The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
A geometric model for active contours in image processing
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
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,070
Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
Osher, S., & Sethian, J. A. (1988) · Journal of Computational Physics · doi:10.1016/0021-9991(88)90002-2 · Cited by 14,062
Linear and Quasi-linear Equations of Parabolic Type
Ladyženskaja, O., Solonnikov, V., & Ural′ceva, N. (1968) · Translations of Mathematical Monographs · doi:10.1090/mmono/023 · Cited by 7,490
Marr, D., & Hildreth, E. (1980) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1980.0020 · Cited by 6,510
User’s guide to viscosity solutions of second order partial differential equations
Crandall, M. G., Ishii, H., & Lions, P.-L. (1992) · Bulletin of the American Mathematical Society · doi:10.1090/s0273-0979-1992-00266-5 · Cited by 4,781
Hirsch, M. W. (1976) · Graduate Texts in Mathematics · doi:10.1007/978-1-4684-9449-5 · Cited by 2,520
Image Selective Smoothing and Edge Detection by Nonlinear Diffusion. II
Alvarez, L., Lions, P.-L., & Morel, J.-M. (1992) · SIAM Journal on Numerical Analysis · doi:10.1137/0729052 · Cited by 2,208
On active contour models and balloons
Cohen, L. D. (1991) · CVGIP: Image Understanding · doi:10.1016/1049-9660(91)90028-n · Cited by 2,153
Blake, A., & Zisserman, A. (1987) · The MIT Press eBooks · doi:10.7551/mitpress/7132.001.0001 · Cited by 2,105
Computational vision and regularization theory
Poggio, T., Torre, V., & Koch, C. (1985) · Nature · doi:10.1038/317314a0 · Cited by 1,412
Cited by
Chan, T. F., & Vese, L. A. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.902291 · Cited by 10,304
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.015 · Cited by 8,868
Snakes, shapes, and gradient vector flow
Xu, C., & Prince, J. L. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.661186 · Cited by 4,001
Shape modeling with front propagation: a level set approach
Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.368173 · Cited by 3,158
Current Methods in Medical Image Segmentation
Pham, D. L., Xu, C., & Prince, J. L. (2000) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.2.1.315 · Cited by 2,341
Distance Regularized Level Set Evolution and Its Application to Image Segmentation
Li, C., Xu, C., Gui, C., & Fox, M. D. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2069690 · Cited by 2,106
Deformable models in medical image analysis: a survey
McInerney, T., & Terzopoulos, D. (1996) · Medical Image Analysis · doi:10.1016/s1361-8415(96)80007-7 · Cited by 1,913
Li, C., Huang, R., Ding, Z., Gatenby, J. C., Metaxas, D. N., & Gore, J. C. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2146190 · Cited by 1,285
Caselles, V., Kimmel, R., & Sapiro, G. (2002) · Proceedings of IEEE International Conference on Computer Vision · doi:10.1109/iccv.1995.466871 · Cited by 1,161
Self-citationA shape-based approach to the segmentation of medical imagery using level sets
Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W. E., & Willsky, A. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.808355 · Cited by 894
State of the art survey on MRI brain tumor segmentation
Gordillo, N., Montseny, E., & Sobrevilla, P. (2013) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2013.05.002 · Cited by 697
Active Contours without Edges for Vector-Valued Images
Chan, T. F., Sandberg, B. Y., & Vese, L. A. (2000) · Journal of Visual Communication and Image Representation · doi:10.1006/jvci.1999.0442 · Cited by 687
Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics
Moccia, S., De Momi, E., El Hadji, S., & Mattos, L. S. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.02.001 · Cited by 567
Three-dimensional modeling for functional analysis of cardiac images, a review
Frangi, A. F., Niessen, W. J., & Viergever, M. A. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906421 · Cited by 533
Behavioral analysis of anisotropic diffusion in image processing
You, Y.-L., Xu, W., Tannenbaum, A., & Kaveh, M. (1996) · IEEE Transactions on Image Processing · doi:10.1109/83.541424 · Cited by 490
Patient-Specific Modeling of Cardiovascular Mechanics
Taylor, C. A., & Figueroa, C. A. (2009) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.10.061807.160521 · Cited by 451
An algorithmic overview of surface registration techniques for medical imaging
Audette, M. A., Ferrie, F. P., & Peters, T. M. (2000) · Medical Image Analysis · doi:10.1016/s1361-8415(00)00014-1 · Cited by 406
Using Prior Shapes in Geometric Active Contours in a Variational Framework
Chen, Y., Tagare, H. D., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K. S., Briggs, R. W., & Geiser, E. A. (2002) · International Journal of Computer Vision · doi:10.1023/a:1020878408985 · Cited by 406
Wang, L., Li, C., Sun, Q., Xia, D., & Kao, C.-Y. (2009) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2009.04.010 · Cited by 405
A review on segmentation of positron emission tomography images
Foster, B., Bagci, U., Mansoor, A., Xu, Z., & Mollura, D. J. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.04.014 · Cited by 370
A review of remote sensing applications for water security: Quantity, quality, and extremes
Chawla, I., Karthikeyan, L., & Mishra, A. K. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124826 · Cited by 363
Geodesic Active Regions: A New Framework to Deal with Frame Partition Problems in Computer Vision
Paragios, N., & Deriche, R. (2002) · Journal of Visual Communication and Image Representation · doi:10.1006/jvci.2001.0475 · Cited by 358
Deformable models in medical image analysis
McInerney, T., & Terzopoulos, D. (1996) · Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis · doi:10.1109/mmbia.1996.534069 · Cited by 329
Automatic breast ultrasound image segmentation: a survey
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
“Yezzi et al. [42] modified traditional GDMs to have an additional term (〈∇ , 〉) that provided stronger stopping power at object edges.”
BackgroundInfluentialPerformance measure characterization for evaluating neuroimage segmentation algorithms
Chang, H.-H., Zhuang, A. H., Valentino, D. J., & Chu, W.-C. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.068 · Cited by 262
User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP
Yushkevich, P. A., Pashchinskiy, A., Oguz, I., Mohan, S., Schmitt, J. E., Stein, J. M., Zukić, D., Vicory, J., McCormick, M., Yushkevich, N., et al. (2019) · Neuroinformatics · doi:10.1007/s12021-018-9385-x · Cited by 175
A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes
Li, C., Wang, X., Eberl, S., Fulham, M., Yin, Y., Chen, J., & Feng, D. D. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2267212 · Cited by 139
(2013) · Liverpool John Moores University · Cited by 133
Yang, J., Li, X., Xu, J., Cao, Y., Zhang, Y., Wang, L., & Jiang, S. (2018) · Applied Optics · doi:10.1364/ao.57.002490 · Cited by 29
Metadata
- Authors
- Caselles, V., Catt�, F., Coll, T., & Dibos, F.
- Venue
- Numerische Mathematik
- Published
- 1993-12
- Volume
- 66
- Issue
- 1
- Pages
- 1-31
- ISSN
- 0029-599X (ISSN-L)
- 0945-3245
- OpenAlex
- W2144133758
- Cited by
- 1,743
- References
- 23
- FWCI
- 17.86
- 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.
