{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:26:07Z","timestamp":1742912767150,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031505706"},{"type":"electronic","value":"9783031505713"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-50571-3_3","type":"book-chapter","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T08:02:33Z","timestamp":1708416153000},"page":"31-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Superpixel Clustering Algorithm to Hip Joint Image Segmentation Registration"],"prefix":"10.1007","author":[{"given":"Jinshun","family":"Ding","sequence":"first","affiliation":[]},{"given":"Xiaoyu","family":"Lian","sequence":"additional","affiliation":[]},{"given":"Taowen","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Dandan","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Zhiying","family":"Cao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,21]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Kleiven, S.: Hip fracture risk functions for elderly men and women in sideways falls. J. Biomechanics 105, 109771 (6 pp.) (2020)","DOI":"10.1016\/j.jbiomech.2020.109771"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Jazinizadeh, F.: Enhancing hip fracture risk prediction by statistical modeling and texture analysis on DXA images. Quenneville, Cheryl E. Medical Engineering and Physics 78, 14\u201320 (2020)","DOI":"10.1016\/j.medengphy.2020.01.015"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"T. Computer Methods in Biomechanics and Biomedical Engineering 23(9), 476\u201383 (2020)","DOI":"10.1080\/10255842.2020.1738404"},{"key":"3_CR4","unstructured":"Cordeiro, M., Caskey, S., Frank, C., Martin, S., Srivastava, A., Atkinson, T.: Hybrid Triad Provides Fracture Plane Stability in a Computational Model of a Pauwels Type III Hip Fracture"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Li, G., Jia, J.: Convolutional neural network to explore the effect of the drug on postoperative POCD in elderly patients with hip fracture. J. Intelligent & Fuzzy Systems: Applications in Engineering and Technol. 39(4), 4989\u201397 (2020)","DOI":"10.3233\/JIFS-179984"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Carballido-Gamio, J., et al.: Hip fracture discrimination based on statistical multi-parametric modeling (SMPM). Annals of Biomedical Engineering 47(11), 2199\u2013212 (2019)","DOI":"10.1007\/s10439-019-02298-x"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Zuki, A.A.M. , Mat, F., Daud, R., Kamaruddin, N.S., Ibrahim, I. A review of hip fracture analysis subjected to impact loading.IOP Conference Series: Materials Science and Eng. 670, 012026 (5 pp.) (2019)","DOI":"10.1088\/1757-899X\/670\/1\/012026"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Ding, J., Xu, K., Ren, Y., Cao, Z.: Modeling and printing technology based on 3D registration algorithm of MIMICS software applied to hip fracture .lecture notes of the institute for computer sciences. Social-Informatics and Telecommunications Engineering, Multimedia Technology and Enhanced Learning - 4th EAI International Conference, ICMTEL 2022, LNICST LNICST 446, 517\u2013524 (2022)","DOI":"10.1007\/978-3-031-18123-8_40"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Aldieri, A., Terzini, M., Audenino, A.L.: Combining shape and intensity dxa-based statistical approaches for osteoporotic HIP fracture risk assessment. Bignardi, Cristina; Morbiducci, Umberto Source: Computers in Biology and Medicine 127 (2020)","DOI":"10.1016\/j.compbiomed.2020.104093"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Jazinizadeh, F., Quenneville, C.E.: 3D Analysis of the proximal femur compared to 2D analysis for hip fracture risk prediction in a clinical population. Annals of Biomedical Eng. 49(4), 1222\u20131232 (2021)","DOI":"10.1007\/s10439-020-02670-2"},{"issue":"4","key":"3_CR11","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1080\/03091902.2021.1893845","volume":"45","author":"A Mishra","year":"2021","unstructured":"Mishra, A., Srivastava, V.: Biomaterials and 3D printing techniques used in the medical field. J. Med. Eng. Technol. 45(4), 290\u2013302 (2021)","journal-title":"J. Med. Eng. Technol."},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Xiao, J., et al.: S Large-scale 3D printing concrete technology: current status and future opportunities. Cement and Concrete Composites 122 (2021)","DOI":"10.1016\/j.cemconcomp.2021.104115"},{"issue":"11","key":"3_CR13","doi-asserted-by":"publisher","first-page":"12900","DOI":"10.1109\/JSEN.2020.3042436","volume":"21","author":"M Schouten","year":"2021","unstructured":"Schouten, M., Wolterink, G., Dijkshoorn, A., Kosmas, D., Stramigioli, S., Krijnen, G.: A review of extrusion-based 3D printing for the fabrication of electro- and biomechanical sensors. IEEE Sens. J. 21(11), 12900\u201312912 (2021)","journal-title":"IEEE Sens. J."},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Blyweert, P., Nicolas, V., Fierro, V., Celzard, A.: 3D printing of carbon-based materials: a review. Carbon 183, 449\u2013485 (2021)","DOI":"10.1016\/j.carbon.2021.07.036"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Shahbazi, M., Jiger, H.: Current status in the utilization of biobased polymers for 3D printing process: a systematic review of the materials, processes, and challenges. ACS Applied Bio Materials 4(1), 325\u2013369 (2021)","DOI":"10.1021\/acsabm.0c01379"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Kamiya, T.Y., Corr\u00eaa, M., Marcell, M., Kleina, M.: Case study applying the methodology in a 3D printing process. SpringerBriefs in Applied Sciences and Technology, pp 31\u201368 (2021)","DOI":"10.1007\/978-3-030-69695-5_3"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Alwazzan, M.J.,Alkhfagi, A.O., Alattar, A.M.: Image segmentation algorithm based on statistical properties. research in intelligent and computing in engineering. Select Proceedings of RICE 2020. Advances in Intelligent Systems and Computing (AISC 1254), pp 333\u201340 (2021)","DOI":"10.1007\/978-981-15-7527-3_32"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Prasath, V.B.S., Dang, N.H.T., Nguyen, H.H., Dvoenko, S.: Multiregion multiscale image segmentation with anisotropic diffusion .pattern recognition. ICPR International Workshops and Challenges. Proceedings. Lecture Notes in Computer Science (LNCS 12665), pp. 129\u201340 (2021)","DOI":"10.1007\/978-3-030-68821-9_13"},{"key":"3_CR19","unstructured":"Jun, M.: Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?. arXiv, p 13 (2021)"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhao, D., Zhong, W.: Auxiliary recognition of alzheimer\u2019s disease based on gaussian probability brain image segmentation model. Communications in Computer and Information Science CCIS 1138, 513\u2013520 (2019)","DOI":"10.1007\/978-981-15-1925-3_37"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ding, J., Fang, W., Cao, J.: Segmentation-assisted diagnosis of pulmonary nodule recognition based on adaptive particle swarm image algorithm. Communications in Computer and Information Science, CCIS, 1138, 504\u2013512 (2019). Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health - International 2019 Cyberspace Congress, CyberDI and CyberLife, Proceedings","DOI":"10.1007\/978-981-15-1925-3_36"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Multimedia Technology and Enhanced Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50571-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T13:02:25Z","timestamp":1716814945000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50571-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031505706","9783031505713"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50571-3_3","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICMTEL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Technology and Enhanced Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leicester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"28 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icmtel2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icmtel.eai-conferences.org\/2023\/","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":"Confy Plus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"285","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":"121","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":"42% - 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":"3.1","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":"6.5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}