{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:41:57Z","timestamp":1743007317271,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030134686"},{"type":"electronic","value":"9783030134693"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-13469-3_89","type":"book-chapter","created":{"date-parts":[[2019,3,2]],"date-time":"2019-03-02T13:03:53Z","timestamp":1551531833000},"page":"766-773","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Automatic Brain Tumor Segmentation Approach Based on Affinity Clustering"],"prefix":"10.1007","author":[{"given":"C.","family":"Ramirez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V.","family":"Gomez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"I.","family":"De la Pava","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Alvarez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"Echeverry","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"Rios","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Orozco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,3,3]]},"reference":[{"issue":"11","key":"89_CR1","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta, R., et al.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274\u20132282 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"89_CR2","unstructured":"Angulakshmi, M., Priya, G.L.: Brain tumour segmentation from MRI using superpixels based spectral clustering. J. King Saud Univ.-Comput. Inf. Sci. (2018)"},{"issue":"13","key":"89_CR3","doi-asserted-by":"publisher","first-page":"R97","DOI":"10.1088\/0031-9155\/58\/13\/R97","volume":"58","author":"S Bauer","year":"2013","unstructured":"Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of mri-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97 (2013)","journal-title":"Phys. Med. Biol."},{"issue":"3","key":"89_CR4","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1177\/096228029700600302","volume":"6","author":"J Bezdek","year":"1997","unstructured":"Bezdek, J., Hall, L., Clark, M., Goldgof, D.B., Clarke, L.: Medical image analysis with fuzzy models. Stat. Method Med. Res. 6(3), 191\u2013214 (1997)","journal-title":"Stat. Method Med. Res."},{"key":"89_CR5","doi-asserted-by":"crossref","unstructured":"Boughattas, N., Berar, M., Hamrouni, K., Ruan, S.: Feature selection and classification using multiple kernel learning for brain tumor segmentation. In: 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 1\u20135. IEEE (2018)","DOI":"10.1109\/ATSIP.2018.8364470"},{"key":"89_CR6","doi-asserted-by":"crossref","unstructured":"Dueck, D., Frey, B.J.: Non-metric affinity propagation for unsupervised image categorization. In: 2007 IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1\u20138. IEEE (2007)","DOI":"10.1109\/ICCV.2007.4408853"},{"issue":"1","key":"89_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/jmri.21815","volume":"30","author":"KE Emblem","year":"2009","unstructured":"Emblem, K.E., Nedregaard, B., Hald, J.K., Nome, T., Due-Tonnessen, P., Bjornerud, A.: Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering. J. Magn. Reson. Imaging 30(1), 1\u201310 (2009)","journal-title":"J. Magn. Reson. Imaging"},{"issue":"8","key":"89_CR8","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1016\/j.mri.2013.05.002","volume":"31","author":"N Gordillo","year":"2013","unstructured":"Gordillo, N., Montseny, E., Sobrevilla, P.: State of the art survey on MRI brain tumor segmentation. Magn. Reson. Imaging 31(8), 1426\u20131438 (2013)","journal-title":"Magn. Reson. Imaging"},{"key":"89_CR9","unstructured":"Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging, 33 (2014). https:\/\/hal.inria.fr\/hal-00935640"},{"key":"89_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2017.01.036","volume":"77","author":"N Nabizadeh","year":"2017","unstructured":"Nabizadeh, N., Kubat, M.: Automatic tumor segmentation in single-spectral MRI using a texture-based and contour-based algorithm. Expert Syst. Appl. 77, 1\u201310 (2017)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"89_CR11","first-page":"226","volume":"2","author":"TU Paul","year":"2012","unstructured":"Paul, T.U., Bandhyopadhyay, S.K.: Segmentation of brain tumor from brain MRI images reintroducing k-means with advanced dual localization method. Int. J. Eng. Res. Appl. 2(3), 226\u2013231 (2012)","journal-title":"Int. J. Eng. Res. Appl."},{"key":"89_CR12","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/j.asoc.2017.04.023","volume":"57","author":"A Vishnuvarthanan","year":"2017","unstructured":"Vishnuvarthanan, A., Rajasekaran, M.P., Govindaraj, V., Zhang, Y., Thiyagarajan, A.: An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images. Appl. Soft Comput. 57, 399\u2013426 (2017)","journal-title":"Appl. Soft Comput."},{"key":"89_CR13","unstructured":"Zhao, L., Wu, W., Corso, J.J.: Brain tumor segmentation based on GMM and active contour method with a model-aware edge map. In: BRATS MICCAI, pp. 19\u201323 (2012)"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-13469-3_89","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T01:16:32Z","timestamp":1677719792000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-13469-3_89"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030134686","9783030134693"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-13469-3_89","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"3 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/atvs.ii.uam.es\/ciarp2018\/","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":"187","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":"112","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":"60% - 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":"2,94","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":"5","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}