{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T05:01:22Z","timestamp":1772859682737,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031299698","type":"print"},{"value":"9783031299704","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-29970-4_3","type":"book-chapter","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T14:06:26Z","timestamp":1682517986000},"page":"28-39","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Brain Blood Vessel Segmentation in\u00a0Hyperspectral Images Through Linear Operators"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5821-0877","authenticated-orcid":false,"given":"Guillermo","family":"Vazquez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7000-6289","authenticated-orcid":false,"given":"Manuel","family":"Villa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4715-6814","authenticated-orcid":false,"given":"Alberto","family":"Mart\u00edn-P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8767-6596","authenticated-orcid":false,"given":"Jaime","family":"Sancho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3236-1236","authenticated-orcid":false,"given":"Gonzalo","family":"Rosa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9395-5807","authenticated-orcid":false,"given":"Pedro L.","family":"Cebri\u00e1n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5731-5199","authenticated-orcid":false,"given":"Pallab","family":"Sutradhar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2668-2903","authenticated-orcid":false,"given":"Alejandro Martinez de","family":"Ternero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0280-3440","authenticated-orcid":false,"given":"Miguel","family":"Chavarr\u00edas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3996-0554","authenticated-orcid":false,"given":"Alfonso","family":"Lagares","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6096-1511","authenticated-orcid":false,"given":"Eduardo","family":"Juarez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2411-9132","authenticated-orcid":false,"given":"C\u00e9sar","family":"Sanz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"issue":"1","key":"3_CR1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JBO.19.1.010901","volume":"19","author":"G Lu","year":"2014","unstructured":"Lu, G., Fei, B.: Medical hyperspectral imaging: a review. J. Biomed. Opt. 19(1), 010901 (2014). https:\/\/doi.org\/10.1117\/1.JBO.19.1.010901","journal-title":"J. Biomed. Opt."},{"key":"3_CR2","doi-asserted-by":"publisher","unstructured":"Leon, R., et al.: Hyperspectral imaging for in-vivo\/ex-vivo tissue analysis of human brain cancer. In: Linte, C.A., Siewerdsen, J.H. (eds.) Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 12034, p. 1203429. International Society for Optics and Photonics, SPIE (2022). https:\/\/doi.org\/10.1117\/12.2611420","DOI":"10.1117\/12.2611420"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Urbanos, G., et al.: Supervised machine learning methods and hyperspectral imaging techniques jointly applied for brain cancer classification. Sensors 21(11) (2021). www.mdpi.com\/1424-8220\/21\/11\/3827","DOI":"10.3390\/s21113827"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Goni, M.R., Ruhaiyem, N.I.R., Mustapha, M., Achuthan, A., Che Mohd Nassir, C.M.N.: Brain vessel segmentation using deep learning-a review. IEEE Access 10, 111322\u2013111336 (2022)","DOI":"10.1109\/ACCESS.2022.3214987"},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"7192","DOI":"10.1109\/TIP.2020.2999854","volume":"29","author":"A Nazir","year":"2020","unstructured":"Nazir, A., et al.: OFF-eNET: an optimally fused fully end-to-end network for automatic dense volumetric 3d intracranial blood vessels segmentation. IEEE Trans. Image Process. 29, 7192\u20137202 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"8041","DOI":"10.1088\/0031-9155\/58\/22\/8041","volume":"58","author":"D Babin","year":"2013","unstructured":"Babin, D., Pizurica, A., De Vylder, J., Vansteenkiste, E., Philips, W.: Brain blood vessel segmentation using line-shaped profiles. Phys. Med. Biol. 58, 8041\u20138061 (2013)","journal-title":"Phys. Med. Biol."},{"issue":"10","key":"3_CR7","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"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: Blood vessel segmentation from low-contrast and wide-field optical microscopic images of cranial window by attention-gate-based network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1864\u20131873 (2022)","DOI":"10.1109\/CVPRW56347.2022.00203"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Fabelo, H., et al.: Deep learning-based framework for in vivo identification of glioblastoma tumor using hyperspectral images of human brain. Sensors 19(4) (2019). www.mdpi.com\/1424-8220\/19\/4\/920","DOI":"10.3390\/s19040920"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation (2015). arxiv.org\/abs\/1505.04597","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101980","volume":"69","author":"J Li","year":"2021","unstructured":"Li, J., Udupa, J., Tong, Y., Wang, L., Torigian, D.: Segmentation evaluation with sparse ground truth data: simulating true segmentations as perfect\/imperfect as those generated by humans. Med. Image Anal. 69, 101980 (2021)","journal-title":"Med. Image Anal."},{"key":"3_CR12","doi-asserted-by":"publisher","unstructured":"Villa, M., et al.: Data-type assessment for real-time hyperspectral classification in medical imaging. n: Desnos, K., Pertuz, S. (eds.) Design and Architecture for Signal and Image Processing, pp. 123\u2013135. Springer International Publishing (2022). https:\/\/doi.org\/10.1007\/978-3-031-12748-9_10","DOI":"10.1007\/978-3-031-12748-9_10"},{"issue":"4","key":"3_CR13","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501\u2013509 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abr\u00e0moff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: SPIE Medical Imaging (2004)","DOI":"10.1117\/12.535349"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Eklund, A., Dufort, P., Forsberg, D., LaConte, S.M.: Medical image processing on the GPU - Past, present and future. Med. Image Anal. 17(8), 1073\u20131094 (2013). www.sciencedirect.com\/science\/article\/pii\/S1361841513000820","DOI":"10.1016\/j.media.2013.05.008"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Wang, X., Shi, B.E.: Gpu implemention of fast Gabor filters. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 373\u2013376 (2010)","DOI":"10.1109\/ISCAS.2010.5537757"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework (2019). arxiv.org\/abs\/1907.10902","DOI":"10.1145\/3292500.3330701"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Martin-Perez, A.: Hyperparameter optimization for brain tumor classification with hyperspectral images. In: 5th Euromicro Conference on Digital System Design (DSD) (2022)","DOI":"10.1109\/DSD57027.2022.00117"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Srinivas, P., Katarya, R.: hyoptxg: optuna hyper-parameter optimization framework for predicting cardiovascular disease using xgboost. Biomed. Signal Process. Control 73, 103456 (2022). www.sciencedirect.com\/science\/article\/pii\/S1746809421010533","DOI":"10.1016\/j.bspc.2021.103456"}],"container-title":["Lecture Notes in Computer Science","Design and Architecture for Signal and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-29970-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T14:06:49Z","timestamp":1682518009000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-29970-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031299698","9783031299704"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-29970-4_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Design and Architecture for Signal and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toulouse","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 January 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 January 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dasip23.citsem.upm.es\/","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":"17","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":"9","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":"0","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":"53% - 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":"1.4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}