Wahid, F F, Sugandhi, K, Raju, G, Debabrata, S, Biswaranjan, A and Pradhan, M R (2021) A Novel Threshold based Method for Vessel Intensity Detection and Extraction from Retinal Images. International Journal of Advanced Computer Science and Applications(IJACSA), 12 (6).
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Abstract
Retinal vessel segmentation is an active research area in medical image processing. Several research outcomes on retinal vessel segmentation have emerged in recent years. Each method has its own pros and cons, either in the vessel detection stage or in its extraction. Based on a detailed empirical investigation, a novel retinal vessel extraction architecture is proposed, which makes use of a couple of existing algorithms. In the proposed algorithm, vessel detection is carried out using a cumulative distribution function-based thresholding scheme. The resultant vessel intensities are extracted based on the hysteresis thresholding scheme. Experiments are carried out with retinal images from DRIVE and STARE databases. The results in terms of Sensitivity, Specificity, and Accuracy are compared with five standard methods. The proposed method outperforms all methods in terms of Sensitivity and Accuracy for the DRIVE data set, whereas for STARE, the performance is comparable with the best method.
Affiliation: | Skyline University College |
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SUC Author(s): | Pradhan, M R ![]() |
All Author(s): | Wahid, F F, Sugandhi, K, Raju, G, Debabrata, S, Biswaranjan, A and Pradhan, M R |
Item Type: | Article |
Uncontrolled Keywords: | Retinal images; blood vessel detection; and segmentation; segmentation; hysteresis thresholding; cumulative distribution function introduction |
Subjects: | B Information Technology > BM Artificial Intelligence |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Veeramani Rasu |
Date Deposited: | 11 Feb 2022 11:25 |
Last Modified: | 11 Feb 2022 11:25 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/78 |
Publisher URL: | https://dx.doi.org/10.14569/IJACSA.2021.0120663 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/19472 |
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