{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T05:52:33Z","timestamp":1725861153635},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319422930"},{"type":"electronic","value":"9783319422947"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-42294-7_55","type":"book-chapter","created":{"date-parts":[[2016,7,11]],"date-time":"2016-07-11T11:00:47Z","timestamp":1468234847000},"page":"613-623","source":"Crossref","is-referenced-by-count":0,"title":["An Improved Ultrasound Image Segmentation Algorithm for Cattle Follicle Based on Markov Random Field Model"],"prefix":"10.1007","author":[{"given":"Jun","family":"Liu","sequence":"first","affiliation":[]},{"given":"Bo","family":"Guan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,7,12]]},"reference":[{"issue":"5","key":"55_CR1","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1016\/j.sigpro.2010.12.003","volume":"91","author":"Y Li","year":"2011","unstructured":"Li, Y., Mao, X., Feng, D., Zhang, Y.: Fast and accuracy extraction of infrared target based on Markov random field. Sig. Process. 91(5), 1216\u20131223 (2011)","journal-title":"Sig. Process."},{"issue":"3","key":"55_CR2","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.media.2011.01.002","volume":"15","author":"L Cordero-Grande","year":"2011","unstructured":"Cordero-Grande, L., Vegas-S\u00e1nchez-Ferrero, G., Casaseca-de-la-Higuera, P., San-Rom\u00e1n-Calvar, J.A., Revilla-Orodea, A., Mart\u00edn-Fern\u00e1ndez, M., Alberola-L\u00f3pez, C.: Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model. Med. Image Anal. 15(3), 283\u2013301 (2011)","journal-title":"Med. Image Anal."},{"issue":"4","key":"55_CR3","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1016\/j.media.2012.01.001","volume":"16","author":"S Yousefi","year":"2012","unstructured":"Yousefi, S., Azmi, R., Zahedi, M.: Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms. Med. Image Anal. 16(4), 840\u2013848 (2012)","journal-title":"Med. Image Anal."},{"key":"55_CR4","doi-asserted-by":"crossref","unstructured":"Li, Q., Liu, G.: Multi-resolution Markov random field model with variable potentials in wavelet domain for texture image segmentation. In: 2010 International Conference on Computer Application and System Modeling (ICCASM), vol. 9, p. V9-342 (2010)","DOI":"10.1109\/ICCASM.2010.5623020"},{"key":"55_CR5","doi-asserted-by":"crossref","unstructured":"Mridula, J., Kumar, K., Patra, D.: Combining GLCM features and markov random field model for colour textured image segmentation. In: 2011 International Conference on Devices and Communications (ICDeCom), pp. 1\u20135 (2011)","DOI":"10.1109\/ICDECOM.2011.5738494"},{"issue":"2","key":"55_CR6","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.patrec.2010.09.017","volume":"32","author":"Y Cao","year":"2011","unstructured":"Cao, Y., Luo, Y., Yang, S.: Image denoising based on hierarchical Markov random field. Pattern Recogn. Lett. 32(2), 368\u2013374 (2011)","journal-title":"Pattern Recogn. Lett."},{"issue":"8","key":"55_CR7","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1109\/TIP.2010.2045708","volume":"19","author":"AK Qin","year":"2010","unstructured":"Qin, A.K., Clausi, D.A.: Multivariate image segmentation using semantic region growing with adaptive edge penalty. IEEE Trans. Image Process. 19(8), 2157\u20132170 (2010)","journal-title":"IEEE Trans. Image Process."},{"issue":"10","key":"55_CR8","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.oceaneng.2010.03.003","volume":"37","author":"XF Ye","year":"2010","unstructured":"Ye, X.F., Zhang, Z.H., Liu, P.X., Guan, H.L.: Sonar image segmentation based on GMRF and level-set models. Ocean Eng. 37(10), 891\u2013901 (2010)","journal-title":"Ocean Eng."},{"issue":"4","key":"55_CR9","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.media.2010.04.007","volume":"14","author":"JP Monaco","year":"2010","unstructured":"Monaco, J.P., Tomaszewski, J.E., Feldman, M.D., Hagemann, I., Moradi, M., Mousavi, P., Madabhushi, A.: High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models. Med. Image Anal. 14(4), 617\u2013629 (2010)","journal-title":"Med. Image Anal."},{"issue":"6","key":"55_CR10","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1016\/j.media.2011.05.002","volume":"15","author":"A Roche","year":"2011","unstructured":"Roche, A., Ribes, D., Bach-Cuadra, M., Kr\u00fcger, G.: On the convergence of EM-like algorithms for image segmentation using Markov random fields. Med. Image Anal. 15(6), 830\u2013839 (2011)","journal-title":"Med. Image Anal."},{"issue":"3","key":"55_CR11","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.compbiomed.2007.12.005","volume":"38","author":"R Khayati","year":"2008","unstructured":"Khayati, R., Vafadust, M., Towhidkhah, F., Nabavi, M.: Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model. Comput. Biol. Med. 38(3), 379\u2013390 (2008)","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"55_CR12","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1109\/JSTARS.2012.2217940","volume":"5","author":"X Yang","year":"2012","unstructured":"Yang, X., Clausi, D.A.: Evaluating SAR sea ice image segmentation using edge-preserving region-based MRFs. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(5), 1383\u20131393 (2012)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"55_CR13","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/978-3-642-35314-7_85","volume-title":"Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA)","author":"A Gupta","year":"2013","unstructured":"Gupta, A., Tripathi, A., Bhateja, V.: Despeckling of SAR images via an improved anisotropic diffusion algorithm. In: Satapathy, S.C., Udgata, S.K., Biswal, B.N. (eds.) Proceedings of Int. Conf. on Front. of Intell. Comput. AISC, vol. 199, pp. 747\u2013754. Springer, Heidelberg (2013)"},{"key":"55_CR14","doi-asserted-by":"crossref","unstructured":"Tauber, C., Batatia, H., Ayache, A.: A robust speckle reducing anisotropic diffusion. In: 2004 International Conference on Image Processing, 2004, ICIP 2004, vol. 1, pp. 247\u2013250 (2004)","DOI":"10.1109\/ICIP.2004.1418736"},{"issue":"11","key":"55_CR15","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1016\/j.patrec.2004.04.007","volume":"25","author":"SS Khan","year":"2004","unstructured":"Khan, S.S., Ahmad, A.: Cluster center initialization algorithm for K-means clustering. Pattern Recogn. Lett. 25(11), 1293\u20131302 (2004)","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"55_CR16","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1016\/j.neuroimage.2003.10.012","volume":"21","author":"P Anbeek","year":"2004","unstructured":"Anbeek, P., Vincken, K.L., van Osch, M.J., Bisschops, R.H., van der Grond, J.: Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage 21(3), 1037\u20131044 (2004)","journal-title":"NeuroImage"},{"issue":"4","key":"55_CR17","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/42.363096","volume":"13","author":"AP Zijdenbos","year":"1994","unstructured":"Zijdenbos, A.P., Dawant, B.M., Margolin, R.A., Palmer, A.C.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imaging 13(4), 716\u2013724 (1994)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"55_CR18","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1006\/nimg.2000.0661","volume":"12","author":"R Stokking","year":"2000","unstructured":"Stokking, R., Vincken, K.L., Viergever, M.A.: Automatic morphology-based brain segmentation (MBRASE) from MRI-T1 data. NeuroImage 12(6), 726\u2013738 (2000)","journal-title":"NeuroImage"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-42294-7_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,24]],"date-time":"2017-06-24T14:11:57Z","timestamp":1498313517000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-42294-7_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319422930","9783319422947"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-42294-7_55","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]}}}