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Automatic retinal vessel extraction algorithm based on contrast-sensitive schemes.

, , , , , and . IVCNZ, page 1-5. IEEE, (2016)

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Study of the Noise Level in the Colour Fundus Images., and . ICDS, volume 9208 of Lecture Notes in Computer Science, page 159-168. Springer, (2015)An Active Contour Model Based on Region Based Fitting Terms Driven by p-Laplace Length Regularization., , and . IEEE Access, (2018)Neural network based denoised methods for retinal fundus images and MRI brain images., and . IJCNN, page 1151-1157. IEEE, (2016)Identification of noise in the fundus images., , , , and . ICCSCE, page 191-196. IEEE, (2013)Strided U-Net Model: Retinal Vessels Segmentation using Dice Loss., , , , , and . DICTA, page 1-8. IEEE, (2018)Active Contours Using Additive Local and Global Intensity Fitting Models for Intensity Inhomogeneous Image Segmentation., , , , and . Comput. Math. Methods Medicine, (2016)Role of Image Contrast Enhancement Technique for Ophthalmologist as Diagnostic Tool for Diabetic Retinopathy., , , , and . DICTA, page 1-8. IEEE, (2016)Non-invasive contrast enhancement for retinal fundus imaging., , and . ICCSCE, page 197-202. IEEE, (2013)Retinal Blood Vessels Extraction of Challenging Images., , , , , and . AusDM, volume 996 of Communications in Computer and Information Science, page 347-359. Springer, (2018)Retinal blood vessel extraction method based on basic filtering schemes., , , and . ICIP, page 4422-4426. IEEE, (2017)