The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Finite-element methods for active contour models and balloons for 2-D and 3-D images
Open accessAbstract
The use of energy-minimizing curves, known as "snakes" to extract features of interest in images has been introduced by Kass, Witkin and Terzopoulos (1987). A balloon model was introduced by Cohen (1991) as a way to generalize and solve some of the problems encountered with the original method. A 3-D generalization of the balloon model as a 3-D deformable surface, which evolves in 3-D images, is presented. It is deformed under the action of internal and external forces attracting the surface toward detected edgels by means of an attraction potential. We also show properties of energy-minimizing surfaces concerning their relationship with 3-D edge points. To solve the minimization problem for a surface, two simplified approaches are shown first, defining a 3-D surface as a series of 2-D planar curves. Then, after comparing finite-element method and finite-difference method in the 2-D problem, we solve the 3-D model using the finite-element method yielding greater stability and faster convergence. This model is applied for segmenting magnetic resonance images.>
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
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,070
Solutions of Ill-Posed Problems.
Bell, J. B., Tikhonov, A. N., & Arsenin, V. Y. (1978) · Mathematics of Computation · doi:10.2307/2006360 · Cited by 8,101
Differential geometry of curves and surfaces
(2011) · Choice Reviews Online · doi:10.5860/choice.48-3330 · Cited by 3,706
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
Self-citationBlake, A., & Zisserman, A. (1987) · The MIT Press eBooks · doi:10.7551/mitpress/7132.001.0001 · Cited by 2,105
Danielsson, P.-E. (1980) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(80)90054-4 · Cited by 1,998
Numerical Methods for Nonlinear Variational Problems
Glowinski, R. (1984) · Springer eBooks · doi:10.1007/978-3-662-12613-4 · Cited by 1,728
Numerical Methods for Nonlinear Variational Problems
Glowinski, R., & Oden, J. T. (1985) · Journal of Applied Mechanics · doi:10.1115/1.3169136 · Cited by 1,541
Feature extraction from faces using deformable templates
Yuille, A. L., Hallinan, P. W., & Cohen, D. S. (1992) · International Journal of Computer Vision · doi:10.1007/bf00127169 · Cited by 1,369
Cited by
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
Image Sharpening by Flows Based on Triple Well Potentials
Gilboa, G., Sochen, N., & Zeevi, Y. Y. (2004) · Journal of Mathematical Imaging and Vision · doi:10.1023/b:jmiv.0000011320.81911.38 · Cited by 616
Monocular model-based 3D tracking of rigid objects: a survey
Lepetit, V., & Fua, P. (2005) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000001 · Cited by 484
Registration of 3-d intraoperative MR images of the brain using a finite-element biomechanical model
Ferrant, M., Nabavi, A., Macq, B., Jolesz, F. A., Kikinis, R., & Warfield, S. K. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.974933 · Cited by 339
A novel method for automatic face segmentation, facial feature extraction and tracking
Sobottka, K., & Pitas, I. (1998) · Signal Processing: Image Communication · doi:10.1016/s0923-5965(97)00042-8 · Cited by 306
Metadata
- Authors
- Cohen, L. D., & Cohen, I.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1993
- Volume
- 15
- Issue
- 11
- Pages
- 1131-1147
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2162837059
- Cited by
- 1,378
- References
- 31
- FWCI
- 27.98
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Finite-element methods for active contour models and balloons for 2-D and 3-D images. https://app.cochat.ai/papers/10.1109/34.244675
- The paper
- Cohen, L. D., & Cohen, I. (1993). Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.244675
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.
