{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:43:35Z","timestamp":1742975015286,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030026974"},{"type":"electronic","value":"9783030026981"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","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":[[2018]]},"DOI":"10.1007\/978-3-030-02698-1_50","type":"book-chapter","created":{"date-parts":[[2018,11,8]],"date-time":"2018-11-08T16:46:59Z","timestamp":1541695619000},"page":"577-586","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Level Set Algorithm Based on Probabilistic Statistics for MR Image Segmentation"],"prefix":"10.1007","author":[{"given":"Jin","family":"Liu","sequence":"first","affiliation":[]},{"given":"Xue","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Langlang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,9]]},"reference":[{"issue":"6","key":"50_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2015\/450341","volume":"2015","author":"I Despotovic","year":"2015","unstructured":"Despotovic, I., Goossens, B., Philips, W.: MRI segmentation of the human brain: challenges, methods, and applications. Comput. Math. Methods Med. 2015(6), 1\u201323 (2015)","journal-title":"Comput. Math. Methods Med."},{"key":"50_CR2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.ins.2015.10.018","volume":"330","author":"Subhashis Banerjee","year":"2016","unstructured":"Banerjee, S., Mitra, S., Shankar, B.U.: Single seed delineation of brain tumor using multi-thresholding. Inf. Sci. 330(C), 88\u2013103 (2016)","journal-title":"Information Sciences"},{"key":"50_CR3","doi-asserted-by":"crossref","unstructured":"Caldairou, B., Passat, N., Habas, P.A., Studholme, C., Rousseau, F.: A non-local fuzzy segmentation method. Pattern Recogn. 44(9), 1916\u20131927 (2016)","DOI":"10.1016\/j.patcog.2010.06.006"},{"issue":"1","key":"50_CR4","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1049\/iet-cvi.2014.0193","volume":"10","author":"V. Anitha","year":"2016","unstructured":"Anitha, V., Murugavalli, S.: Brain tumour classification using two-tier classifier with adaptive segmentation technique. IET Comput. Vision. 10(1), 9\u201317 (2016)","journal-title":"IET Computer Vision"},{"key":"50_CR5","unstructured":"Kamaruddin, N.: Active contour model using fractional sync wave function for medical image segmentation. Surf. Sci. 363(1\u20133), 321\u2013325 (2017)"},{"key":"50_CR6","doi-asserted-by":"crossref","unstructured":"Khadidos, A., Sanchez, V., Li, C.T.: Active contours based on weighted gradient vector flow and balloon forces for medical image segmentation. In: IEEE International Conference on Image Processing, pp. 902\u2013906 (2015)","DOI":"10.1109\/ICIP.2014.7025181"},{"issue":"2","key":"50_CR7","first-page":"1","volume":"99","author":"R Agrawal","year":"2018","unstructured":"Agrawal, R., Sharma, M., Singh, B.K.: Segmentation of brain lesions in MRI and CT scan images: a hybrid approach using k-means clustering and image morphology. J. Inst. Eng. 99(2), 1\u20138 (2018)","journal-title":"J. Inst. Eng."},{"issue":"1","key":"50_CR8","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.eij.2015.01.003","volume":"16","author":"Eman Abdel-Maksoud","year":"2015","unstructured":"Abdel-Maksoud, E., Elmogy, M., Al-Awadi, R.: Brain tumor segmentation based on a hybrid clustering technique. Egypt. Inf. J. 16(1), 71\u201381(2015)","journal-title":"Egyptian Informatics Journal"},{"key":"50_CR9","doi-asserted-by":"crossref","unstructured":"Lu, S., Lei, L., Huang, H., Xiao, L.: A hybrid extraction-classification method for brain segmentation in MR image. Int. Congr. Image Sig. Proc. 1381\u20131385 (2017)","DOI":"10.1109\/CISP-BMEI.2016.7852932"},{"key":"50_CR10","doi-asserted-by":"crossref","unstructured":"Li, C., Huang, R., et al.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007\u20132016 (2011)","DOI":"10.1109\/TIP.2011.2146190"},{"key":"50_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1007\/978-3-642-02498-6_24","volume-title":"Information Processing in Medical Imaging","author":"C Li","year":"2009","unstructured":"Li, C., Xu, C., Anderson, Adam W., Gore, John C.: MRI tissue classification and bias field estimation based on coherent local intensity clustering: a unified energy minimization framework. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 288\u2013299. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-02498-6_24"},{"key":"50_CR12","doi-asserted-by":"crossref","unstructured":"Li, C., Gatenby, C., Wang, L., et al.: A robust parametric method for bias field estimation and segmentation of MR images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 218\u2013223 (2009)","DOI":"10.1109\/CVPR.2009.5206553"},{"issue":"2","key":"50_CR13","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1109\/TCYB.2015.2409119","volume":"46","author":"Kaihua Zhang","year":"2016","unstructured":"Zhang, K., Zhang, L., Lam, K.M., Zhang, D.: A level set approach to image segmentation with intensity inhomogeneity. IEEE Trans. Cybern. 46(2), 546\u2013557 (2016)","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"8","key":"50_CR14","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1109\/TCYB.2014.2352343","volume":"45","author":"Kaihua Zhang","year":"2015","unstructured":"Zhang, K., Liu, Q., Song, H., Li, X.: A variational approach to simultaneous image segmentation and bias correction. IEEE Trans. Cybern. 45(5), 1426\u20131437 (2015)","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"4","key":"50_CR15","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1016\/j.imavis.2009.10.009","volume":"28","author":"Kaihua Zhang","year":"2010","unstructured":"Zhang, K., Zhang, L., Song, H., Zhou, W.: Active contours with selective local or global segmentation: a new formulation and level set method. Image Vis. Comput. 28(4), 668\u2013676 (2010)","journal-title":"Image and Vision Computing"},{"issue":"2","key":"50_CR16","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1109\/42.563663","volume":"16","author":"J.C. Rajapakse","year":"1997","unstructured":"Rajapakse, J., Giedd, J., Rapoport, J.: Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans. Med. Imaging 16(2), 176\u2013186 (1997)","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"3","key":"50_CR17","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/S0262-8856(97)00067-X","volume":"16","author":"Jagath C. Rajapakse","year":"1998","unstructured":"Rajapakse, J., Kruggel, F.: Segmentation of MR images with intensity inhomogeneities. Image Vis. Comput. 16(3), 165\u2013180 (1998)","journal-title":"Image and Vision Computing"},{"key":"50_CR18","doi-asserted-by":"crossref","unstructured":"Yang, X., Gao, X., Li, X., et al.: An efficient MRF embedded level set method for image segmentation. IEEE Trans. Image Proc. 24(1), 9 (2015)","DOI":"10.1109\/TIP.2014.2372615"},{"issue":"1","key":"50_CR19","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1109\/TIP.2012.2214046","volume":"22","author":"Kaihua Zhang","year":"2013","unstructured":"Zhang, K., Zhang, L., Song, H., Zhang, D.: Re-initialization free level set evolution via reaction diffusion. IEEE Trans. Image Process. 22(1), 258\u2013271 (2012)","journal-title":"IEEE Transactions on Image Processing"},{"key":"50_CR20","doi-asserted-by":"crossref","unstructured":"Xie, X.: Active contouring based on gradient vector interaction and constrained level set diffusion. IEEE Trans. Image Process. 19(1), 154\u2013164 (2010)","DOI":"10.1109\/TIP.2009.2032891"}],"container-title":["Lecture Notes in Computer Science","Intelligence Science and Big Data Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-02698-1_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:04:36Z","timestamp":1710241476000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-02698-1_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030026974","9783030026981"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-02698-1_50","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"9 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IScIDE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Science and Big Data Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lanzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"18 August 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 August 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iscide2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iscide.lzu.edu.cn\/","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":"121","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":"59","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":"49% - 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":"2.7","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":"4.9","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)"}}]}}