Distinguising Proof of Diabetic Retinopathy Detection by Hybrid Approaches in Two Dimensional Retinal Fundus Images
Diabetes is characterized by constant high level of blood glucose. The human body needs to maintain insulin at very constrict range. The patients who are all affected by diabetes for a long time affected by eye disease called Diabetic Retinopathy (DR). The retinal landmarks namely Optic disc is predicted and masked to decrease the false positive in the exudates detection. The abnormalities like Exudates, Microaneurysms and Hemorrhages are segmented to classify the various stages of DR. The proposed approach is employed to separate the landmarks of retina and lesions of retina for the classification of stages of DR. The segmentation algorithms like Gabor double-sided hysteresis thresholding, maximum intensity variation, inverse surface adaptive thresholding, multi-agent approach and toboggan segmentation are used to detect and segment BVs, ODs, EXs, MAs and HAs. The feature vector formation and machine learning algorithm used to classify the various stages of DR are evaluated using images available in various retinal databases, and their performance measures are presented in this paper.
KeywordsOptic Disc Diabetic Retinopathy Hemorrhages Blood Vessels Microaneurysms Image Processing
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Conflict of Interest
This paper has not communicated anywhere till this moment, now only it is communicated to your esteemed journal for the publication with the knowledge of all co-authors.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 5.Acharya, U. R., Mookiah, M. R. K., Koh, J. E., Tan, J. H., Bhandary, S. V., Rao, A. K., Fujita, H., Hagiwara, Y., Chua, C. K., and Laude, A., Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index. Computers in biology and medicine 75:54–62, 2016.CrossRefGoogle Scholar
- 9.Mookiah, M. R. K., Acharya, U. R., Martis, R. J., Chua, C. K., Lim, C. M., Ng, E. Y. K., and Laude, A., Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach. Knowledge-based systems 39:9–22, 2013.CrossRefGoogle Scholar
- 15.Kauppi, T, Kalesnykiene, V, Kamarainen, JK, Lensu, L, Sorri, I, Uusitalo, H, Kälviäinen, H & Pietilä, J 2006, 'DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms', Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology, Finland, pp.134.Google Scholar
- 16.Kauppi, T, Kalesnykiene, V, Kamarainen, J.-K, Lensu, L, Sorri, I, Raninen A, Voutilainen R, Uusitalo, H, Kälviäinen, H & Pietilä, J 2007, 'DIARETDB1 diabetic retinopathy database and evaluation protocol, in proceedings of the eleventh conference on Medical Image Understanding and Analysis, pp.1-10.Google Scholar
- 20.Acharya, U. R., Mookiah, M. R. K., Koh, J. E., Tan, J. H., Noronha, K., Bhandary, S. V., Rao, A. K., Hagiwara, Y., Chua, C. K., and Laude, A., Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features. Computers in biology and medicine 73:131–140, 2016.CrossRefGoogle Scholar