Qualitative of Glaucoma Detection

Authors: Noor Elaiza Abdul Khalid, Noorhayati Mohamed Noor, Zamalia Mahmud, Saadiah Yahya, and Norharyati Md Ariff

Abstract:

Glaucoma diagnosis involves extracting three features of the fundus image; optic cup, optic disc and vernacular. Present manual diagnosis is expensive, tedious and time consuming. A number of researches have been conducted to automate this process. However, the variability between the diagnostic capability of an automated system and ophthalmologist has yet to be established. This paper discusses the efficiency and variability between ophthalmologist opinion and digital technique; threshold. The efficiency and variability measures are based on image quality grading; poor, satisfactory or good. The images are separated into four channels; gray, red, green and blue. A scientific investigation was conducted on three ophthalmologists who graded the images based on the image quality. The images are threshold using multithresholding and graded as done by the ophthalmologist. A comparison of grade from the ophthalmologist and threshold is made. The results show there is a small variability between result of ophthalmologists and digital threshold.

Keywords: Digital Fundus Image, Glaucoma Detection, Multithresholding, Segmentation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1327614

References:

[1] J. C. Dunn, "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters," Journal of Cybernetics, vol 3, pp. 32-57,1973.
[2] J. C. Bezdek, "Pattern Recognition with Fuzzy Objective Function Algoritms," Plenum Press, New York, 1981.
[3] A.Murthi and M.Madheswaran," Enhancement Of Optic Cup To Disc Ratio Detection In Glaucoma Diagnosis," International Conference on Computer Communication and Informatics (ICCCI -2012), Coimbatore, INDIA, Jan 2012
[4] S. Stacy, "Glaucoma-A Silent Thief." Bay Area MD News, Vol 2, No 12. 2009.
[5] N. Jagadish , A.U. Rajendra, P. Subbanna B., S. Nakul and C.L. Teik " Automated Diagnosis of Glaucoma Using Digital Fundus Images," J Med Syst, 33:337-346 , 2009.
[6] L.G. Nyul, "Retinal image analysis for automated glaucoma risk evaluation," SPIE: Medical Imaging, vol. 7497, pp. 74 971C1-9, 2009.
[7] X. Song X., K. Song, Y. Chen," A Computer-based Diagnosis System for Early Glaucoma Screening," Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. page(s): 6608 - 6611 Print ISBN: 0-7803-8741-4, 2005.
[8] Y. Hatanaka, A. Noudo, C. Muramatsu, A. Sawada, T. Hara, T. Yamamoto, and H. Fujita, "Automatic measurement of cup to disc ratio based on line profile analysis in retinal images," Proc. of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 3387-90,2011.
[9] J. Liu, F.S. Yin, D.W.K. Wong, Z. Zhang, N.M. Tan, C.Y. Cheung, M. Baskaran, T. Aung, T.Y. Wong, "Automatic Glaucoma Diagnosis from Fundus Image," Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE Page(s): 3383 - 3386, 2011.
[10] S. Kavitha, S. Karthikeyan, K. Duraiswamy, "Neuroretinal rim Quantification in Fundus Images to Detect Glaucoma," IJCSNS International Journal Of Computer Science and Network Security, Vol. 10, No. 6 pp. 134-140, 2010.
[11] M. Huang, H. Chen, and J. Huang, "Glaucoma detection using adaptive neuroÔÇÉfuzzy inference system," In Proceedings of Expert Syst. Applications, pp. 458-468, 2007.
[12] H. Li and O. Chutatape, 2003, "A model-based approach for automated feature extraction in fundus images," Computer Vision Proceedings. Ninth IEEE International Conference, page(s): 394 - 399 vol.1, Print ISBN: 0-7695-1950-4, Oct 2003.
[13] A.F.M.Hani, N. F. Ngah, T. M. George, L.I. Izhar, N. Hermawan and A.N. Hanung, "Analysis of Foveal Avascular Zone in Colour Fundus Images for Grading of Diabetic Retinopathy Severity," 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, August 31 - September 4, 2010.
[14] T. P. Karnowski, D. Aykac, L. Giancardo, Y.Li , T. Nichols, K.W. Tobin, E. Chaum, "Automatic Detection of Retina Disease: Robustness to Image Quality and Localization of Anatomy Structure," 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, August 30 - September 3, 2011.
[15] K. Noronha, J. Nayak, S.N. Bhat, "Enhancement of retinal fundus Image to highlight the features for detection of abnormal eyes," TENCON 2006. IEEE Region 10 Conference page: 1 - 4 E-ISBN: 1- 4244-0549-1 Print ISBN: 1-4244-0548-3, Nov 2006.
[16] S. Kumar, M. Madheswaran, "Extraction of Blood Vascular Network for Development of an Automated Diabetic Retinopathy Screening System," Computer Technology and Development, ICCTD '09. International Conference page(s): 360 - 364, Print ISBN: 978-0-7695-3892-1, November 2009.
[17] G. Xinbo, "Fuzzy Cluster Analysis and Application," Sian: Xidian University Press, pp.124-130, 2004.
[18] S. Arora, J. Acharya, A. Verma., P. K. Panigrahi, "Multilevel Thresholding for Image Segmentation through a Fast Statistical Recursive Algorithm," Pattern Recognition Letters, 29(2), pp 119-125, 2008.
[19] W.X Kang, Q.Q. Yang, R.P. Liang, "The Comparative Research on Image Segmentation Algorithms," First International Workshop on Education Technology and Computer Science, pp.703- 707, vol. 2, 2009.
[20] P.G. Prageeth, J.David, A. Sukesh Kumar, "Early Detection of Retinal Nerve Fiber Layer Defects using Fundus Image Processing," Recent Advances in Intelligent Computational Systems (RAICS), IEEE , page(s): 930 - 936 , ISBN: 978-1-4244-9478-1,2011.
[21] J. Liu, D.W.K. Wong, J.H. Lim, X. Jia, F. Yin, H. Li, W. Xiong, T.Y. Wong, "Optic cup and disk extraction from retinal fundus images for determination of cup-to-disc ratio," Industrial Electronics and Applications, ICIEA 2008. 3rd IEEE Conference, page(s): 1828 - 1832, E-ISBN: 978-1-4244-1718-6 Print ISBN: 978-1-4244-1717-9, 2008.
[22] G.D Joshi, J. Sivaswamy, S.R. Krishnadas, "Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment," Medical Imaging, IEEE Transactions, Volume: 30 Issue:6 ,On page(s): 1192 - 1205, ISSN: 0278-0062, 2011.
[23] F.Kurugollu, B. Sankur and A.E.Harmanci, "Color Image Segmentation using Histogram Multithresholding and Fusion," Image and Vision Computing 19, pp 915-928, 2001
[24] C.Sinthanayothin, J.F. Boyce, H.L. Cook, T.H. Williamson, "Automated localisation of the optic disc, fovea,and retinal blood vessels from digital colour fundus images," Br J Ophthalmol, 83:902-910,1999
[25] T. Romanay and B. Harvey, "Slit-Lamp Biomicroscopy," Optician, Vol 232, No 6069, September 2006.
[26] Augusto Azuara-Blanco, Jennifer Burr, Ruth Thomas, Graeme Maclennan, Stephen McPherson, "The accuracy of accredited glaucoma optometrists in the diagnosis and treatment recommendation for glaucoma", Br J Ophthalmol 2007;91:1639-1643. doi: 10.1136/bjo.2007.119628
[27] F. Topouzis and E. Anastasopoulos, "Glaucoma- The Importance of Early Detection and Early Treatement", Touch Briefings,2007
[28] Ganesh Babu T.R, Shenbagadevi, " Automatic Detection of Glaucoma Using Fundus Image", European Journal of Scientific Research , ISSN 1450-216X Vol.59 No.1, pp.22-32, 2011.
[29] Narasimhan K., Vijayarekha K., "An Efficient Automated System For Glaucoma Detection Using Fundus Image", Journal of Theoretical and Applied Information Technology,Vol. 33 No.1, 2011.
[30] Kavitha S., Duraiswamy K., "An Efficient Decision Support System for Detection Of Glaucoma In Fundus Images Using Anfis" International Journal of Advances in Engineering & Technology, Vol. 2, Issue 1, pp. 227-240, 2012.
[31] Y. Hatanaka, A. Noudo, C. Muramatsu, A. Sawada, T. Hara, T. Yamamoto, and H. Fujita, "Vertical cup-to-disc ratio measurement for diagnosis of glaucoma on fundus images", Proc. of SPIE Vol. 7624, 2010.