A Threshold Selection Method from Gray-Level Histograms

Otsu, Nobuyuki·1979·IEEE Transactions on Systems, Man, and Cybernetics

DOI 10.1109/tsmc.1979.4310076

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10.1016/0010-4809(72)90070-5 · DOI
10.1016/0146-664X(74)90029-X · DOI
fukunage, 1972, Introduction to statistical pattern recognition, 260
10.1109/T-C.1974.223858 · DOI
10.1111/j.1749-6632.1965.tb11715.x · DOI · Paper page

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Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731
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Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059968 · Cited by 3,796
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Armato, S. G., McLennan, G., Bidaut, L., McNitt‐Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., et al. (2011) · Medical Physics · doi:10.1118/1.3528204 · Cited by 2,887
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Yu, C., Gao, C., Wang, J., Yu, G., Shen, C., & Sang, N. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01515-2 · Cited by 1,813
Dipy, a library for the analysis of diffusion MRI data
Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., Nimmo-Smith, I., & Contributors, D. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00008 · Cited by 1,371
“…median_otsu (see Figure 3 ). from dipy.segment.mask import median_otsu mask, S0_mask = median_otsu(data [:, :, :, 0]) median_otsu uses ﬁrst a median ﬁlter to smooth the S0 and then Otsu’s method, an automated histogram method (Otsu, 1979) to separate the brain (foreground) from its background.”
Method
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Pfefferbaum, A., Mathalon, D. H., Sullivan, E. V., Rawles, J. M., Zipursky, R. B., & Lim, K. O. (1994) · Archives of Neurology · doi:10.1001/archneur.1994.00540210046012 · Cited by 1,363
Software techniques for two- and three-dimensional kinematic measurements of biological and biomimetic systems
Hedrick, T. L. (2008) · Bioinspiration & Biomimetics · doi:10.1088/1748-3182/3/3/034001 · Cited by 1,360
Plug-and-Play priors for model based reconstruction
Venkatakrishnan, S. V., Bouman, C. A., & Wohlberg, B. (2013) · 2013 IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2013.6737048 · Cited by 1,178
Autonomous concrete crack detection using deep fully convolutional neural network
Dung, C. V., & Anh, L. D. (2019) · Automation in Construction · doi:10.1016/j.autcon.2018.11.028 · Cited by 1,170
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Valm, A. M., Cohen, S., Legant, W. R., Melunis, J., Hershberg, U., Wait, E., Cohen, A. R., Davidson, M. W., Betzig, E., & Lippincott-Schwartz, J. (2017) · Nature · doi:10.1038/nature22369 · Cited by 1,113
Automated breast ultrasound lesions detection using convolutional neural networks
Yap, M. H., Pons, G., Martí, J., Ganau, S., Sentís, M., Zwiggelaar, R., Davison, A. K., & Martí, R. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2731873 · Cited by 1,086
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., & Snyder, M. (2016) · Nature Communications · doi:10.1038/ncomms12474 · Cited by 972
Image segmentation evaluation: A survey of unsupervised methods
Zhang, H., Fritts, J. E., & Goldman, S. A. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.08.003 · Cited by 963
Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
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Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07441-w · Cited by 921
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Jalili-Firoozinezhad, S., Gazzaniga, F. S., Calamari, E. L., Camacho, D. M., Fadel, C. W., Bein, A., Swenor, B., Nestor, B., Cronce, M. J., Tovaglieri, A., et al. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0397-0 · Cited by 855
Telomeric DNA damage is irreparable and causes persistent DNA-damage-response activation
Fumagalli, M., Rossiello, F., Clerici, M., Barozzi, S., Cittaro, D., Kaplunov, J. M., Bucci, G., Dobreva, M., Matti, V., Beausejour, C. M., et al. (2012) · Nature Cell Biology · doi:10.1038/ncb2466 · Cited by 851
Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
Caicedo, J. C., Goodman, A., Karhohs, K. W., Cimini, B. A., Ackerman, J., Haghighi, M., Heng, C., Becker, T., Doan, M., McQuin, C., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0612-7 · Cited by 809
Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions
De Sousa E Melo, F., Wang, X., Jansen, M., Fessler, E., Trinh, A., de Rooij, L. P. M. H., de Jong, J. H., de Boer, O. J., van Leersum, R., Bijlsma, M. F., et al. (2013) · Nature Medicine · doi:10.1038/nm.3174 · Cited by 771
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review
Azimi, M., Eslamlou, A., & Pekcan, G. (2020) · Sensors · doi:10.3390/s20102778 · Cited by 769
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
Bravo González-Blas, C., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01938-4 · Cited by 778
“We have included several binarization methods (applicable for topic-region and cell-topic distributions): ’otsu’ (Otsu, 1979)107, ’yen’ (Yen et al., 1995)108, ’li’ (Li & Lee, 1993)109, ’aucell’ (Van de Sande et al., 2020)20 or ’ntop’ (Taking the top n regions per topic).”
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Turnbull, L., Toyofuku, M., Hynen, A. L., Kurosawa, M., Pessi, G., Petty, N. K., Osvath, S. R., Cárcamo-Oyarce, G., Gloag, E. S., Shimoni, R., et al. (2016) · Nature Communications · doi:10.1038/ncomms11220 · Cited by 709
Deep Features for Text Spotting
Jaderberg, M., Vedaldi, A., & Zisserman, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10593-2_34 · Cited by 653
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Al-Kofahi, Y., Lassoued, W., Lee, W., & Roysam, B. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2035102 · Cited by 681
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Tustison, N. J., Cook, P. A., Klein, A., Song, G., Das, S. R., Duda, J. T., Kandel, B. M., van Strien, N., Stone, J. R., Gee, J. C., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.044 · Cited by 670
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Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07894-z · Cited by 580
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Bandi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Ehteshami Bejnordi, B., Lee, B., Paeng, K., Zhong, A., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2867350 · Cited by 569
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Bost, P., Giladi, A., Liu, Y., Bendjelal, Y., Xu, G., David, E., Blecher-Gonen, R., Cohen, M., Medaglia, C., Li, H., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.006 · Cited by 471
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Schmauch, B., Romagnoni, A., Pronier, E., Saillard, C., Maillé, P., Calderaro, J., Kamoun, A., Sefta, M., Toldo, S., Zaslavskiy, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17678-4 · Cited by 533
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Cruz Hernández, J. C., Bracko, O., Kersbergen, C. J., Muse, V., Haft-Javaherian, M., Berg, M., Park, L., Vinarcsik, L. K., Ivasyk, I., Rivera, D. A., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0329-4 · Cited by 508
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Xie, X. (2008) · ELCVIA Electronic Letters on Computer Vision and Image Analysis · doi:10.5565/rev/elcvia.268 · Cited by 500
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Lotter, W., Diab, A. R., Haslam, B., Kim, J. G., Grisot, G., Wu, E., Wu, K., Onieva, J. O., Boyer, Y., Boxerman, J. L., et al. (2021) · Nature Medicine · doi:10.1038/s41591-020-01174-9 · Cited by 423
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Baykara, E., Gesierich, B., Adam, R., Tuladhar, A. M., Biesbroek, J. M., Koek, H. L., Ropele, S., Jouvent, E., Initiative, A. D. N., Chabriat, H., et al. (2016) · Annals of Neurology · doi:10.1002/ana.24758 · Cited by 386
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Dankert, H., Wang, L., Hoopfer, E. D., Anderson, D. J., & Perona, P. (2009) · Nature Methods · doi:10.1038/nmeth.1310 · Cited by 382
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Doi, D., Magotani, H., Kikuchi, T., Ikeda, M., Hiramatsu, S., Yoshida, K., Amano, N., Nomura, M., Umekage, M., Morizane, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17165-w · Cited by 350
Partial volume effect as a hidden covariate in DTI analyses
Vos, S. B., Jones, D. K., Viergever, M. A., & Leemans, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.048 · Cited by 345
Multi-compartment microscopic diffusion imaging
Kaden, E., Kelm, N. D., Carson, R. P., Does, M. D., & Alexander, D. C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.002 · Cited by 345
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Wei, X., Yang, Z., Liu, Y., Wei, D., Jia, L., & Li, Y. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.01.008 · Cited by 338
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Li, Q., & Ren, S. (2012) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2012.2184959 · Cited by 299
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Hamamci, A., Kucuk, N., Karaman, K., Engin, K., & Unal, G. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2181857 · Cited by 286
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Xian, M., Zhang, Y., Cheng, H. D., Xu, F., Zhang, B., & Ding, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.02.012 · Cited by 284
“[115] Global thresholding ROI generation Otsu’s algorithm [130] Depended on image preprocessing.”
Method
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Bonafilia, D., Tellman, B., Anderson, T., & Issenberg, E. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00113 · Cited by 295
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Langer, E. M., Allen-Petersen, B. L., King, S. M., Kendsersky, N. D., Turnidge, M. A., Kuziel, G. M., Riggers, R., Samatham, R., Amery, T. S., Jacques, S. L., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2018.12.090 · Cited by 256
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Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1415-x · Cited by 241
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Berck, M. E., Khandelwal, A., Claus, L., Hernandez-Nunez, L., Si, G., Tabone, C. J., Li, F., Truman, J. W., Fetter, R. D., Louis, M., et al. (2016) · eLife · doi:10.7554/elife.14859 · Cited by 209
The Dynamics of mRNA Turnover Revealed by Single-Molecule Imaging in Single Cells
Horvathova, I., Voigt, F., Kotrys, A. V., Zhan, Y., Artus-Revel, C. G., Eglinger, J., Stadler, M. B., Giorgetti, L., & Chao, J. A. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.09.030 · Cited by 225
The spatiotemporal program of zonal liver regeneration following acute injury
Ben-Moshe, S., Veg, T., Manco, R., Dan, S., Papinutti, D., Lifshitz, A., Kolodziejczyk, A. A., Bahar Halpern, K., Elinav, E., & Itzkovitz, S. (2022) · Cell Stem Cell · doi:10.1016/j.stem.2022.04.008 · Cited by 224
DMP1 depletion decreases bone mineralization in vivo: an FTIR imaging analysis
Ling, Y., Rios, H. F., Myers, E. R., Lu, Y., Feng, J. Q., & Boskey, A. L. (2005) · Journal of Bone and Mineral Research · doi:10.1359/jbmr.050815 · Cited by 216
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Fernandez Alcon, J., Ciuhu, C., ten Kate, W., Heinrich, A., Uzunbajakava, N., Krekels, G., Siem, D., & de Haan, G. (2009) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2008.2011156 · Cited by 210
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Dumont, N., Liu, B., DeFilippis, R. A., Chang, H., Rabban, J. T., Karnezis, A. N., Tjoe, J. A., Marx, J., Parvin, B., & Tlsty, T. D. (2013) · Neoplasia · doi:10.1593/neo.121950 · Cited by 192
Automatic detection of solar photovoltaic arrays in high resolution aerial imagery
Malof, J. M., Bradbury, K., Collins, L. M., & Newell, R. G. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.191 · Cited by 196
“Regions are grown around each remaining maxima location using Otsu’s method [70], which automatically segments pixels into foreground (high confidence) and background (low confidence) regions.”
Method
Brain Structural Abnormalities in Psychotropic Drug-Naive Pediatric Patients With Obsessive-Compulsive Disorder
Szeszko, P. R., MacMillan, S., McMeniman, M., Chen, S., Baribault, K., Lim, K. O., Ivey, J., Rose, M., Banerjee, S. P., Bhandari, R., et al. (2004) · American Journal of Psychiatry · doi:10.1176/appi.ajp.161.6.1049 · Cited by 201
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Triana, S., Vonficht, D., Jopp-Saile, L., Raffel, S., Lutz, R., Leonce, D., Antes, M., Hernández-Malmierca, P., Ordoñez-Rueda, D., Ramasz, B., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-01059-0 · Cited by 195
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Levo, M., Raimundo, J., Bing, X. Y., Sisco, Z., Batut, P. J., Ryabichko, S., Gregor, T., & Levine, M. S. (2022) · Nature · doi:10.1038/s41586-022-04680-7 · Cited by 188
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Xue-wu, Z., Yan-qiong, D., Yan-yun, L., Ai-ye, S., & Rui-yu, L. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.11.030 · Cited by 188
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Trinh, A., Trumpi, K., De Sousa E Melo, F., Wang, X., de Jong, J. H., Fessler, E., Kuppen, P. J. K., Reimers, M. S., Swets, M., Koopman, M., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-0680 · Cited by 180
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Kenkhuis, B., Somarakis, A., de Haan, L., Dzyubachyk, O., IJsselsteijn, M. E., de Miranda, N. F. C. C., Lelieveldt, B. P. F., Dijkstra, J., van Roon-Mom, W. M. C., Höllt, T., et al. (2021) · Acta Neuropathologica Communications · doi:10.1186/s40478-021-01126-5 · Cited by 184
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Wen, C., Sun, X., Li, J., Wang, C., Guo, Y., & Habib, A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.10.007 · Cited by 158
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Wang, H., Zhang, J., Tian, Y., Chen, H., Sun, H., & Liu, K. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2887145 · Cited by 153
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Yeh, F.-C., Vettel, J. M., Singh, A., Poczos, B., Grafton, S. T., Erickson, K. I., Tseng, W.-Y. I., & Verstynen, T. D. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005203 · Cited by 151
Road marking detection using LIDAR reflective intensity data and its application to vehicle localization
Hata, A., & Wolf, D. (2014) · 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2014.6957753 · Cited by 150
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Dolezal, J. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S., Cody, B., Mansfield, A. S., Rakshit, S., Bansal, R., Bois, M. C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34025-x · Cited by 147
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Oliveira, A. D., Prats, C., Espasa, M., Zarzuela Serrat, F., Montañola Sales, C., Silgado, A., Codina, D. L., Arruda, M. E., i Prat, J. G., & Albuquerque, J. (2017) · JMIR Research Protocols · doi:10.2196/resprot.6758 · Cited by 144
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Win, M., Bushroa, A. R., Hassan, M. A., Hilman, N. M., & Ide-Ektessabi, A. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2417676 · Cited by 142
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HOPTMAN, M., NIERENBERG, J., BERTISCH, H., CATALANO, D., ARDEKANI, B., BRANCH, C., & DELISI, L. (2008) · Schizophrenia Research · doi:10.1016/j.schres.2008.07.023 · Cited by 143
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Wang, J., Li, Q., Gan, J., Yu, H., & Yang, X. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2917522 · Cited by 141
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Jalil, B., Leone, G. R., Martinelli, M., Moroni, D., Pascali, M. A., & Berton, A. (2019) · Sensors · doi:10.3390/s19133014 · Cited by 141
Dissolution and secondary mineral precipitation in basalts due to reactions with carbonic acid
Kanakiya, S., Adam, L., Esteban, L., Rowe, M. C., & Shane, P. (2017) · Journal of Geophysical Research: Solid Earth · doi:10.1002/2017jb014019 · Cited by 140
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Kim, Y. J., Jang, H., Lee, K., Park, S., Min, S.-G., Hong, C., Park, J. H., Lee, K., Kim, J., Hong, W., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101854 · Cited by 131
Osteocytic osteolysis observed in rats to which parathyroid hormone was continuously administered
Tazawa, K., Hoshi, K., Kawamoto, S., Tanaka, M., Ejiri, S., & Ozawa, H. (2004) · Journal of Bone and Mineral Metabolism · doi:10.1007/s00774-004-0519-x · Cited by 124
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Authors: Otsu, N.
Venue: IEEE Transactions on Systems, Man, and Cybernetics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 1979-01
Volume: 9
Issue: 1
Pages: 62-66
ISSN: 0018-9472 (ISSN-L)
2168-2909
DOI: 10.1109/tsmc.1979.4310076
OpenAlex: W2133059825
Cited by: 43,398
References: 0
FWCI: 4.52
Type: journal article
Language: en
Open access: closed

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