{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:38:08Z","timestamp":1742913488428,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":15,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811563140"},{"type":"electronic","value":"9789811563157"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-6315-7_18","type":"book-chapter","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T21:02:51Z","timestamp":1592946171000},"page":"217-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Segmentation of Blood Vessels from Fundus Image Using Scaled Grid"],"prefix":"10.1007","author":[{"given":"Rajat Suvra","family":"Nandy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohit Kamal","family":"Chatterjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhishek","family":"Das","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"18_CR1","unstructured":"Wong, T.Y., Klein, R., Sharrett, A.R., et al.: Retinal arteriolar narrowing and risk of coronary heart disease in men and women 287(9), 1153\u20131159. JAMA: J. Am. Med. Assoc. (2002). \nhttp:\/\/jama.amaassn.org\/content\/287\/9\/1153.abstract"},{"issue":"12","key":"18_CR2","doi-asserted-by":"publisher","first-page":"4734","DOI":"10.1167\/iovs.05-0646","volume":"46","author":"R Gelman","year":"2005","unstructured":"Gelman, R., Martinez-Perez, M.E., Vanderveen, D.K., et al.: Diagnosis of plus disease in retinopathy of prematurity using retinal image multiscale analysis. Invest. Ophthalmol. Vis. Sci. 46(12), 4734\u20134738 (2005)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"18_CR3","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.cmpb.2012.03.009","volume":"108","author":"MM Fraz","year":"2012","unstructured":"Fraz, M.M., Barman, S.A., Remagnino, P., et al.: Blood vessel segmentation methodologies in retinal images \u2013 a survey. Comput. Methods Prog. Biomed. 108, 407\u2013433 (2012)","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"2","key":"18_CR4","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.cmpb.2011.08.009","volume":"108","author":"MM Fraz","year":"2011","unstructured":"Fraz, M.M., Barman, S.A., Remagnino, P., et al.: An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput. Methods Prog. Biomed. 108(2), 600\u2013616 (2011)","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"3","key":"18_CR5","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/42.845178","volume":"19","author":"AD Hoover","year":"2000","unstructured":"Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203\u2013210 (2000)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"18_CR6","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TMI.2009.2017941","volume":"28","author":"B Al-Diri","year":"2009","unstructured":"Al-Diri, B., Hunter, A., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE Trans. Med. Imaging 28(9), 1488\u20131497 (2009)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"18_CR7","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1109\/TMI.2006.884190","volume":"25","author":"M Sofka","year":"2006","unstructured":"Sofka, M., Stewart, C.V.: Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures. IEEE Trans. Med. Imaging 25(12), 1531\u20131546 (2006)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"18_CR8","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1109\/TMI.2007.898551","volume":"26","author":"E Ricci","year":"2007","unstructured":"Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26(10), 1357\u20131365 (2007)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"18_CR9","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1109\/TITB.2010.2052282","volume":"14","author":"CA Lupascu","year":"2010","unstructured":"Lupascu, C.A., Tegolo, D., Trucco, E.: FABC: retinal vessel segmentation using AdaBoost. IEEE Trans. Inf. Technol. Biomed. 14(5), 1267\u20131274 (2010)","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Samanta, S., Saha, S.K., Chanda, B.: A simple and fast algorithm to detect the fovea region in fundus retinal image. In: Second International Conference on Emerging Applications of Information Technology, pp. 206\u2013209. IEEE Xplore (2011)","DOI":"10.1109\/EAIT.2011.22"},{"issue":"1","key":"18_CR11","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.media.2014.08.002","volume":"19","author":"G Azzopardi","year":"2015","unstructured":"Azzopardi, G., Strisciuglio, N., Vento, M., Petkov, N.: Trainable COSFIRE filters for vessel delineation with application to retinal images. Med. Image Anal. 19(1), 46\u201357 (2015)","journal-title":"Med. Image Anal."},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Kundu, A., Chatterjee, R.K.: Morphological scale-space based vessel segmentation of retinal image. In: Annual IEEE India Conference (INDICON), pp. 986\u2013990 (2012)","DOI":"10.1109\/INDCON.2012.6420760"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Mondal, R., Chatterjee, R.K., Kar, A.: Segmentation of retinal blood vessels using adaptive noise island detection. In: Fourth International Conference on Image Information Processing (ICIIP), pp. 1\u20135 (2017)","DOI":"10.1109\/ICIIP.2017.8313673"},{"key":"18_CR14","unstructured":"http:\/\/www.isi.uu.nl\/Research\/Databases\/DRIVE\/\n\n. Accessed 30 June 2012"},{"issue":"1","key":"18_CR15","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9(1), 62\u201366 (1979)","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Communications in Computer and Information Science","Machine Learning, Image Processing, Network Security and Data Sciences"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-6315-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T21:05:46Z","timestamp":1592946346000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-6315-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811563140","9789811563157"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-6315-7_18","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"15 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIND","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Image Processing, Network Security and Data Sciences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Silchar","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mind2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/mind2020.nits.ac.in\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"219","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"79","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}