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International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)
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IJTEEE >> Volume 3 - Issue 12, December 2015 Edition



International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289



Automated Glaucoma Detection Techniques Using Fundus Image

[Full Text]

 

AUTHOR(S)

Rohan Appasaheb Borgalli, Hari Pratap Gautam , Winner George Parayil

 

KEYWORDS

Keywords : Intra ocular pressure, Ocular Computing Tomography, Heidelberg Retinal Tomography, Cup to Disk Ratio, Support Vector System, Artificial Neural Network.

 

ABSTRACT

ABSTRACT: This paper presents automated glaucoma detection techniques based on neural network and Adaptive Neuro fuzzy Inference system (ANFIS) Classifier. Digital image processing techniques, such as preprocessing, morphological operations and thresholding, are widely used for the automatic detection of optic disc, blood vessels and computation of the features of fundus image. Glaucoma is a disease of the optic nerve caused by the increase in the intraocular pressure of the eye. Glaucoma mainly affects the optic disc by increasing the cup size. It can lead to the blindness if it is not detected and treated in proper time. The detection of glaucoma through Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) is very expensive, this limitation is removed by this Glaucoma Diagnosis system with good performance. In addition to diagnosis of Glaucoma a Graphical user interface (GUI) is developed. This GUI is used for automatic diagnosing and displaying the diagnosis result in a more friendly user interface The results presented in this paper indicate that the features are clinically significant in the detection of glaucoma. Proposed system of this paper is able to classify the glaucoma automatically with a sensitivity and specificity of 98% and 95% respectively.

 

REFERENCES

[1] H.A. Quigley and A.T. Broman, "The number of people with glaucoma worldwide in 2010 and 2020," Br J Ophthalmology, vol. 90, pp. 262-7, Mar 2006.

[2] B. Thylefors and A.D. Negrel, "The global impact of glaucoma," Bull World Health Organ, vol. 72, no. 3, pp. 323-6, 1994.

[3] S.Y. Shen et al., "The prevalence and types of glaucoma in Malay people: the Singapore Malay eye study," Invest Ophthalmology Vis Sci,vol. 49, no. 9, pp. 3846-51, 2008.

[4] P.J. Foster et al., "The prevalence of glaucoma in Chinese resi-dents of Singapore: a cross-sectional population survey of the Tanjong Pagar district," Arch Ophthalmol, vol. 118, no. 8, pp. 1105-11, 2000.

[5] D.H. Sim and L.G. Goh, "Screening for glaucoma in the Chinese elderly population in Singapore," Singapore Med J, vol. 40, no. 10, pp. 644-7, 1999.

[6] Congdon, N., et al. “Eye Diseases Prevalence Research Group. ‘Causes and Prevalence of Visual Impairment among Adults in the United States.” Archives of Ophthalmology 122.4 (2004): 477-85.

[7] Betz, P., Camps, F., Collignon-Brach, J., Lavergne, G., Weekers, R., 1982. Biometric study of the disc cup in open-angle glauco-ma. Graefes Arch. Clin. Exp. Ophthalmol. 218 (2), 70–74.

[8] Quillen, D. A. “Common Causes of Vision Loss in Elderly Pa-tients.” American Family Physician 60.1 (1999): 99-108.

[9] Medeiros, F.A., Zangwill, L.M., Bowd, C., Weinreb, R.N., 2004b. Comparison of the GDx VCC scanning laser polarimeter, HRT II confocal scanning laser ophthalmoscope, and stratus OCT opti-cal coherence tomograph for the detection of glaucoma. Arch. Ophthalmol. 122 (6), 827–837.

[10] Staal, J., Abràmoff, M., Niemeijer, M., Viergever, M., van Ginneken, B., 2004. Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imag. 23 (4), 501–509.

[11] Burgansky-Eliash, Z., Wollstein, G., Bilonick, R.A., Ishikawa, H., Kagemann, L., Schuman, J.S., 2007. Glaucoma detection with the Heidelberg Retina Tomograph (HRT) 3. Ophthalmology 114 (3), 466–471.

[12] Rafael C. Gonzalez and Richard E. Woods. ‘Digital Image Pro-cessing using MATLAB’ 2nd edition. Prentice Hall, 2002. ISBN 0-201-18075-8.

[13] Narasimha-Iyer, H., Can, A., Roysam, B., Stewart, C.V., Tanen-baum, H.L., Majerovics, A., Singh, H., 2006. Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy. IEEE Trans. Biomed. Eng. 53 (6), 1084–1098.

[14] Meier, J., Bock, R., Michelson, G., Nyúl, L.G., Hornegger, J., 2007. Effects of preprocessing eye fundus images on appearance based glaucoma classification. In: 12th International Conference on Computer Analysis of Images and Patterns, CAIP. Lecture Notes in Computer Science (LNCS), vol. 4673/2007, Berlin, pp. 165–173.

[15] Canny, J.F., 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8 (6), 679–698.

[16] Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C., 2000. Image inpainting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, Siggraph 2000, New Orleans, USA, pp. 417–424.

[17] Can, A., Shen, H., Turner, J.N., Tanenbaum, H.L., Roysam, B., 1999. Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans. Inform. Technol. Biomed. 3 (2), 125–138.

[18] Chrástek, R., Wolf, M., Donath, K., Niemann, H., Paulus, D., Hothorn, T., Lausen, B., Lämmer, R., Mardin, C., Michelson, G., 2005. Automated segmentation of theoptic nerve head for diag-nosis of glaucoma. Med. Image Anal. 9 (4), 297–314.

[19] Turk, M., Pentland, A., 1991. Eigenfaces for recognition. J. Cognit. Neurosci. 3 (1), 71 -86.

[20] Blanco, M., Penedo, M.G., Barreira, N., Penas, M., Carreira, M.J., 2006. Localization and extraction of the optic disc using the fuzzy circular Hough transform. Lect. Notes Comput. Sci. 4029, 712–721.

[21] Zhu, X., Rangayyan, R., Ells, A., 2009. Detection of the optic nerve head in fundus images of the retina using the hough transform for circles. J. Digit. Imag.

[22] Xu, J., Chutatape, O., Sung, E., Zheng, C., Kuan, P.C.T., 2007. Optic disk feature extraction via modified deformable model technique for glaucoma analysis. Pattern Recognit. 40 (7), 2063–2076.

[23] Hoover, A., Kouznetsova, V., Goldbaum, M., 2000. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imag. 19 (3), 203–210.

[24] Abràmoff, M.D., Alward, W.L.M., Greenlee, E.C., Shuba, L., Kim, C.Y., Fingert, J.H., Kwon, Y.H., 2007. Automated segmentation of the optic disc from stereo color photographs using physiologi-cally plausible features. Invest. Ophthalmol. Vis. Sci. 48 (4), 1665–1673.