An Analysis of Two-Dimensional Image Data Using a Grouping Estimator — Oak Academic Publishing
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An Analysis of Two-Dimensional Image Data Using a Grouping Estimator
Graduate School of Engineering, University of Tokyo, Bunkyo-ku, Tokyo, Japan
,
Research Center for Health Policy and Economics, Hitotsubashi Institute for Advanced Study (HIAS), Hitotsubashi University, Kunitachi-shi, Tokyo, Japan
1 Graduate School of Engineering, University of Tokyo, Bunkyo-ku, Tokyo, Japan
2 Research Center for Health Policy and Economics, Hitotsubashi Institute for Advanced Study (HIAS), Hitotsubashi University, Kunitachi-shi, Tokyo, Japan
Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.
KeywordsTwo-Dimensional Image AnalysisHigh-Resolution and Low-Resolution Im-agesSemiparametric EstimatorMachine LearningGrouping Estimator
De la Calleja, J. and Fuentes, O. (2004) Machine Learning and Image Analysis for Morphological Galaxy Classification. Monthly Notices of the Royal Astronomical Society, 349, 87-93. https://doi.org/10.1111/j.1365-2966.2004.07442.x
Duro, D.C., Franklin, S.E. and Dubé, M.G. (2012) A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery. Remote Sensing of Environment, 118, 259-272. https://doi.org/10.1016/j.rse.2011.11.020
Li, X., Cheng, X., Chen, W., et al. (2015) Identification of Forested Landslides Using LiDar Data, Object-Based Image Analysis, and Machine Learning Algorithms. Remote Sensing, 7, 9705-9726. https://doi.org/10.3390/rs70809705
Arganda-Carreras, I., Kaynig, V., Rueden, C., et al. (2017) Trainable Weka Segmentation: A Machine Learning Tool for Microscopy Pixel Classification. Bioinformatics, 33, 2424-2426. https://doi.org/10.1093/bioinformatics/btx180
Kan, A. (2017) Machine Learning Applications in Cell Image Analysis. Immunology and Cell Biology, 95, 525-530. https://doi.org/10.1038/icb.2017.16
Zhang, Y.C. and Kagen, A.C. (2017) Machine Learning Interface for Medical Image Analysis. Journal of Digital Imaging, 30, 615-621. https://doi.org/10.1007/s10278-016-9910-0
Bulat, A. and Tzimiropoulos, G. (2018) Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses with GANs. 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, 18-23 June 2018, 109-117. https://doi.org/10.1109/CVPR.2018.00019
Komura, D. and Ishikawa, S. (2019) Machine Learning Approaches for Pathologic Diagnosis. Virchows Archiv, 475, 131-138. https://doi.org/10.1007/s00428-019-02594-w
Bi, Q., Goodman, K.E., Kaminsky, J., et al. (2020) What Is Machine Learning? A Primer for the Epidemiologist. American Journal of Epidemiology, 188, 2222-2239. https://doi.org/10.1093/aje/kwz189
Kose, K., Bozkurt, A., Alessi-Fox, C., et al. (2020) Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy. Journal of Investigative Dermatology, 140, 1214-1222. https://doi.org/10.1016/j.jid.2019.10.018
Tang, H. and Hu, Z. (2020) Research on Medical Image Classification Based on Machine Learning. IEEE Access, 8, 93145-93154. https://doi.org/10.1109/ACCESS.2020.2993887
Turner, O.C., Aeffner, F. and Bangari, D.S. (2020) Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology. Toxicologic Pathology, 48, 277-294. https://doi.org/10.1177/0192623319881401
Wei, P.W., He, F. and Zou, Y. (2020) Content Semantic Image Analysis and Storage Method Based on Intelligent Computing of Machine Learning Annotation. Neural Computing and Applications, 32, 1813-1822. https://doi.org/10.1007/s00521-020-04739-4
Li, J., Shao, S. and Hong, J. (2021) Machine Learning Shadowgraph for Particle Size and Shape Characterization. Measurement Science and Technology, 32, Article ID: 015406. https://doi.org/10.1088/1361-6501/abae90
Santhi, K. and Reddy, A.R.M. (2021) An Automated Framework for Coronary Analysis from Coronary Cine Angiograms Using Machine Learning and Image Analysis Techniques. IT in Industry, 9, 1406-1412. https://doi.org/10.17762/itii.v9i1.284
Sato, S., Maki, S., Yamanaka, T., et al. (2021) Machine Learning-Based Image Analysis for Accelerating the Diagnosis of Complicated Preneoplastic and Neoplastic Ductal Lesions in Breast Biopsy Tissues. Breast Cancer Research and Treatment, 188, 649-659. https://doi.org/10.1007/s10549-021-06243-2
Botero, U.J., Lson, R., Lu, H., et al. (2021) Hardware Trust and Assurance through Reverse Engineering: A Tutorial and Outlook from Image Analysis and Machine Learning Perspectives. ACM Journal on Emerging Technologies in Computing Systems, 17, Article 62. https://doi.org/10.1145/3464959
Tang, X., Kusmartseva, I., Kulkarni, S., et al. (2021) Image-Based Machine Learning Algorithms for Disease Characterization in the Human Type 1 Diabetes Pancreas. American Journal of Pathology, 191, 454-462. https://doi.org/10.1016/j.ajpath.2020.11.010
Wang, P., Fan, E. and Wang, P. (2021) Comparative Analysis of Image Classification Algorithms Based on Traditional Machine Learning and Deep Learning. Pattern Recognition Letters, 141, 61-67. https://doi.org/10.1016/j.patrec.2020.07.042
Liakos, K.G., Busato, P., Moshou, D., et al. (2018) Machine Learning in Agriculture: A Review. Sensors, 18, 2674. https://doi.org/10.3390/s18082674
Gewali, U.B., Monteiro, S.T. and Saber, E. (2019) Machine Learning Based Hyperspectral Image Analysis: A Survey.
Martin-Isla, C., Campello, V.M., Izquierdo, C., et al. (2020) Image-Based Cardiac Diagnosis with Machine Learning: A Review. Frontiers in Cardiovascular Medicine, 7, Article No. 1. https://doi.org/10.3389/fcvm.2020.00001
Zahia, S., Zapirain, M.B.G. and Sevillano, X. (2020) Pressure Injury Image Analysis with Machine Learning Techniques: A systematic Review on Previous and Possible Future Methods. Artificial Intelligence in Medicine, 102, Article ID: 101742. https://doi.org/10.1016/j.artmed.2019.101742
de Matos, J., Ataky, S.T.M., de Souza Britto Jr., A., et al. (2021) Machine Learning Methods for Histopathological Image Analysis: A Review. Electronics, 10, 562. https://doi.org/10.3390/electronics10050562
Brown, S. (2021) Machine Learning, Explained. MIT Sloan School of Management, Cambridge. https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained
Mahesh, B. (2020) Machine Learning Algorithms—A Review. International Journal of Science and Research, 9, 381-386.
Ma, X., Niu, Y., Gu, L., Wang, Y., et al. (2021) Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems. American Journal of Pathology, 110, Article ID: 107332. https://doi.org/10.1016/j.patcog.2020.107332
Nawata, K. (1994) Estimation of the Boundary of the Two Regions by the Grouping Method. Journal of the Japan Statistical Society, 24, 14-35.
Nawata, K. (1990) Robust Estimation Based on Grouped-Adjusted Data in Linear Regression Models. Journal of Econometrics, 43, 317-336. https://doi.org/10.1016/0304-4076(90)90123-B
Nawata, K. (1990) Robust Estimation Based on Grouped-Adjusted Data in Censored Regression Models. Journal of Econometrics, 43, 337-362. https://doi.org/10.1016/0304-4076(90)90124-C
Amemiya, T. (1985) Advanced Econometrics. Harvard University Press, Cambridge.
Miguel-Hurtado, O., Guest, R., Stevenage, S.V., et al. (2016) Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics. PLoS ONE, 11, e0165521. https://doi.org/10.1371/journal.pone.0165521
Bennett, G. (1962) Probability Inequalities for Sum of Independent Random Variables. Journal of the American Statistical Association, 57, 33-45. https://doi.org/10.1080/01621459.1962.10482149
Mojica, E., Pertuz, S. and Arguello, H. (2017) High-Resolution Coded-Aperture Design for Compressive X-Ray tomography Using Low Resolution Detectors. Optic Communications, 404, 103-109. https://doi.org/10.1016/j.optcom.2017.06.053
Singh, S., Guo, Y., Winiarski, B., et al. (2018) High Resolution Low kV EBSD of Heavily Deformed and Nanocrystalline Aluminum by Dictionary-Based Indexing. Scientific Reports, 8, Article No. 10991. https://doi.org/10.1038/s41598-018-29315-8
Li, Y., Xu, L., et al. (2019) A Y-Net Deep Learning Method for Road Segmentation Using High-Resolution Visible Remote Sensing Images. Remote Sensing Letters, 10, 381-390. https://doi.org/10.1080/2150704X.2018.1557791
Alganci, U., Soydas, M. and Sertel, E. (2020) Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images. Remote Sensing, 12, 458. https://doi.org/10.3390/rs12030458
Yi, Z., Tang, Q., Azizi, S., et al. (2020) Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 14-19 June 2020, 7508-7517. https://doi.org/10.1109/CVPR42600.2020.00753
Cao, Y. and Huang, X. (2021) A Deep Learning Method for Building Height Estimation Using High-Resolution Multi-View Imagery over Urban Areas: A Case Study of 42 Chinese Cities. Remote Sensing of Environment, 264, Article ID: 112590. https://doi.org/10.1016/j.rse.2021.112590
Giles, A.B., Davies, J.E., Ren, K., et al. (2021) A Deep Learning Algorithm to Detect and Classify Sun Glint from High-Resolution Aerial Imagery over Shallow Marine Environments. Journal of Photogrammetry and Remote Sensing, 181, 20-26. https://doi.org/10.1016/j.isprsjprs.2021.09.004
Horwath, J.P., Zakharov, D.N., Mégret, R., et al. (2021) Understanding Important Features of Deep Learning Models for Segmentation of High-Resolution Transmission Electron Microscopy Images. Computational Materials, 6, Article No. 108. https://doi.org/10.1038/s41524-020-00363-x
Wen, Q., Luo, Z., Chen, R., et al. (2021) Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators. Sensors, 21, 1033. https://doi.org/10.3390/s21041033
Zamboni, P., Marcato Junior, J., de Andrade Silva, J., et al. (2021) Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images. Remote Sensing, 13, 2482. https://doi.org/10.3390/rs13132482
Karsa, A., Punwani, S. and Shmueli, K. (2018) The Effect of Low Resolution and Coverage on the Accuracy of Susceptibility Mapping. Magnetic Resonace in Medicine, 81, 1833-1848. https://doi.org/10.1002/mrm.27542
Yu, X., Fernando, B., Hartley, R. and Porikli, F. (2018) Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-23 June 2018, 908-917. https://doi.org/10.1109/CVPR.2018.00101
Kuhlbrodt, T., Jones, C.G., Sellar, A., et al. (2018) The Low-Resolution Version of HadGEM3 GC3.1: Development and Evaluation for Global Climate. Journal of Advances in Modeling Earth Systems, 10, 2865-2888. https://doi.org/10.1029/2018MS001370
Wang, S., Zhang, K., et al. (2020) Physically-Based Landslide Prediction over a Large Region: Scaling Low-Resolution Hydrological Model Results for High-Resolution Slope, Stability Assessment. Environmental Modelling and Software, 124, Article ID: 104607. https://doi.org/10.1016/j.envsoft.2019.104607
Shao, Z., Zhou, Z., Huang, X., et al. (2021) MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images. Remote Sensing, 13, 239. https://doi.org/10.3390/rs13020239
Xu, H., Li, X., Zhang, K., et al. (2021) SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting. Algorithms, 14, 236. https://doi.org/10.3390/a14080236
Weilharter, A. and Fraudorfer, F. (2021) HighRes-MVSNet: A Fast Multi-View Stereo Network for Dense 3D Reconstruction from High-Resolution Images. IEEE Access, 9, 11306-11315. https://doi.org/10.1109/ACCESS.2021.3050556
Saito, S., Simon, T., Saragih, J., et al. (2020) PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 13-19 June 2020, 84-93. https://doi.org/10.1109/CVPR42600.2020.00016