Abstract
Automatic retinal vessel segmentation has turned out to be highly propitious for medical practitioners to diagnose diseases like glaucoma and diabetic retinopathy. These diseases are classified based on the thickness of the retinal vessel, the pressure imposed on the nerve endings and optical disc to cup ratio of the retina. The state-of-the-art device for this purpose presently available in the market is expensive and has scope to meliorate sensitivity and precision of its performance. Thus, automatic retinal blood vessel segmentation and classification is the need of the hour. In this paper, a novel non-local total variational retinex based retinal image preprocessing approach is proposed to extract the retinal vessel features and classify the vessels using ground truth images. Matlab implementation results indicate that an average accuracy of 94% with an acceptable range of sensitivity and specificity could be achieved on the retinal image database available online .
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Notes
- 1.
Local gradient \(|\nabla u_u| = \sqrt{u_{ux}^2 + u_{uy}^2}\) is transformed into weberized TV given as \(|\nabla u_u|_w = \frac{|\Delta u_u|}{u_u}\) [17].
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Acknowledgements
Dr. Jidesh and Ms. Febin would like to thank the Science and Engineering Research Board, India for providing financial support under the Project Grant No. ECR/2017/000230. Ms. Smitha expresses her gratitude to the Ministry of Human Resource, Government of India for providing financial support for pursuing Ph.D. at National Institute of Technology Karnataka, Surathkal. Furthermore, the authors acknowledge the authors of [4,5,6] for making the dataset available for testing and providing the Matlab code for comparison purpose.
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Smitha, A., Jidesh, P., Febin, I.P. (2020). Retinal Vessel Classification Using the Non-local Retinex Method. In: Tiwary, U., Chaudhury, S. (eds) Intelligent Human Computer Interaction. IHCI 2019. Lecture Notes in Computer Science(), vol 11886. Springer, Cham. https://doi.org/10.1007/978-3-030-44689-5_15
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