The DOI matches its Crossref record.
A Threshold Selection Method from Gray-Level Histograms
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 5
- 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
Cited by
Items 101–200 of 208
Schaer, M., Eric Schmitt, J., Glaser, B., Lazeyras, F., Delavelle, J., & Eliez, S. (2006) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2005.10.002 · Cited by 76
Machine learning methods for histopathological image analysis: updates in 2024
Komura, D., Ochi, M., & Ishikawa, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.033 · Cited by 76
Cordero-Espinoza, L., Dowbaj, A. M., Kohler, T. N., Strauss, B., Sarlidou, O., Belenguer, G., Pacini, C., Martins, N. P., Dobie, R., Wilson-Kanamori, J. R., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2021.07.002 · Cited by 74
“GFP signal is segmented using the Otsu threshold (Otsu, 1979) on smoothed signal, and Tomatocontaining cell clusters are segmented using Kapur’s maximum entropy threshold (Kapur et al., 1985) on difference of Gaussians processed images.”
MethodSoto, F., Ma, X., Cecil, J. L., Vo, B. Q., Culican, S. M., & Kerschensteiner, D. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.0194-12.2012 · Cited by 66
Tract-specific white matter hyperintensities disrupt neural network function in Alzheimer’s disease
Taylor, A. N. W., Kambeitz-Ilankovic, L., Gesierich, B., Simon-Vermot, L., Franzmeier, N., Araque Caballero, M. Á., Müller, S., Hesheng, L., Ertl-Wagner, B., Bürger, K., et al. (2017) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.06.2358 · Cited by 73
Localization and Recovery of Peripheral Neural Sources With Beamforming Algorithms
Wodlinger, B., & Durand, D. M. (2009) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2034072 · Cited by 73
Xu, B., Han, Q., Chen, J., Li, F., Wang, N., Li, D., & Pan, X. (2013) · Applied Energy · doi:10.1016/j.apenergy.2012.09.026 · Cited by 71
Xu, B., Zhang, C., Wang, Y., Chen, J., Xu, K., Li, F., & Wang, N. (2015) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2015.06.037 · Cited by 69
Long-term effects of grass-clover leys on the structure of a silt loam soil in a cold climate
Jarvis, N., Forkman, J., Koestel, J., Kätterer, T., Larsbo, M., & Taylor, A. (2017) · Agriculture, Ecosystems & Environment · doi:10.1016/j.agee.2017.06.042 · Cited by 69
Gurari, D., Theriault, D., Sameki, M., Isenberg, B., Pham, T. A., Purwada, A., Solski, P., Walker, M., Zhang, C., Wong, J. Y., et al. (2015) · 2015 IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.160 · Cited by 68
Training-Free Consistent Text-to-Image Generation
Tewel, Y., Kaduri, O., Gal, R., Kasten, Y., Wolf, L., Chechik, G., & Atzmon, Y. (2024) · ACM Transactions on Graphics · doi:10.1145/3658157 · Cited by 68
Ascending neurons convey behavioral state to integrative sensory and action selection brain regions
Chen, C.-L., Aymanns, F., Minegishi, R., Matsuda, V. D. V., Talabot, N., Günel, S., Dickson, B. J., & Ramdya, P. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01281-z · Cited by 58
Kelley, C. P., Haerle, M. C., & Wang, E. T. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111226 · Cited by 61
Steel Surface Defect Detection Using Texture Segmentation Based on Multifractal Dimension
Yazdchi, M., Yazdi, M., & Mahyari, A. G. (2009) · 2009 International Conference on Digital Image Processing · doi:10.1109/icdip.2009.68 · Cited by 65
Roy, M., Wang, F., Vo, H., Teng, D., Teodoro, G., Farris, A. B., Castillo-Leon, E., Vos, M. B., & Kong, J. (2020) · Laboratory Investigation · doi:10.1038/s41374-020-0463-y · Cited by 68
Wiren, K. M., Semirale, A. A., Zhang, X.-W., Woo, A., Tommasini, S. M., Price, C., Schaffler, M. B., & Jepsen, K. J. (2008) · Bone · doi:10.1016/j.bone.2008.04.026 · Cited by 65
Orlando, J. I., Seebock, P., Bogunovic, H., Klimscha, S., Grechenig, C., Waldstein, S., Gerendas, B. S., & Schmidt-Erfurth, U. (2019) · 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) · doi:10.1109/isbi.2019.8759581 · Cited by 65
Nuclear Localization of Huntingtin mRNA Is Specific to Cells of Neuronal Origin
Didiot, M.-C., Ferguson, C. M., Ly, S., Coles, A. H., Smith, A. O., Bicknell, A. A., Hall, L. M., Sapp, E., Echeverria, D., Pai, A. A., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.07.106 · Cited by 64
FHDe2Net: Full High Definition Demoireing Network
He, B., Wang, C., Shi, B., & Duan, L.-Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58542-6_43 · Cited by 62
Masoudnia, S., Mersa, O., Araabi, B. N., Vahabie, A.-H., Sadeghi, M. A., & Ahmadabadi, M. N. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.03.040 · Cited by 57
Facial expression recognition using thermal image processing and neural network
Yoshitomi, Y., Miyawaki, N., Tomita, S., & Kimura, S. (2002) · Proceedings 6th IEEE International Workshop on Robot and Human Communication. RO-MAN'97 SENDAI · doi:10.1109/roman.1997.647016 · Cited by 58
Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists
Gu, H., Huang, J., Hung, L., & Chen, X. '. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449084 · Cited by 56
Microscopic malaria parasitemia diagnosis and grading on benchmark datasets
Rehman, A., Abbas, N., Saba, T., Mehmood, Z., Mahmood, T., & Ahmed, K. T. (2018) · Microscopy Research and Technique · doi:10.1002/jemt.23071 · Cited by 57
Fingernail analysis management system using microscopy sensor and blockchain technology
Lee, S. H., & Yang, C. S. (2018) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147718767044 · Cited by 56
Liu, M., Liu, Y., Hu, H., & Nie, L. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.04.005 · Cited by 55
AXNav: Replaying Accessibility Tests from Natural Language
Taeb, M., Swearngin, A., Schoop, E., Cheng, R., Jiang, Y., & Nichols, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642777 · Cited by 52
Emotion recognition from thermal infrared images using deep Boltzmann machine
Wang, S., He, M., Gao, Z., He, S., & Ji, Q. (2014) · Frontiers of Computer Science · doi:10.1007/s11704-014-3295-3 · Cited by 51
Ray Dillon, A., Tillson, D. M., Wooldridge, A., Cattley, R., Hathcock, J., Brawner, W. R., Cole, R., Welles, B., Christopherson, P. W., Lee-Fowler, T., et al. (2014) · Veterinary Parasitology · doi:10.1016/j.vetpar.2014.09.007 · Cited by 51
Towards DeepFake video forensics based on facial textural disparities in multi-color channels
Xia, Z., Qiao, T., Xu, M., Zheng, N., & Xie, S. (2022) · Information Sciences · doi:10.1016/j.ins.2022.06.003 · Cited by 47
Athanasiou, L., Rigas, G., Sakellarios, A. I., Exarchos, T. P., Siogkas, P. K., Bourantas, C. V., Garcia-Garcia, H. M., Lemos, P. A., Falcao, B. A., Michalis, L. K., et al. (2016) · BMC Medical Imaging · doi:10.1186/s12880-016-0111-6 · Cited by 48
“contrast enhancement [23], which maps the intensity values of the image to new values such that, 1 % of the data is saturated at low and high intensities of the input data, image thresholding [24], which allows identification of the vessel [25],”
BackgroundA Generic Automated Surface Defect Detection Based on a Bilinear Model
Zhou, F., Liu, G., Xu, F., & Deng, H. (2019) · Applied Sciences · doi:10.3390/app9153159 · Cited by 47
On the dissection of degenerate cosmologies with machine learning
Merten, J., Giocoli, C., Baldi, M., Meneghetti, M., Peel, A., Lalande, F., Starck, J.-L., & Pettorino, V. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz972 · Cited by 45
Gao, R., Yuan, X., Ma, Y., Wei, T., Johnston, L., Shao, Y., Lv, W., Zhu, T., Zhang, Y., Zheng, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101536 · Cited by 45
ATF4-induced warburg metabolism drives over-proliferation in Drosophila
Sorge, S., Theelke, J., Yildirim, K., Hertenstein, H., McMullen, E., Müller, S., Altbürger, C., Schirmeier, S., & Lohmann, I. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107659 · Cited by 44
Vision-based rain sensing with an in-vehicle camera
Gormer, S., Kummert, A., Park, S.-B., & Egbert, P. (2009) · 2009 IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2009.5164291 · Cited by 43
Börner, K., Hermle, J., Sommer, C., Brown, N. P., Knapp, B., Glass, B., Kunkel, J., Torralba, G., Reymann, J., Beil, N., et al. (2010) · Biotechnology Journal · doi:10.1002/biot.200900226 · Cited by 43
Differentiation reveals latent features of aging and an energy barrier in murine myogenesis
Kimmel, J. C., Yi, N., Roy, M., Hendrickson, D. G., & Kelley, D. R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109046 · Cited by 42
Song, K.-C., Hu, S.-P., Yan, Y.-H., & Li, J. (2014) · ISIJ International · doi:10.2355/isijinternational.54.2598 · Cited by 41
Chlorella biomass as feed supplement for freshwater fish: sterlet,Acipenser ruthenus
Sergejevová, M., & Masojídek, J. (2012) · Aquaculture Research · doi:10.1111/j.1365-2109.2011.03011.x · Cited by 40
Kao, S.-P., Chang, Y.-C., & Wang, F.-L. (2023) · Sensors · doi:10.3390/s23052572 · Cited by 40
Zong, C., & Jafari, R. (2015) · 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7320268 · Cited by 39
Genetic Neural Architecture Search for automatic assessment of human sperm images
Miahi, E., Mirroshandel, S. A., & Nasr, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115937 · Cited by 39
Yazdchi, M. R., Mahyari, A. G., & Nazeri, A. (2008) · 2008 International Conference on Computational Intelligence for Modelling Control & Automation · doi:10.1109/cimca.2008.130 · Cited by 39
Carrillo-Perez, F., Pizurica, M., Zheng, Y., Nandi, T. N., Madduri, R., Shen, J., & Gevaert, O. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01193-8 · Cited by 37
Patkar, S., Chen, A., Basnet, A., Bixby, A., Rajendran, R., Chernet, R., Faso, S., Kumar, P. A., Desai, D., El-Zammar, O., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00765-w · Cited by 41
“All WSI in the experiments were first preprocessed to mask out the tumor tissue from background using RGB to HSV color transformation, median blurring and Otsu thresholding 51 .”
Detecting unfocused raindrops: In-vehicle multipurpose cameras
Cord, A., & Gimonet, N. (2014) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2013.2287451 · Cited by 35
Colón, J. M., Torrado, A. I., Cajigas, Á., Santiago, J. M., Salgado, I. K., Arroyo, Y., & Miranda, J. D. (2016) · Journal of Neurotrauma · doi:10.1089/neu.2015.4111 · Cited by 35
Auclair, Y., Rouget, R., Belisle, J. M., Costantino, S., & Drobetsky, E. A. (2010) · DNA Repair · doi:10.1016/j.dnarep.2010.03.013 · Cited by 34
Qiu, L., Zhao, L., Hou, R., Zhao, W., Zhang, S., Lin, Z., Teng, H., & Zhao, J. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102176 · Cited by 35
Three-stage segmentation of lung region from CT images using deep neural networks
Osadebey, M., Andersen, H. K., Waaler, D., Fossaa, K., Martinsen, A. C. T., & Pedersen, M. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00640-1 · Cited by 34
Selective Search and Sequential Detection for Standard Plane Localization in Ultrasound
Ni, D., Li, T., Yang, X., Qin, J., Li, S., Chin, C.-T., Ouyang, S., Wang, T., & Chen, S. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-41083-3_23 · Cited by 33
MacLean, M., Juranek, J., Cuddapah, S., López-Díez, R., Ruiz, H. H., Hu, J., Frye, L., Li, H., Gugger, P. F., & Schmidt, A. M. (2021) · Journal of Neuroinflammation · doi:10.1186/s12974-021-02191-2 · Cited by 33
Phosphoregulation of DSB-1 mediates control of meiotic double-strand break activity
Guo, H., Stamper, E. L., Sato-Carlton, A., Shimazoe, M. A., Li, X., Zhang, L., Stevens, L., Tam, K. J., Dernburg, A. F., & Carlton, P. M. (2022) · eLife · doi:10.7554/elife.77956 · Cited by 32
Meta-Evaluation of Image Segmentation Using Machine Learning
Zhang, H., Cholleti, S., Goldman, S. A., & Fritts, J. E. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.185 · Cited by 32
Acute neuroinflammation induces AIS structural plasticity in a NOX2-dependent manner
Benusa, S. D., George, N. M., Sword, B. A., DeVries, G. H., & Dupree, J. L. (2017) · Journal of Neuroinflammation · doi:10.1186/s12974-017-0889-3 · Cited by 31
Impact of new ingredients obtained from brewer’s spent yeast on bread characteristics
Martins, Z. E., Pinho, O., & Ferreira, I. M. P. L. V. O. (2018) · Journal of Food Science and Technology · doi:10.1007/s13197-018-3107-0 · Cited by 31
Detection of perineural invasion in prostate needle biopsies with deep neural networks
Kartasalo, K., Ström, P., Ruusuvuori, P., Samaratunga, H., Delahunt, B., Tsuzuki, T., Eklund, M., & Egevad, L. (2022) · Virchows Archiv · doi:10.1007/s00428-022-03326-3 · Cited by 29
Kim, Y.-G., Choi, G., Go, H., Cho, Y., Lee, H., Lee, A.-R., Park, B., & Kim, N. (2019) · Scientific Reports · doi:10.1038/s41598-019-41479-5 · Cited by 29
Forensic Analysis of Synthetically Generated Western Blot Images
Mandelli, S., Cozzolino, D., Cannas, E. D., Cardenuto, J. P., Moreira, D., Bestagini, P., Scheirer, W. J., Rocha, A., Verdoliva, L., Tubaro, S., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3179116 · Cited by 26
“For each image, we exploit Otsu’s image thresholding [26] andWatershed segmentation [27] algorithms to automatically obtain possible blot-masks associatedwith the image, thenwe pick the best mask by visual inspection.”
MethodSaltman, L. H., Javed, A., Ribadeneyra, J., Hussain, S., Young, D. W., Osdoby, P., Amcheslavsky, A., van Wijnen, A. J., Stein, J. L., Stein, G. S., et al. (2005) · Journal of Cellular Physiology · doi:10.1002/jcp.20329 · Cited by 28
EUV-induced hydrogen desorption as a step towards large-scale silicon quantum device patterning
Constantinou, P., Stock, T. J. Z., Tseng, L.-T., Kazazis, D., Muntwiler, M., Vaz, C. A. F., Ekinci, Y., Aeppli, G., Curson, N. J., & Schofield, S. R. (2024) · Nature Communications · doi:10.1038/s41467-024-44790-6 · Cited by 27
Enhancing elderly care: Efficient and reliable real-time fall detection algorithm
Wang, Y., & Deng, T. (2024) · DIGITAL HEALTH · doi:10.1177/20552076241233690 · Cited by 28
Breast MRI and X-ray mammography registration using gradient values
García, E., Diez, Y., Diaz, O., Lladó, X., Gubern-Mérida, A., Martí, R., Martí, J., & Oliver, A. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.02.013 · Cited by 26
Prediction of protein subcellular localization in single cells
(2025) · doi:10.1038/s41592-025-02696-1 · Cited by 25
Image processing for frost thickness measurement in fin-and-tube heat exchangers
Lee, W.-J., & Kyung Kwon, O. (2021) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2021.100937 · Cited by 25
SoRelle, E. D., Yecies, D. W., Liba, O., Bennett, F. C., Graef, C. M., Dutta, R., Mitra, S., Joubert, L.-M., Cheshier, S., Grant, G. A., et al. (2019) · ACS Nano · doi:10.1021/acsnano.9b02656 · Cited by 24
A novel framework for segmentation of small targets in medical images
Zhao, L., Wang, T., Chen, Y., Zhang, X., Tang, H., Lin, F., Li, C., Li, Q., Tan, T., Kang, D., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-94437-9 · Cited by 24
Quantification of α-Gal Antigen Removal in the Porcine Dermal Tissue by α-Galactosidase
Gao, H.-W., Li, S.-B., Sun, W. Q., Yun, Z.-M., Zhang, X., Song, J.-W., Zhang, S.-K., Leng, L., Ji, S.-P., Tan, Y.-X., et al. (2015) · Tissue Engineering Part C: Methods · doi:10.1089/ten.tec.2015.0129 · Cited by 23
Naraghi, S., Mutsvangwa, T., Goliath, R., Rangaka, M. X., & Douglas, T. S. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.009 · Cited by 22
Wu, Y., Rocha, B. M., Kaimakamis, E., Cheimariotis, G.-A., Petmezas, G., Chatzis, E., Kilintzis, V., Stefanopoulos, L., Pessoa, D., Marques, A., et al. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121089 · Cited by 23
A Review of Detection and Removal of Raindrops in Automotive Vision Systems
Hamzeh, Y., & Rawashdeh, S. A. (2021) · Journal of Imaging · doi:10.3390/jimaging7030052 · Cited by 22
“and applying Ostu’s method [39] to segment the image into dark and bright regions.”
MethodComputing Precision and Recall with Missing or Uncertain Ground Truth
Lamiroy, B., & Sun, T. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-36824-0_15 · Cited by 22
Zhang, J., Wang, Y., Donarski, E. D., Toma, T. T., Miles, M. T., Acton, S. T., & Gahlmann, A. (2022) · npj Biofilms and Microbiomes · doi:10.1038/s41522-022-00362-4 · Cited by 22
A simulation-based few samples learning method for surface defect segmentation
Wei, T., Cao, D., Zheng, C., & Yang, Q. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.090 · Cited by 21
Removal of adherent noises from image sequences by spatio-temporal image processing
Yamashita, A., Fukuchi, I., Kaneko, T., & Miura, K. T. (2008) · 2008 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2008.4543570 · Cited by 21
Abbas, W., & Masip Rodo, D. (2019) · Sensors · doi:10.3390/s19153274 · Cited by 21
Goff, M. G., Lambers, F. M., Sorna, R. M., Keaveny, T. M., & Hernandez, C. J. (2015) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2015.10.023 · Cited by 20
Chen, J., Wu, J., He, J., & Guo, Z. (2022) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2021.11.008 · Cited by 20
A rapid smartphone-based lactate dehydrogenase test for neonatal diagnostics at the point of care
Halvorsen, C. P., Olson, L., Araújo, A. C., Karlsson, M., Nguyễn, T. T., Khu, D. T. K., Le, H. T. T., Nguyễn, H. T. B., Winbladh, B., & Russom, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-45606-0 · Cited by 19
Colón, J. M., González, P. A., Cajigas, Á., Maldonado, W. I., Torrado, A. I., Santiago, J. M., Salgado, I. K., & Miranda, J. D. (2018) · Experimental Neurology · doi:10.1016/j.expneurol.2017.10.006 · Cited by 19
Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir
Cardoni, M., Pau, D. P., Falaschetti, L., Turchetti, C., & Lattuada, M. (2021) · Electronics · doi:10.3390/electronics10222836 · Cited by 17
Adnan, G., Rubikaite, A., Khan, M., Reber, M., Suetterlin, P., Hindges, R., & Drescher, U. (2020) · The Journal of Neuroscience · doi:10.1523/jneurosci.1759-19.2020 · Cited by 17
(2009) · 2009 ICCAS-SICE · Cited by 15
Visible and Infrared Imaging Based Inspection of Power Installation
Jalil, B., Pascali, M. A., Leone, G. R., Martinelli, M., Moroni, D., Salvetti, O., & Berton, A. (2019) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661819010140 · Cited by 14
Automated visual inspection of an airplane exterior
Jovančević, I., Orteu, J.-J., Sentenac, T., & Gilblas, R. (2015) · SPIE Proceedings · doi:10.1117/12.2182811 · Cited by 13
Amyot, R., Miyashita, O., Wu, X., Takeda, K., Kodera, N., Konno, H., Tama, F., & Flechsig, H. (2025) · ACS Nano · doi:10.1021/acsnano.5c10073 · Cited by 13
Recovery of neural activity from nerve cuff electrodes
Wodlinger, B., & Durand, D. M. (2011) · 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2011.6091152 · Cited by 13
Dropout-Enabled Ensemble Learning for Multi-scale Biomedical Data
Momeni, A., Thibault, M., & Gevaert, O. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_41 · Cited by 12
Voice Biomarkers of Recovery From Acute Respiratory Illness
Tracey, B., Patel, S., Zhang, Y., Chappie, K., Volfson, D., Parisi, F., Adans-Dester, C., Bertacchi, F., Bonato, P., & Wacnik, P. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3137050 · Cited by 10
Image Processing Contrast Enhancement
Grigoryan, A. M., & Agaian, S. S. (2017) · Wiley Encyclopedia of Electrical and Electronics Engineering · doi:10.1002/047134608x.w5525.pub2 · Cited by 10
Kurz, A., Krahl, D., Kutzner, H., Barnhill, R., Perasole, A., Figueras, M. T. F., Ferrara, G., Braun, S. A., Starz, H., Llamas-Velasco, M., et al. (2023) · European Journal of Cancer · doi:10.1016/j.ejca.2023.113294 · Cited by 10
(2019) · Cited by 10
Park, C., & Won, S. (2009) · 2009 35th Annual Conference of IEEE Industrial Electronics · doi:10.1109/iecon.2009.5415248 · Cited by 10
Juhola, V., Mikkola, V., Inkeri, E., Kabelac, S., & Tynjälä, T. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.127396 · Cited by 9
Li, M., Zhao, C., Duan, Z., Cheng, H., Lian, Y., & Wang, G. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131913 · Cited by 9
A Novel Cognitively Inspired Deep Learning Approach to Detect Drivable Areas for Self-driving Cars
Jiang, F., Wang, Z., & Yue, G. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10215-7 · Cited by 9
Sangnark, S., Rattanachaisit, P., Patcharatrakul, T., & Vateekul, P. (2024) · IEEE Access · doi:10.1109/access.2024.3409077 · Cited by 8
Kennedy, L., Morland, C., Narum, M., Bergersen, L. H., & Rinholm, J. E. (2025) · eneuro · doi:10.1523/eneuro.0563-24.2025 · Cited by 8
Acharya, P., Lloyd, C., Lam, N. T., Kumke, J., Ray, S., Yanira Muñoz Ramirez, Z., Das, S., & Lam, H. N. (2026) · mSphere · doi:10.1128/msphere.00870-25 · Cited by 8
Metadata
- Authors
- Otsu, N.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics
- Published
- 1979-01
- Volume
- 9
- Issue
- 1
- Pages
- 62-66
- ISSN
- 0018-9472 (ISSN-L)
- 2168-2909
- OpenAlex
- W2133059825
- Cited by
- 43,398
- References
- 0
- FWCI
- 4.52
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.