Automatic computer vision-based detection and quantitative analysis of indicative parameters for grading of diabetic retinopathy
Diabetic retinopathy (DR) is one of the complications of diabetes affecting the eyes. If not treated at an early stage, then it can cause permanent blindness. The present work proposes a method for automatic detection of pathologies that are indicative parameters for DR and use them strategically in a framework to grade the severity of the disease. The bright lesions are highlighted using a normalization process followed by anisotropic diffusion and intensity threshold for detection of lesions which makes the algorithm robust to correctly reject false positives. SVM-based classifier is used to reject false positives using 10 distinct feature types. Red lesions are accurately detected from a shade-corrected green channel image, followed by morphological flood filling and regional minima operations. The rejection of false positives using geometrical features makes the system less complex and computationally efficient. A comprehensive quantitative analysis to grade the severity of the disease has resulted in an average sensitivity of 92.85 and 86.03% on DIARETDB1 and MESSIDOR databases, respectively.
KeywordsFundus images Diabetic retinopathy Optic disc Bright lesions Red lesions Mathematical morphology Classification Grading
This work was supported in part by the Grants from Department of Science and Technology, No. DST/TSG/ICT/2013/37. Also, the authors express their thankfulness to Dr. S. C. Gupta, Medical Director of Venu Eye Research Centre, for his kind support.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 6.Sengar N, Dutta MK (2017) Automated system for detection and hierarchal grading of diabetic retinopathy. In: Computer methods in biomechanics and biomedical engineering: imaging and visualization. Taylor & Francis Publishers, pp 1–11Google Scholar
- 9.Parthasarathi M, Dutta MK, Singh A, Travieso CM (2016) Blood vessel inpainting based technique for efficient localization and segmentation of optic disc in digital fundus images. Biomed Signal Process Control 25:108–117. ISSN 1746-8094. https://doi.org/10.1016/j.bspc.2015.10.012
- 11.Yadav D, Sarathi MP, Dutta MK (2014) Classification of glaucoma based on texture features using neural networks. In: 7th international conference on contemporary computing (IC3), Noida, 2014, pp 109–112Google Scholar
- 14.Ganguly S et al (2014) An adaptive threshold based algorithm for detection of red lesions of diabetic retinopathy in a fundus image. In: International conference on medical imaging, m-health and emerging communication systems (MedCom), Greater Noida, 2014, pp 91–94Google Scholar
- 18.Liu J et al (2009) Photometric correction of retinal images by polynomial interpolation. In: 16th IEEE international conference on image processing (ICIP), Cairo, 2009, pp 3893–3896Google Scholar
- 20.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 11th conference on medical image understanding and analysis, Aberystwyth, Wales, 2007Google Scholar
- 21.Decenciere E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A, Charton B, Klein JC (2014) Feedback on a publicly distributed database: the Messidor database. Image Anal Stereol 33(3):231–234. ISSN 1854-5164Google Scholar