Abstract
The present work applied different image processing techniques like green component image, background estimation and image skeletonization on the subject’s fundus images. Statistical methods like fractal dimensions, neighbourhood concept was used to distinguish between normal and abnormal fundus images in subjects (n = 45). The results show that, in normal fundus images the vein structures were clearly visible, while in the fundoscopic positive images, the vein structures were totally absent. In fundoscopic negative images the visible vein structures are observed to be thick and coiled up. No significant changes were found in Fractal Dimension (FD) values among the subjects. Neighbourhood pixels (NP) values were found to be 45 ± 0.74 (mean ± S.D.) for normal subjects, 34 ± 1.01 for fundoscopic positive subjects, 20.47 ± 0.49 for fundoscopic negative subjects. The results of this work validated the skeletonized images and support the strength of diagnosis with the help of accurate figures.
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Dhananjay, B., Srinivas, M., Suman, D., Malini, M., Sivaraman, J. (2020). Fractal Dimension of Fundoscopical Retinal Images for Diagnosing of Diabetic Retinopathy. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-24322-7_4
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