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    <name>Article</name>
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          <name>Title</name>
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              <text>A Novel Threshold based Method for Vessel Intensity Detection and Extraction from Retinal Images</text>
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              <text>and segmentation; blood vessel detection; cumulative distribution function introduction; hysteresis thresholding; Retinal images; segmentation</text>
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              <text>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.  2021. All Rights Reserved.</text>
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            <elementText elementTextId="120898">
              <text>Wahid F.F.; K S.; G R.; Swain D.; Acharya B.; Pradhan M.R.</text>
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              <text>International Journal of Advanced Computer Science and Applications, Vol-12, No. 6, pp. 546-554.</text>
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              <text>2021-01-01</text>
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              <text>&lt;a href="https://doi.org/10.14569/IJACSA.2021.0120663" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.14569/IJACSA.2021.0120663&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109160687&amp;amp;doi=10.14569%2FIJACSA.2021.0120663&amp;amp;partnerID=40&amp;amp;md5=5635a3600b11f7e5db4a8067119abec6" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109160687&amp;amp;doi=10.14569%2fIJACSA.2021.0120663&amp;amp;partnerID=40&amp;amp;md5=5635a3600b11f7e5db4a8067119abec6&lt;/a&gt;</text>
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          <name>Rights</name>
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            <elementText elementTextId="120903">
              <text>All Open Access; Gold Open Access</text>
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              <text>ISSN: 2158107X</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Wahid F.F., Department of Information Technology, Kannur University, Kannur, Kerala, India; K S., Department of Information Technology, Kannur University, Kannur, Kerala, India; G R., Department of Computer Science and Engineering, Faculty of Engineering, Christ (Deemed to be University), Bengaluru, Karnataka, India; Swain D., Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India; Acharya B., School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India; Pradhan M.R., School of Information Technology, Skyline University College, Sharjah, United Arab Emirates</text>
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