International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.13, No.2, Apr. 2021

Statistical Texture Features Based Automatic Detection and Classification of Diabetic Retinopathy

Full Text (PDF, 519KB), PP.53-61

Views:3   Downloads:0


Md. Rahat Khan, A. S. M. Shafi

Index Terms

Diabetes, Diabetic Retinopathy, Run Length Matrix, Image Classification


Diabetes is a globally prevalent disease that can cause microvascular compilation such as Diabetic Retinopathy (DR) in the human eye organs and it might prompt a significant reason for visual deficiency. The present study aimed to develop an automatic detection and classification system to diagnosing diabetic retinopathy from digital fundus images. An automated diabetic retinopathy detection and classification system from retinal images is proposed in our work to reduce the workload of ophthalmologists. This work comprises three main stages. Our proposed method first extracts the blood vessels from color fundus image. Secondly, the method detects whatever the input image as normal or diabetic retinopathy and then illustrates an automatic diabetic retinopathy classification technique through statistical texture features. It embeds Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) for second-order and higher-order statistical texture feature as a feature extraction technique into three renowned classifiers namely K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM). The evaluation results containing a dataset of 644 retinal images indicate that the proposed method based on random forest classifier is found to be effective with a weighted sensitivity, precision, F1-score and accuracy of 95.53% 96.45%, 95.38% and 95.19% respectively for the detection and classification of diabetic retinopathy. These outcomes propose, that the method could decrease the cost of screening and diagnosis while achieving higher than suggested performance and that the system could be implemented in clinical assessments requiring better evaluating.

Cite This Paper

Md. Rahat Khan, A. S. M. Shafi, " Statistical Texture Features Based Automatic Detection and Classification of Diabetic Retinopathy", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.13, No.2, pp. 53-61, 2021.DOI: 10.5815/ijigsp.2021.02.05


[1]D. Fong, L. Aiello, T. Gardner, G. King, G. Blankenship, et al. “Retinopathy in Diabetes”. Diabetes Care. Vol. 27. 2004. pp. 584-587.

[2]D. Browning, “Diabetic Retinopathy: Evidence Based Management”, 1st ed. Ed. Springer, New York, USA, 2010, pp. 31-61.

[3]BoserB ,Guyon I.G,Vapnik V., "A Training Algorithm for Optimal Margin Classifiers", Proc. Fifth Ann. Workshop Computational Learning Theory, pp. 144-152, 1992. 

[4]Nathan Silberman, Kristy Ahlrich, Rob Fergus & Lakshminarayanan Subramanian (2010), "Association for the Advancement of Artificial Intelligence", October, 2013.

[5]Fong. D. S. , L. Aiello, T. W. Gardner, G. L. King, G. Blankenship, J. D. Cavallerano, F. L. Ferris & R. Klein, (2003),"Diabetic Retinopathy”, Diabetes Care, Vol. 26, pp. 226–229.

[6]Velázquez-González, Jesús Salvador, Rosales-Silva, Alberto Jorge, Gallegos-Funes, Francisco Javier, & Guzmán-Bárcenas, Guadalupe de Jesús. (2015), “Detection and Classification of Non-Proliferative Diabetic Retinopathy using a Back-Propagation Neural Network”, Revista Facultad de Ingeniería Universidad de Antioquia, (74), 70-85.

[7]L. Daniel Maxim, Ron Niebo, “Screening tests: a review with examples”, Inhal Toxicol. 2014 Nov; 26(13): 811–828. 

[8]Khademi and S. Krishnan, “Shift-invariant discrete wavelet transform analysis for retinal image classification,” Medical and Biological Engineering and Computing, vol. 45, issue 12, pp. 1211–1222, 2007. 

[9]Neto, L.C.; Ramalho, G.L.; Neto, J.F.R.; Veras, R.M.; Medeiros, F.N., “An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images”, Expert System with Application, Vol. 78, pp. 182–192, 2017. 

[10]Sarathi, M.P. Dutta, M.K. Singh, A. Travieso, C.M., “Blood vessel in painting based technique for efficient localization and segmentation of optic disc in digital fundus images”, Biomedical Signal Processing, Vol. 25, pp. 108–117, 2016. 

[11]García G., Gallardo J., Mauricio A., López J., Del Carpio C., “Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images”, In Lecture Notes in Computer Science, Proceedings of the Artificial Neural Networks and Machine Learning—ICANN, vol. 10614, pp. 635–642, 2017.

[12]Matthew N. Dailey, Bunyarit Uyyanonvara, Sarah Barman, Tom Williamson, Khine Thet Nwe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients”, Tailor and Francis online  Volume 57, 2010.

[13]Rajendra Acharya U.,  E. Y. Ng, Kwan-Hoong Ng, “Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images”, Journal of Medical Systems, Volume 36, No. 1, February 2012,

[14]Varun G., Lily P., Mark C., “Development and validation of a deep learning Algorithm for Detection of Diabetic Retinopathy”, November 2016, JAMA The Journal of the American Medical Association 316(22), doi:  10.1001/jama.2016.17216

[15]Maliha M., Tareque A. and Roy S. S. 2018 Diabetic Retinopathy Detection Using Machine Learning 

[16]Imani, E., Pourreza, H.R., Banaee, T, “Fully automated diabetic retinopathy screening using morphological component analysis”, Comput. Med. Imaging Graph. March 2015, 43, 78–88, doi: 10.1016/j.compmedimag.2015.03.004

[17]Goh, J., Tang, L., Saleh, G., Al Turk, L.; Fu, Y., Browne, A, “Filtering normal retinal images for diabetic retinopathy screening using multiple classifiers”, In Proceedings of the 9th International Conference on Information Technology and Applications in Biomedicine, December 2009, doi: 10.1109/ITAB.2009.5394392

[18]Qureshi, I., Ma, J., & Abbas, Q. (2019), “Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy”, Symmetry, 11(6), 749. doi:10.3390/sym11060749 

[19]Rahim, S. S., Palade, V., Shuttleworth, J., & Jayne, C. (2016), “Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing”, Brain Informatics, 3(4), 249–267, doi: 10.1007/s40708-016-0045-3.

[20]Manjaramkar, A., & Kokare, M. (2017), “Statistical Geometrical Features for Microaneurysm Detection”, Journal of Digital Imaging, 31(2), 224–234, doi: 10.1007/s10278-017-0008-0.

[21]Bin Zheng, PhD, Xingwei Wang, PhD, Dror Lederman, PhD, Jun Tan, “Computer-Aided Detection – The Effect of Training Databases on Detection of Subtle Breast Masses”, 2010 Nov, 17(11): 1401–1408.

[22]H.S. Bhadauria (2013), “Vessels Extraction from Retinal Images”, IOSR J. Electron. Commun. Eng. 6 79–82

[23]T. Jemima Jebaseeli, C. Anand Deva Durai,1 and J. Dinesh Peter, “Extraction of Retinal Blood Vessels on Fundus Images by Kirsch's Template and Fuzzy C-Means”, J Med Phys, 2019 Jan-Mar; 44(1): 21–26, doi: 10.4103/jmp.JMP_51_18

[24]Good, P.I. (2006), “Resampling methods: A practical guide to data analysis”, 3rd Edition, Birkhauser

[25]Sapiah Binti Sakri, Nuraini Binti Abdul Rashid, and Zuhaira Muhammad Zain, “Particle Swarm Optimization Feature Selection for Breast Cancer Recurrence Prediction”, Special Section on Big Data Learning and Discovery, IEEE Access, DOI 10.1109/ACCESS.2018.2843443.

[26]Gandhi M, Dhanasekaran R, “Diagnosis of Diabetic Retinopathy Using Morphological Process and SVM Classifier”, IEEE International conference on Communication and Signal Processing, 2013, India pp: 873-877.

[27]Cuong Nguyen, Yong Wang, Ha Nam Nguyen, “Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic”, J. Biomedical Science and Engineering, 2013, 6, 551-560.

[28]Javit JC. Cost-savings associated with detection and treatment of diabetic eye disease. PharmacoEconomics 1995; 8:33–39.