A Brief Review of Analysis, Diagnosis & Detection of Glaucoma Eye Disease in Human Beings

Swathi Ramachandran, T. C. Manjunath, Pavithra G., Prathibha Harish

Abstract


This document gives formatting instructions for authors preparing papers for publication in the Proceedings of an IEEE conference. The authors must follow the instructions given in the document for the papers to be published. You can use this document as both an instruction set and as a template into which you can type your own text.The human eye forms one of the vital organs of the body. The eye always plays a vital role in our daily life ; as without eyes, the whole world would be dark & performance of the daily routine works would be very difficult. In the sense, without sight, it would be very difficult for any person to do any activity.  There are various reasons for the loss of vision/sight in the human eyes. Hence, blindness has to be avoided in the human eyes as the most precious human organ is solely responsible for the vision.  One of the cause for blindness & loss of vision in the eyes is due to different types of diseases that occurs in the eyes because of various factors. One such disease which is caused due to vision loss is the ‘Glaucoma’. In this paper, a brief review of the analysis, diagnosis & detection of Glaucoma Eye disease in human beings is being presented in a nutshell as a survey paper. In this paper, only a exhaustive literature survey of the recent works done by various authors across the globe till date is being presented w.r.t. the exciting & application oriented bio-medical field of glaucoma is being presented in a nutshell so that this paper serves as a ready reckoner for all the people who are going to work in this exciting field.

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References


Khan, Fauzia, et.al., “Detection of glaucoma using retinal fundus images", Biomedical Engineering International Conference (BMEiCON), 2013 6th. IEEE, 2013.

Kavitha, S., S. Karthikeyan, and K. Duraiswamy, “Early detection of glaucoma in retinal images using cup to disc ratio”, Computing Communication and Networking Technologies (ICCCNT), 2010 International Conference on. IEEE, 2010.

Babu, TR Ganesh, and S. Shenbagadevi. "Automatic detection of glaucoma using fundus image." European Journal of Scientific Research 59.1 (2011): 22-32.

Murthi, A., and M. Madheswaran. "Enhancement of optic cup to disc ratio detection in glaucoma diagnosis." Computer Communication and Informatics (ICCCI), 2012 Int. Conf. on. IEEE, 2012.

Ho, Chih-Yin, et al. "An atomatic fundus image analysis system for clinical diagnosis of glaucoma." Complex, Intelligent and Software Intensive Systems (CISIS), 2011 International Conference on. IEEE, 2011.

Yang, Xiaoyun, Philip Morrow, and Bryan Scotney. "optic nerve head segmentation in HRT images." Image Processing, 2006 IEEE International Conference on. IEEE, 2006.

Li, Huiqi, and OpasChutatape. "Automated feature extraction in color retinal images by a model based approach." Biomedical Engineering, IEEE Transactions on 51.2 (2004): 246-254.

Tan, N. M., et al. "Mixture model-based approach for optic cup segmentation."Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. IEEE, 2010.

Hatanaka, Yuji, et al. "Automatic measurement of cup to disc ratio based on line profile analysis in retinal images." Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. IEEE, 2011.

Yin, Fengshou, et al. "Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis." Computer-Based Medical Systems (CBMS), 2012 25th International Symposium on. IEEE, 2012.

Morales, Sandra, et al. "Automatic detection of optic disc based on PCA and mathematical morphology." IEEE transactions on medical imaging 32.4 (2013): 786-796.

Cheng, Jun, et al. "Superpixel classification based optic disc and optic cup segmentation for glaucoma screening." Medical Imaging, IEEE Transactions on32.6 (2013): 1019-1032.

Rajendra Acharya, U., et al. "Automated diagnosis of glaucoma using texture and higher order spectra features." Information Technology in Biomedicine, IEEE Transactions on 15.3 (2011): 449-455.

Kurita, Yuichi, et al. "Contact-based stiffness sensing of human eye."Biomedical Engineering, IEEE Transactions on 55.2 (2008): 739-745.

Aquino, Arturo, Manuel Emilio Gegúndez-Arias, and Diego Marín. "Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques." Medical Imaging, IEEE Transactions on 29.11 (2010): 1860-1869.

Liu, J., et al. "Automatic glaucoma diagnosis from fundus image." Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. IEEE, 2011.

R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing using MATLAB. New York: PHI, 2004.

Tiwari, Shamik, et al. "A Blind Blur Detection Scheme Using Statistical Features of Phase Congruency and Gradient Magnitude." Advances in Electrical Engineering 2014 (2014).

DRIONS-DB: Digital Retinal Images for optic Nerve Segmentation Database

Haykin, Simon, and Neural Network. "A comprehensive foundation." Neural Networks 2.2004 (2004).

Tiwari, Shamik, Vidya Prasad Shukla, SangappaBiradar, and Ajay Singh. "Texture features based blur classification in barcode images." International Journal of Information Engineering and Electronic Business (IJIEEB) 5, no. 5 (2013): 34

Quigley HA. Number of people with glaucoma worldwide. British Journal of Ophthalmology. 1996 May; 80(5):389–93.

Kingman S. Glaucoma is second leading cause of blindness globally. Bulletin of the

WHO. 2004 Nov; 82(11):887–8.

Allingham, “Shields' Textbook of Glaucoma”, Lippincott

Williams and Wilkins, 6th Revised edition, 1 November 2010.

Paul N. Schacknow, John R. Samples, “The Glaucoma Book: A Practical, Evidence-Based Approach to Patient Care.

Liye Guo, Ji-Jiang Yang, Lihui Peng, Jianqiang Li, Qingfeng Liang, “A computer-aided healthcare system for cataract classification and grading based on fundus image analysis.”, journal of computers industry, 2014.”, 2010.

Surya and Paul, “Wavelet based electroretinographic signal analysis for diagnosis” Journal of Biomedical Signal Proc. & Control, vol. 9, pp. 37-44, 2014.

George K. Matsopoulos, Pantelis A. Asvestas, Konstantinos K. Delibasis, Nikolaos A. Mouravliansky, Thierry G. Zeyen, “Detection of glaucomatous change based on vessel shape analysis”, journal of Computerized Medical Imaging and Graphics, vol. 32, pp. 183–192, 2008.

Muhammad Salman Haleema, Liangxiu Han, Jano van Hemert, BaihuaLi , “ Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review”, Journal of Computerized Medical Imaging and Graphics, vol. 37, pp. 581– 596, 2013.

Muthu Rama Krishnan Mookiah, U. Rajendra Acharya, Choo Min Lim, Andrea Petznick, Jasjit S. Suri, “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features.”, journal of Knowledge-Based Systems, vol. 33, pp. 73–82, 2012.

Sumeet Dua, U. Rajendra Acharya, Pradeep Chowriappa, S. VinithaSree, “ Wavelet-Based Energy Features for Glaucomatous Image Classification.”, IEE transactions on information technology in biomedicine, vol. 16, no. 1, January 2012.

J.M. Miguel-Jiméneza, L. Boquete, S. Ortega, J.M. Rodríguez-Ascariz, R. Blanco, “Glaucoma detection by wavelet-based analysis of the global flash multifocal Electroretinogram”, journal of Medical Engineering & Physics, vol. 32, pp. 617– 622, 2010.

Muhammad Salman Haleema, Liangxiu Han, Jano van Hemert, BaihuaLi , “Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review”, Journal of Computerized Medical Imaging and Graphics, vol. 37, pp. 581– 596, 2013.

Shaily Agarwal, Kalyan Acharjya, Sudhir Kr. Sharma, Shiksha Pandita, “Automatic computer aided diagnosis for early diabetic retinopathy detection and monitoring : A comprehensive review”, Online Int. Conf. on Green Engg. Tech. (IC-GET), Coimbatore, TN, India, pp. 1-7, 19 Nov. 2016.

Pooja Chaudhari, Prof. Girish A. Kulkarni, “ Review of different Image Processing Methods to Detect Glaucoma”, Int. Journal of Adv. Res. in Electronics & Communication Engg. (IJARECE), Vol. 5, Issue 6, pp. 1781-1784, Jun. 2016.

Shruti P.Y., Sharangouda N., “Automatic Retina Feature Analysis for Glaucoma Detection using Cup to Disk Ratio Based on Morphology and Hough Circle Based Techniques”, Int. Jour. of Adv. Res. in Comp. & Communication Engg., ISSN (Online) 2278-1021, ISSN (Print) 2319-5940, Vol. 4, No. 6, pp. 163-166, Jun. 2015.

ThresiammaDevasia, Poulose Jacob, Tessamma Thomas, “Automatic Computation of CDR Using Fuzzy Clustering Techniques”, Proc. of the Int. Conf. on Comp. Sci & Info. Tech. (CS & IT-CSCP 2015), Bangalore, Karnataka, India, ISBN: 978-1-921987-37-3, pp. 27-40, April 25-26, 2015.

K. Kavitha, M. Malathi, “Optic Disc and Optic Cup Segmentation for Glaucoma Classification”, Int. Journal of Advanced Research in Computer Science & Technology (IJARCST 2014), Vol. 2, Issue Special 1, pp. 87-90, Jan-March 2014.

Romuere Rodrigues Veloso e Silva, Flavio Henrique Duarte de Araujo, Luckas Moreno Rodrigues dos Santos, Rodrigo de Melo Souza Veras, Fatima Nelsizeuma, Sombra de Medeiros, “Optic Disc Detection in Retinal Images Using Algorithms Committee with Weighted Voting”, IEEE Latin America Trans., Vol. 15, No. 5, pp. 2446 – 2454, May 2016.




DOI: https://doi.org/10.23956/ijarcsse.v9i6.1029

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