Automated Glaucoma Diagnosis System Based on Fundus Images Features
Article Sidebar
-
CDR (Cup to Disc Ratio), Fundus Images, Glaucoma, ISNT Ratio, KNN (k-Nearest Neighbor), SVM (Support Vector Machine)
Abstract
Glaucoma is a disease that can damage the eye’s optic nerve and cause permanent vision loss or even total blindness if not detected in early stage and thus, it is important diagnose early to prevent blindness. Image processing techniques has been used to detect early glaucoma using the fundus images. In this paper, a computer aided glaucoma diagnosis system is proposed based on fundus image features. The optic disc and optic cup features are extracted from funds images and two parameters are calculated, namely: Cup to Disc Ratio (CDR) which indicates the enlargement of cup and the Inferior Superior to Nasal Temporal (ISNT) which determine the ratio of the thickness of the rim. Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) classifiers are used to classify images into "Normal" or "Abnormal". The quality of both classifiers is evaluated and compared in terms of three performance metrics, namely: sensitivity, specificity and accuracy and satisfactory results have been achieved where the system accuracy is 95%. Moreover; Patient data such as age, family history of the disease, eye pressure and the last eye examination record were taken in account. The proposed system can categorize images into five categories: "no risk", "low risk", "moderate risk", "high risk" and "very high risk".
Full text article
References
[1]- M. N. Madhusudhan M., Nirmala S.R., Samerendra D. , "Image Processing Techniques for Glaucoma Detection," Communications in Computer and Information Science, vol. 192, pp. 365-373, 2011.
[2]- C. B. Anusorn, W. Kongprawechnon, T. Kondo, S. Sintuwong, and K. Tungpimolrut, "Image processing techniques for glaucoma detection using the cup-to-disc ratio," Science & Technology Asia, vol. 18, pp. 22-34, 2013.
[3]- O. Sheeba, J. George, P. Rajin, N. Thomas, and S. George, "Glaucoma detection using artificial neural network," International Journal of Engineering and Technology, vol. 6, p. 158, 2014.
[4]- A. Murthi and M. Madheswaran, "Enhancement of optic cup to disc ratio detection in glaucoma diagnosis," in Computer Communication and Informatics (ICCCI), 2012 International Conference on, 2012, pp. 1-5.
[5]- A. Aquino, M. E. Gegúndez-Arias, and D. Marín, "Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques," IEEE transactions on medical imaging, vol. 29, pp. 1860-1869, 2010.
[6]- M. Fingeret, F. A. Medeiros, R. Susanna, and R. N. Weinreb, "Five rules to evaluate the optic disc and retinal nerve fiber layer for glaucoma," Optometry-Journal of the American Optometric Association, vol. 76, pp. 661-668, 2005.
[7]- Y. Hagiwara, J. E. W. Koh, J. H. Tan, S. V. Bhandary, A. Laude, E. J. Ciaccio, et al., "Computer-aided diagnosis of glaucoma using fundus images: A review," Computer Methods and Programs in Biomedicine, vol. 165, pp. 1-12, 2018/10/01/ 2018.
[8]- S. S. Kanse and D. M. Yadav, "Retinal Fundus Image for Glaucoma Detection: A Review and Study," Journal of Intelligent Systems, vol. 28, pp. 43-56, 2019.
[9]- K.Narasimhan and Dr.K.Vijayarekha, "An efficient automated system for glaucoma detection using fundus image," Theoretical and Applied Information Technology, vol. 33, pp. 104-110, 2011.
[10]- D. S and R. M. Nair, "A Novel Approach towards Automatic Glaucoma Assessment," International Journal of Scientific Research Engineering & Technology (IJSRET), vol. 3, pp. 281-288, 2014.
[11]- J. Kim¹, B. Kim, and S. Savarese, "Comparing image classification methods: K-nearest-neighbor and support-vector-machines," in Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics, 2012, pp. 48109-2122.
[12]- N. Annu and J. Justin, "Automated classification of glaucoma images by wavelet energy features," International Journal of Engineering and Technology, vol. 5, pp. 1716-1721, 2013.
Authors
Copyright (c) 2021 Journal of Pure & Applied Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
In a brief statement, the rights relate to the publication and distribution of research published in the journal of the University of Sebha where authors who have published their articles in the journal of the university of Sebha should how they can use or distribute their articles. They reserve all their rights to the published works, such as (but not limited to) the following rights:
- Copyright and other property rights related to the article, such as patent rights.
- Research published in the journal of the University of Sebha and used in its future works, including lectures and books, the right to reproduce articles for their own purposes, and the right to self-archive their articles.
- The right to enter a separate article, or for a non-exclusive distribution of their article with an acknowledgment of its initial publication in the journal of Sebha University.
Privacy Statement The names and e-mail addresses entered on the Sabha University Journal site will be used for the aforementioned purposes only and for which they were used.