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Reseach Article

Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques

by R. Geetha Ramani, Sugirtharani S., Lakshmi B.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 166 - Number 8
Year of Publication: 2017
Authors: R. Geetha Ramani, Sugirtharani S., Lakshmi B.
10.5120/ijca2017914130

R. Geetha Ramani, Sugirtharani S., Lakshmi B. . Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques. International Journal of Computer Applications. 166, 8 ( May 2017), 38-43. DOI=10.5120/ijca2017914130

@article{ 10.5120/ijca2017914130,
author = { R. Geetha Ramani, Sugirtharani S., Lakshmi B. },
title = { Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { May 2017 },
volume = { 166 },
number = { 8 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = { 38-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume166/number8/27692-2017914130/ },
doi = { 10.5120/ijca2017914130 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:13:11.548294+05:30
%A R. Geetha Ramani
%A Sugirtharani S.
%A Lakshmi B.
%T Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 166
%N 8
%P 38-43
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computational techniques are highly used in medical image analysis to aid the medical professionals. Glaucoma is a sight threatening retinal disease that needs attention at its early stages, though it does not reveal any symptoms. Glaucoma is identified usually through cup to disc ratio and ISNT rule. This work involves segmentation of blood vessels, segmentation of optic disc through proposed maximum voting of three segmentation algorithms (K-Means, Wavelet and Histogram based), segmentation of optic cup through intensity thresholding, feature extraction from these segmented structures, feature selection to identify siginificant features, hybrid model involving Naive Bayes to remove noise in data followed by ensemble classification of Reduced Error Pruning Tree. Optic disc segmentation methodology attains an average accuracy of 99.33%. Glaucoma detection accuracy reaches a maximum of 96.42%.

References
  1. Han, J., Pei, J. and Kamber, M., 2011. Data mining: concepts and techniques. Elsevier.
  2. Ramani, R.G., Lakshmi, B. and Jacob, S.G., 2012. Automatic prediction of Diabetic Retinopathy and Glaucoma through retinal image analysis and data mining techniques. In Machine Vision and Image Processing (MVIP), 2012 International Conference on (pp. 149-152). IEEE.
  3. Glaucoma Tutorial (available at) http://www.optic-disc.org/tutorials/glaucoma_evaluation_basics/page13.html
  4. Nayak, J., Acharya, R., Bhat, P.S., Shetty, N. and Lim, T.C., 2009. Automated diagnosis of glaucoma using digital fundus images. Journal of medical systems, 33(5), p.337.
  5. Acharya, U.R., Dua, S., Du, X. and Chua, C.K., 2011. Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE transactions on Information Technology in Biomedicine, 15(3), pp.449-455.
  6. Babu, T.G. and Shenbagadevi, S., 2011. Automatic detection of glaucoma using fundus image. European Journal of Scientific Research, 59(1), pp.22-32.
  7. Ho, C.Y., Pai, T.W., Chang, H.T. and Chen, H.Y., 2011. An atomatic fundus image analysis system for clinical diagnosis of glaucoma. In Complex, Intelligent and Software Intensive Systems (CISIS), 2011 International Conference on (pp. 559-564). IEEE.
  8. GeethaRamani, R, Dhanapackiam, C and Lakshmi, B. 2013. Automatic Detection of Glaucoma in Fundus Images through Image Features, International conference on Knowledge Modelling and Knowledge Management, pp. 135-144.
  9. Vijapur, N., Srinivasa, R. and Rao, K., 2014. Improved Efficiency of Glaucoma Detection by using Wavelet Filters Prediction and Segmentation Method. International journal of electronics, Electrical and Computational System, 3(8).
  10. Salam, A.A., Khalil, T., Akram, M.U., Jameel, A. and Basit, I., 2016. Automated detection of glaucoma using structural and non structural features. SpringerPlus, 5(1), p.1519.
  11. Prakash, N.B. and Selvathi, D., 2017. An Efficient Detection System for Screening Glaucoma in Retinal Images. Biomedical and Pharmacology Journal, 10(1), pp.459-465.
  12. Ramani, R.G. and Lakshmi, B., 2015. Automatic retinal vessel segmentation through Gabor filtering, principal component analysis and ensemble of classifiers (C4. 5 with bagging). Advances in Natural and Applied Sciences, 9(6 SE), pp.600-607.
  13. Hariprasath, S. and Mohan, V., 2009. Iris pattern recognition using complex wavelet and wavelet packet transform. Journal of Computer Applications, 2(2).
  14. Lloyd, S., 1982. Least squares quantization in PCM. IEEE transactions on information theory, 28(2), pp.129-137.
  15. GeethaRamani, R. and Lakshmi, B., 2016. Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybernetics and Biomedical Engineering, 36(1), pp.102-118.
  16. Lai, Y.K. and Kuo, C.C.J., 1997, October. Image quality measurement using the Haar wavelet. In Optical Science, Engineering and Instrumentation'97 (pp. 127-138). International Society for Optics and Photonics.
  17. Dehghani, A., Moghaddam, H.A. and Moin, M.S., 2012. Optic disc localization in retinal images using histogram matching. EURASIP Journal on Image and Video Processing, 2012(1), p.19.
  18. Pizer, S.M., Johnston, R.E., Ericksen, J.P., Yankaskas, B.C. and Muller, K.E., 1990, May. Contrast-limited adaptive histogram equalization: speed and effectiveness. In Visualization in Biomedical Computing, 1990., Proceedings of the First Conference on (pp. 337-345). IEEE.
  19. Geetha Ramani, R. and Lakshmi, B., 2013. Multi-class classification for prediction of retinal diseases (retinopathy and occlusion) from fundus images. Proceedings of ICKM, 13, pp.122-134.
  20. Haralick, R.M. and Shanmugam, K., 1973. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, 3(6), pp.610-621.
  21. Hall, M.A., 1999. Correlation-based feature selection for machine learning (Doctoral dissertation, The University of Waikato).
  22. Tang, J., Alelyani, S. and Liu, H., 2014. Feature selection for classification: A review. Data Classification: Algorithms and Applications, p.37.
  23. Ramani, R.G., Lakshmi, B. and Alaghu Meenal, A., 2015. A hybrid classification model employing Genetic algorithm and Root Guided Decision Tree for improved categorization of data. ARPN Journal of Engineering and Applied Sciences, 10(21), pp.9968-9975.
  24. Zhao, Y. and Zhang, Y., 2008. Comparison of decision tree methods for finding active objects. Advances in Space Research, 41(12), pp.1955-1959.
  25. GOLD standard database (available at) https://www5.cs.fau.de/research/data/fundus-images/
Index Terms

Computer Science
Information Sciences

Keywords

Glaucoma Retina Fundus Image Optic disc Maximum voting hybrid classification