Commenced in January 2007
Paper Count: 30827
Early Recognition and Grading of Cataract Using a Combined Log Gabor/Discrete Wavelet Transform with ANN and SVM
Abstract:Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2022731Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 423
 M. Vance Thompson, "What happens if cataracts are left untreated?", All About Vision, 2017. (Online). Available: http://www.allaboutvision.com/conditions/faq-cataract-lefuntreated. htm. (Accessed: 02- Apr- 2017).
 TheFreeDictionary.com, 2017. (Online). Available: http://medicaldictionary. thefreedictionary.com/brown+cataract. (Accessed: 02- Apr- 2017).
 Removal of a Mature Cataract", American Academy of Ophthalmology, 2017. (Online). Available: https://www.aao.org/eye-health/askophthalmologist- q/mature-cataract-removal. (Accessed: 02- Apr- 2017).
 WHO | Prevention of Blindness and Visual Impairment", Who.int, 2017. (Online). Available: http://www.who.int/blindness/causes/priority/en/index1.html. (Accessed: 02- Apr- 2017).
 R. Acharya U, L. Wong, E. Ng and J. Suri, "Automatic identification of anterior segment eye abnormality", IRBM, vol. 28, no. 1, pp. 35-41, 2007.
 R. Acharya, W. Yu, K. Zhu, J. Nayak, T. Lim and J. Chan, "Identification of Cataract and Post-cataract Surgery Optical Images Using Artificial Intelligence Techniques", Journal of Medical Systems, vol. 34, no. 4, pp. 619-628, 2009.
 Md. Anayet U. Patwari, Muammer D. Arif, Md. N. A. Chowdhury, A. Arefin and Md. I. Imam, "Detection, Categorization, and Assessment of Eye Cataracts Using Digital Image Processing", The First International Conference on Interdisciplinary Research and Development, Thailand, 2011.
 H. Shen, H. Hao, L. Wei and Z. Wang, "An Image Based Classification Method for Cataract", 2008 International Symposium on Computer Science and Computational Technology, 2008.
 Jagadish Nayak, "Automated Classification of Normal, Cataract and Post Cataract Optical Eye Images using SVM Classifier" Proceedings of the World Congress on Engineering and Computer Science, WCECS, San Francisco, USA, Vol I, 2013.
 L. Guo, J. Yang, L. Peng, J. Li and Q. Liang, "A computer-aided healthcare system for cataract classification and grading based on fundus image analysis", Computers in Industry, vol. 69, pp. 72-80, 2015.
 Y. N. Fuadah, A. W. Setiawan and T. L. R. Mengko, "Performing High Accuracy of The System for Cataract Detection Using Statistical Texture Analysis and K-Nearest Neighbor" International Seminar on Intelligent Technology and Its Applications, 2015.
 EyeRounds.org: Online Ophthalmic Atlas", Webeye.ophth.uiowa.edu, 2017. (Online). Available: http://webeye.ophth.uiowa.edu/eyeforum/atlas/index.htm. (Accessed: 03- Apr- 2017).
 Volume 1, Chapter 73. Cataract: Clinical Types", Oculist.net, 2017. (Online). Available: http://www.oculist.net/downaton502/prof/ebook/duanes/pages/v1/v1c07 3.html. (Accessed: 03- Apr- 2017).
 J. G. Daugman, "Two-dimensional spectral analysis of cortical receptive field profiles", Vision Research, vol. 20, no. 10, pp. 847-856, 1980.
 D. Field, "Relations between the statistics of natural images and the response properties of cortical cells", Journal of the Optical Society of America A, vol. 4, no. 12, p. 2379, 1987.
 "Log-Gabor Filters", Peterkovesi.com, 2017. (Online). Available: http://www.peterkovesi.com/matlabfns/PhaseCongruency/Docs/convexp l.html. (Accessed: 04- May- 2017).
 Bharath K and Dr. N. G. Kurahatti,"Verilog design for feature extraction using Log-Gaborfilter for desiese detection", International Journal for Technological Research in Engineering, Volume 2, Issue 9, May, 2015.
 Xiao Zhitao, Guo Chengming and Yu Ming " Research on log Gabor wavelet and its application in image edge detection", IEEE, Signal Processing 6th International Conference, 2003.
 M. Nixon and A. Aguado, Feature extraction & image processing for computer vision, 2nd ed. Amsterdam: Elsevier, Academic Press, 2013.
 Ramy Zewail, Ahmed Seif, Nadder Hamdy and Magdy Saeb,"Irisidentification based on Log-Gabor Filtering",- unpublished.
 Rajeshwari J, K. Karibasappa and Gopalkrishna M.T "S-Log: Skin based Log-Gabor Approach for Face Detection in Video" Journal of Multimedia Processing and Technologies, Volume 7, March, 2014.
 Shail Kumari Shah and Vineet Khanna "Facial Expression Recognition for Color Images using Gabor, Log Gabor Filters and PCA", International Journal of Computer Applications, Volume 113 – No. 4, March 2015.
 Dr. K. Kanagalakshmi & Dr. E. Chandra,"Log-Gabor Orientation with Run-Length Code based Fingerprint Feature Extraction Approach "in Global Journal of Computer Science and Technology, Volume 14, 2014.
 C. E. Heil and D. F. Walnut, “Continuous and Discrete Wavelet Transforms,” SIAM Review, vol. 31, no. 4, pp. 628-666, 1989.
 Jageshvar K. Keche and Mahendra P. Dhore," Facial Feature Expression Based Approach for Human Face Recognition: A Review" Vol. 1, Issue 3, IJISET - International Journal of Innovative Science, Engineering & Technology, May, 2014.
 Sylvian Fischer, Filip Sroubek, Rafael Redondo and Gabriel Cristo´ bal" Self-Invertible 2D Log-Gabor Wavelets", in International Journal of Computer Vision, January, 2007.
 "A Brief Introduction to Support Vector Machine (SVM)", 2017. (Online). Available: http://www.cs.uky.edu/~jzhang/CS689/PPDMChapter2. pdf. (Accessed: 05- May- 2017).
 U. code, U. code and S. Ray, "Understanding Support Vector Machine algorithm from examples (along with code)", Analytics Vidhya, 2017. (Online). Available: https://www.analyticsvidhya.com/blog/2015/10/understaing-supportvector- machine-example-code/. (Accessed: 03- Apr- 2017).
 B. Yekkehkhany, A. Safari, S. Homayouni and M. Hasanlou, "A comparison study of different kernel functions for svm-based classification of multi-temporal polamiterly sar data", ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. -23, pp. 281-285, 2014.
 "MATLAB - MathWorks", Mathworks.com, 2017. (Online). Available: https://www.mathworks.com/products/matlab.html. (Accessed: 26- Aug- 2017).
 "A Basic Introduction To Neural Networks", Pages.cs.wisc.edu, 2017. (Online). Available: http://pages.cs.wisc.edu/~bolo/shipyard/neural/local.html. (Accessed: 16- May- 2017).
 Zupan. Jure," Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them*", Slovenica.2017.
 "Artificial neural network", 2017. (Online). Available: http://zsi.ii.us.edu.pl/~nowak/bien/w7.pdf. (Accessed: 26- Aug- 2017).
 Anca Ignat, "Combining Features for Texture Analysis", Springer International Publishing, G. Azzopardi and N. Petkov (Eds), Part II, LNCS 9257, pp. 220–229, 2015 Switzerland.
 Salim Lahmiri and Mounir Boukadoum," Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images" in Journal of Medical Engineering,Volume 2013, Article ID 104684, March, 2013.