{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:01:04Z","timestamp":1760122864386,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030210762"},{"type":"electronic","value":"9783030210779"}],"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\/978-3-030-21077-9_20","type":"book-chapter","created":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T23:14:41Z","timestamp":1560899681000},"page":"216-226","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Non-parametric Brain Tissues Segmentation via a Parallel Architecture of CNNs"],"prefix":"10.1007","author":[{"given":"Dante","family":"M\u00fajica-Vargas","sequence":"first","affiliation":[]},{"given":"Alicia","family":"Mart\u00ednez","sequence":"additional","affiliation":[]},{"given":"Manuel","family":"Matuz-Cruz","sequence":"additional","affiliation":[]},{"given":"Antonio","family":"Luna-Alvarez","sequence":"additional","affiliation":[]},{"given":"Mildred","family":"Morales-Xicohtencatl","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,18]]},"reference":[{"key":"20_CR1","unstructured":"Angulakshmi, M., Priya, G.L.: Brain tumour segmentation from MRI using superpixels based spectral clustering. J. King Saud Univ.-Comput. Inf. Sci. (2018). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1319157817303476"},{"key":"20_CR2","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.compbiomed.2018.02.004","volume":"95","author":"O Charron","year":"2018","unstructured":"Charron, O., Lallement, A., Jarnet, D., Noblet, V., Clavier, J.B., Meyer, P.: Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput. Biol. Med. 95, 43\u201354 (2018)","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"20_CR3","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1080\/10798587.2016.1231472","volume":"23","author":"M Ganesh","year":"2017","unstructured":"Ganesh, M., Naresh, M., Arvind, C.: MRI brain image segmentation using enhanced adaptive fuzzy K-means algorithm. Intell. Autom. Soft Comput. 23(2), 325\u2013330 (2017)","journal-title":"Intell. Autom. Soft Comput."},{"key":"20_CR4","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.jvcir.2018.04.007","volume":"54","author":"P Ghosh","year":"2018","unstructured":"Ghosh, P., Mali, K., Das, S.K.: Chaotic firefly algorithm-based fuzzy C-means algorithm for segmentation of brain tissues in magnetic resonance images. J. Vis. Commun. Image Represent. 54, 63\u201379 (2018)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"10","key":"20_CR5","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"20_CR6","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1049\/iet-ipr.2016.0891","volume":"11","author":"A Namburu","year":"2017","unstructured":"Namburu, A., Samayamantula, S.K., Edara, S.R.: Generalised rough intuitionistic fuzzy C-means for magnetic resonance brain image segmentation. IET Image Process. 11(9), 777\u2013785 (2017)","journal-title":"IET Image Process."},{"issue":"2","key":"20_CR7","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.bbe.2018.12.003","volume":"39","author":"A Narayanan","year":"2018","unstructured":"Narayanan, A., Rajasekaran, M.P., Zhang, Y., Govindaraj, V., Thiyagarajan, A.: Multi-channeled MR brain image segmentation: a novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation. Biocybern. Biomed. Eng. 39(2), 350\u2013381 (2018)","journal-title":"Biocybern. Biomed. Eng."},{"key":"20_CR8","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.asoc.2018.01.003","volume":"65","author":"TX Pham","year":"2018","unstructured":"Pham, T.X., Siarry, P., Oulhadj, H.: Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation. Appl. Soft Comput. 65, 230\u2013242 (2018)","journal-title":"Appl. Soft Comput."},{"key":"20_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Senthilkumar, C., Gnanamurthy, R.: A fuzzy clustering based MRI brain image segmentation using back propagation neural networks. Cluster Comput., 1\u20138 (2018). https:\/\/link.springer.com\/article\/10.1007\/s10586-017-1613-x","DOI":"10.1007\/s10586-017-1613-x"},{"key":"20_CR11","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.asoc.2018.03.054","volume":"68","author":"C Singh","year":"2018","unstructured":"Singh, C., Bala, A.: A DCT-based local and non-local fuzzy C-means algorithm for segmentation of brain magnetic resonance images. Appl. Soft Comput. 68, 447\u2013457 (2018)","journal-title":"Appl. Soft Comput."},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., Lenc, K., Ehrhardt, S., Jaderberg, M.: MatConvNet: CNNs for MATLAB (2014)","DOI":"10.1145\/2733373.2807412"},{"key":"20_CR13","unstructured":"Brain Web: Simulated brain database. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill (2004). http:\/\/brainweb.bic.mni.mcgill.ca\/brainweb"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-21077-9_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:04:53Z","timestamp":1687133093000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-21077-9_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030210762","9783030210779"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-21077-9_20","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":"18 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Quer\u00e9taro","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mcpr22019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mcpr.org.mx","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"86","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":"40","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":"47% - 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":"2.82","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":"3.39","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"}]}}