Comparing images using the Hausdorff distance

Image registration methods: a survey

Zitová, B., & Flusser, J. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(03)00137-9 · Cited by 6,423 · see citing works

Shape matching and object recognition using shape contexts

Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306 · see citing works

One-shot learning of object categories

Fei-Fei, L., Fergus, R., & Perona, P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.79 · Cited by 3,029 · see citing works

A new point matching algorithm for non-rigid registration

Chui, H., & Rangarajan, A. (2003) · Computer Vision and Image Understanding · doi:10.1016/s1077-3142(03)00009-2 · Cited by 1,560 · see citing works

A modified Hausdorff distance for object matching

Dubuisson, M.-P., & Jain, A. K. (2002) · Proceedings of 12th International Conference on Pattern Recognition · doi:10.1109/icpr.1994.576361 · Cited by 1,464 · see citing works

Comparison and evaluation of methods for liver segmentation from CT datasets

Heimann, T., van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2013851 · Cited by 1,135 · see citing works

Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis

Crum, W. R., Camara, O., & Hill, D. L. G. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.880587 · Cited by 776 · see citing works

Segmentation-Driven 6D Object Pose Estimation

Hu, Y., Hugonot, J., Fua, P., & Salzmann, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00350 · Cited by 333 · see citing works

Automated measurement of fetal head circumference using 2D ultrasound images

van den Heuvel, T. L. A., de Bruijn, D., de Korte, C. L., & Ginneken, B. V. (2018) · PLOS ONE · doi:10.1371/journal.pone.0200412 · Cited by 280 · see citing works

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Mendrik, A. M., Vincken, K. L., Kuijf, H. J., Breeuwer, M., Bouvy, W. H., de Bresser, J., Alansary, A., de Bruijne, M., Carass, A., El-Baz, A., et al. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/813696 · Cited by 261 · see citing works

Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning Data

Guan, H., Li, J., Yu, Y., Chapman, M., Wang, H., Wang, C., & Zhai, R. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2344714 · Cited by 112 · see citing works

GIST: an interactive, GPU-based level set segmentation tool for 3D medical images

CATES, J., LEFOHN, A., & WHITAKER, R. (2004) · Medical Image Analysis · doi:10.1016/j.media.2004.06.022 · Cited by 102 · see citing works

Deep-learning-based Accurate Hepatic Steatosis Quantification for Histological Assessment of Liver Biopsies

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 · see citing works

A Systematic Review of Contouring Guidelines in Radiation Oncology: Analysis of Frequency, Methodology, and Delivery of Consensus Recommendations

Lin, D., Lapen, K., Sherer, M. V., Kantor, J., Zhang, Z., Boyce, L. M., Bosch, W., Korenstein, D., & Gillespie, E. F. (2020) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2020.04.011 · Cited by 59 · see citing works

Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data

Chen, H., Liu, T., Zhao, Y., Zhang, T., Li, Y., Li, M., Zhang, H., Kuang, H., Guo, L., Tsien, J. Z., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.050 · Cited by 46 · see citing works

Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning

Hesse, L. S., Aliasi, M., Moser, F., Haak, M. C., Xie, W., Jenkinson, M., & Namburete, A. I. L. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119117 · Cited by 44 · see citing works

Onboard Autonomous rock shape analysis for Mars rovers

Fox, J., Castano, R., & Anderson, R. C. (2003) · Proceedings, IEEE Aerospace Conference · doi:10.1109/aero.2002.1035371 · Cited by 38 · see citing works

Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics

Ma, C., Chitra, U., Zhang, S., & Raphael, B. J. (2022) · Cell Systems · doi:10.1016/j.cels.2022.09.002 · Cited by 27 · see citing works

Deep learning on sparse manifolds for faster object segmentation

Nascimento, J. C., & Carneiro, G. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2725582 · Cited by 17 · see citing works

Fully Automated Placental Volume Quantification From<scp>3D</scp>Ultrasound for Prediction of Small‐for‐Gestational‐Age Infants

Schwartz, N., Oguz, I., Wang, J., Pouch, A., Yushkevich, N., Parameshwaran, S., Gee, J., Yushkevich, P., & Oguz, B. (2022) · Journal of Ultrasound in Medicine · doi:10.1002/jum.15835 · Cited by 11 · see citing works

Uncertainty-aware segmentation quality prediction via deep learning Bayesian Modeling: Comprehensive evaluation and interpretation on skin cancer and liver segmentation

O.K., S., Riera-Marín, M., Galdran, A., López, J. G., Rodríguez-Comas, J., Piella, G., & Ballester, M. A. G. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102547 · Cited by 11 · see citing works

Fréchet Kernel for Trajectory Data Analysis

Takeuchi, K., Imaizumi, M., Kanda, S., Tabei, Y., Fujii, K., Yoda, K., Ishihata, M., & Maekawa, T. (2021) · Proceedings of the 29th International Conference on Advances in Geographic Information Systems · doi:10.1145/3474717.3483949 · Cited by 4 · see citing works