A Novel Threshold based Method for Vessel Intensity Detection and Extraction from Retinal Images
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 i...
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| Veröffentlicht in: | International journal of advanced computer science & applications 2021, Vol.12 (6) |
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| container_title | International journal of advanced computer science & applications |
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| creator | Fatina Wahid, Farha K, Sugandhi G, Raju Swain, Debabrata Acharya, Biswaranjan Pradhan, Manas Ranjan |
| description | 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. |
| doi_str_mv | 10.14569/IJACSA.2021.0120663 |
| format | Article |
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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.</description><identifier>ISSN: 2158-107X</identifier><identifier>EISSN: 2156-5570</identifier><identifier>DOI: 10.14569/IJACSA.2021.0120663</identifier><language>eng</language><publisher>West Yorkshire: Science and Information (SAI) Organization Limited</publisher><subject>Accuracy ; Algorithms ; Blood vessels ; Cameras ; Computer engineering ; Computer science ; Diabetic retinopathy ; Distribution functions ; Experiments ; Image processing ; Image segmentation ; Information technology ; Medical imaging ; Medical research ; Methods ; Morphology ; Neural networks ; Pattern recognition ; Retina ; Retinal images ; Sensitivity ; Veins & arteries</subject><ispartof>International journal of advanced computer science & applications, 2021, Vol.12 (6)</ispartof><rights>2021. 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| subjects | Accuracy Algorithms Blood vessels Cameras Computer engineering Computer science Diabetic retinopathy Distribution functions Experiments Image processing Image segmentation Information technology Medical imaging Medical research Methods Morphology Neural networks Pattern recognition Retina Retinal images Sensitivity Veins & arteries |
| title | A Novel Threshold based Method for Vessel Intensity Detection and Extraction from Retinal Images |
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