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Active contours without edges
Abstract
We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We give a numerical algorithm using finite differences. Finally, we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.
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Cites
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
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Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059968 · Cited by 3,796
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
Vese, L. A., & Chan, T. F. (2002) · International Journal of Computer Vision · doi:10.1023/a:1020874308076 · Cited by 2,384
Self-citationDistance 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
Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models
Chan, T. F., Esedoglu, S., & Nikolova, M. (2006) · SIAM Journal on Applied Mathematics · doi:10.1137/040615286 · Cited by 1,049
Self-citationA curated mammography data set for use in computer-aided detection and diagnosis research
Lee, R. S., Gimenez, F., Hoogi, A., Miyake, K. K., Gorovoy, M., & Rubin, D. L. (2017) · Scientific Data · doi:10.1038/sdata.2017.177 · Cited by 859
Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
Caicedo, J. C., Goodman, A., Karhohs, K. W., Cimini, B. A., Ackerman, J., Haghighi, M., Heng, C., Becker, T., Doan, M., McQuin, C., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0612-7 · Cited by 809
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
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
Active contours driven by local Gaussian distribution fitting energy
Wang, L., He, L., Mishra, A., & Li, C. (2009) · Signal Processing · doi:10.1016/j.sigpro.2009.03.014 · Cited by 466
Review of brain MRI image segmentation methods
Balafar, M. A., Ramli, A. R., Saripan, M. I., & Mashohor, S. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-010-9155-0 · Cited by 438
Zhao, Y., Rada, L., Chen, K., Harding, S. P., & Zheng, Y. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2409024 · Cited by 391
The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation
Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C. J., Ebert, D. S., & Delp, E. J. (2010) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2010.2051471 · Cited by 355
A deep learning approach for the analysis of masses in mammograms with minimal user intervention
Dhungel, N., Carneiro, G., & Bradley, A. P. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.01.009 · Cited by 336
Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications
Hamamci, A., Kucuk, N., Karaman, K., Engin, K., & Unal, G. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2181857 · Cited by 286
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
“They proposed a BUS image segmentation approach based on the active contour without edges (ACWE) model [66] which defined the stopping term using Mumford-Shah technique and was robust to segment images with weak boundaries.”
MethodInfluentialA study on continuous max-flow and min-cut approaches
Yuan, J., Bae, E., & Tai, X.-C. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539903 · Cited by 237
Faria, A. V., Zhang, J., Oishi, K., Li, X., Jiang, H., Akhter, K., Hermoye, L., Lee, S.-K., Hoon, A., & Stashinko, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.238 · Cited by 180
NuClick: A deep learning framework for interactive segmentation of microscopic images
Alemi Koohbanani, N., Jahanifar, M., Zamani Tajadin, N., & Rajpoot, N. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101771 · Cited by 147
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
An active contour model based on local fitted images for image segmentation
Wang, L., Chang, Y., Wang, H., Wu, Z., Pu, J., & Yang, X. (2017) · Information Sciences · doi:10.1016/j.ins.2017.06.042 · Cited by 127
CT radiomics to predict high‐risk intraductal papillary mucinous neoplasms of the pancreas
Chakraborty, J., Midya, A., Gazit, L., Attiyeh, M., Langdon‐Embry, L., Allen, P. J., Do, R. K. G., & Simpson, A. L. (2018) · Medical Physics · doi:10.1002/mp.13159 · Cited by 105
Yu, L., Guo, Y., Wang, Y., Yu, J., & Chen, P. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2628401 · Cited by 96
Unsupervised Variational Image Segmentation/Classification Using a Weibull Observation Model
Ben Ayed, I., Hennane, N., & Mitiche, A. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881961 · Cited by 70
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
Automatic detection of impact damage in carbon fiber composites using active thermography
Usamentiaga, R., Venegas, P., Guerediaga, J., Vega, L., & López, I. (2013) · Infrared Physics & Technology · doi:10.1016/j.infrared.2013.01.004 · Cited by 67
Pedestrian detection in automotive safety: Understanding state-of-the-art
Ragesh, N. K., & Rajesh, R. (2019) · IEEE Access · doi:10.1109/access.2019.2909992 · Cited by 57
Incorporating prior knowledge in medical image segmentation: a survey
Nosrati, M. S., & Hamarneh, G. (2016) · arXiv · doi:10.48550/arxiv.1607.01092 · Cited by 55
Puberty in the corpus callosum
Chavarria, M. C., Sánchez, F. J., Chou, Y.-Y., Thompson, P. M., & Luders, E. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.01.030 · Cited by 46
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
Local‐ and Global‐Statistics‐Based Active Contour Model for Image Segmentation
Wu, B., & Yang, Y. (2012) · Mathematical Problems in Engineering · doi:10.1155/2012/791958 · Cited by 30
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
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
Yang, S., Kim, H., Rezayof, O., Bonyun, J., John, J. V., Dokmeci, M. R., Khademhosseini, A., & Alambeigi, F. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3382200 · Cited by 7
Metadata
- Authors
- Chan, T. F., & Vese, L. A.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2001
- Volume
- 10
- Issue
- 2
- Pages
- 266-277
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2116040950
- Cited by
- 10,304
- References
- 1
- FWCI
- 77.94
- Type
- journal article
- Language
- en
- Open access
- closed
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