The week's pick
Random Articles
Reseach Article
ANN Glaucoma Detection using Cup-to-Disk Ratio and Neuroretinal Rim
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 111 - Number 11 |
| Year of Publication: 2015 |
| Authors: Kurnika Choudhary, Shamik Tiwari |
10.5120/19581-1233
|
Kurnika Choudhary, Shamik Tiwari . ANN Glaucoma Detection using Cup-to-Disk Ratio and Neuroretinal Rim. International Journal of Computer Applications. 111, 11 ( February 2015), 8-14. DOI=10.5120/19581-1233
Abstract
Glaucoma is a disease in which the intraocular pressure is very high, causing the optic disc to become cupped with eventual everlasting impairment of vision. It is the second leading cause of permanent blindness. It cannot be cured, but its progression can be slowed down by treatment in early stage. Therefore, detecting glaucoma in time is crucial. In this paper glaucoma is classified by extracting two features using retinal fundus images. (i) Cup to Disc Ratio (CDR). (ii) Ratio of Neuroretinal Rim in inferior, superior, temporal and nasal quadrants that is to say ISNT quadrants. Glaucoma frequently damages superior and inferior fibers before temporal and nasal optic nerve fibers and which start decreasing the superior and inferior rims areas and change the order of ISNT rule. Hence, the detection of rim areas in four directions can assist the correct verification of ISNT rule and then improve the correct diagnosis of glaucoma at early stages. In the end, feed forward back propagation neural network is used for classification based on the above two features. The tool used to accomplish the objective is MATLAB R2013a. The average accuracy of the system is around 96%. The method does not rely on trained glaucoma specialists or specialized and costly OCT/HRT machines. Several fundus retinal images containing normal and glaucoma were applied to the proposed method for demonstration.
References
- WHO: Glaucoma bulletin 2011 http://www. who. int/bulletin/volumes/82/11/feature1104/en/, 2011.
- 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 International Conference 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 Opas Chutatape. "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: Pearson Prentice Hall, 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).
- High Resolution Fundus Image database https://www5. cs. fau. de/research/data/fundus-images/
- Opticdisc. org Database http://www. optic-disc. org/library/normal-discs/page7. html
- DRIONS-DB: Digital Retinal Images for optic Nerve Segmentation Database http://www. ia. uned. es/~ejcarmona/DRIONS-DB. html
- Haykin, Simon, and Neural Network. "A comprehensive foundation. " Neural Networks 2. 2004 (2004).
- Tiwari, Shamik, Vidya Prasad Shukla, Sangappa Biradar, 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
Index Terms
Keywords