An active contour model based on local fitted images for image segmentation
Open accessAbstract
Active contour models are popular and widely used for a variety of image segmentation applications with promising accuracy, but they may suffer from limited segmentation performances due to the presence of intensity inhomogeneity. To overcome this drawback, a novel region-based active contour model based on two different local fitted images is proposed by constructing a novel local hybrid image fitting energy, which is minimized in a variational level set framework to guide the evolving of contour curves toward the desired boundaries. The proposed model is evaluated and compared with several typical active contour models to segment synthetic and real images with different intensity characteristics. Experimental results demonstrate that the proposed model outperforms these models in terms of accuracy in image segmentation.
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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 · see citing works
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Research works printed but not linked: 20
- [2]Chan T Vese L Active contour without edges IEEE Trans Image Process 10 2 2001 266 277 18249617
- [3]Chen F Yu H Hu R Shape sparse representation for joint object classification and segmentation IEEE Trans Image Process 22 3 2013 992 1004 23144032
- [4]Cremers D Rousson M Deriche R A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape Int J Comput Vis 72 2 2007 195 215
- [6]Estellers V Zosso D Lai R Osher S Thiran J Bresson X Efficient algorithm for level set method preserving distance function IEEE Trans Image Process 21 12 2012 4722 4734 22692909
- [9]Jayadevappa D Kumar S Murty D Medical image segmentation algorithms using deformable models: a review IETE Tech Rev 28 3 2011 248 255
- [13]Lankton S Tannenbaum A Localizing region-based active contours IEEE Trans Image Process 17 11 2008 2029 2039 18854247 PMC2796112
- [14]Li C Kao C Gore J Ding Z Implicit active contours driven by local binary fitting energy IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2007 339 345
- [15]Li C Kao C Gore J Ding Z Minimization of region-scalable fitting energy for image segmentation IEEE Trans Image Process 17 10 2008 1940 1949 18784040 PMC2720140
- [16]Li C Wang X Eberl S Fulham M Feng D Robust model for segmenting images with/without intensity inhomogeneities IEEE Trans Image Process 22 8 2013 3296 3309 23693130
- [17]Li C Wang X Eberl S Fulham M Yin Y Chen J Feng D A likelihood and local constraint level set model for liver tumor segmentation from ct volumes IEEE Trans Biomed Eng 60 10 2013 2967 2977 23771304
- [18]Li C Xu C Gui C Fox M Distance regularized level set evolution and its application to image segmentation IEEE Trans Image Process 19 12 2010 3243 3254 20801742
- [23]Mylona E Savelonas M Maroulis D Automated adjustment of region-based active contour parameters using local image geometry IEEE Trans Cybern 44 12 2014 2757 2770 24771604
- [24]Pablo A Michael M Charless F Jitendra M Contour detection and hierarchical image segmentation IEEE Trans Pattern Anal Mach Intell 33 5 2011 898 916 20733228
- [26]Ranjith U Caroline P Martial H A measure for objective evaluation of image segmentation algorithms IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2005 34 34
- [29]Tomoshige S Oost E Shimizu A Watanabe H Nawano S A conditional statistical shape model with integrated error estimation of the conditions: application to liver segmentation in non-contrast ct images Med Image Anal 18 1 2014 130 143 24184436
- [30]Wang B Gao X Tao D Li X A nonlinear adaptive level set for image segmentation IEEE Trans Cybern 44 3 2014 418 428 23797311
- [35]Wang L Shi F Li G Gao Y Lin W Gilmore J Shen D Segmentation of neonatal brain mr images using patch-driven level sets NeuroImage 84 1 2014a 141 158 23968736 PMC3849114
- [37]Wu H Appia V Yezzi A Numerical conditioning problems and solutions for nonparametric i.i.d. statistical active contours IEEE Trans Pattern Anal Mach Intell 35 6 2013a 1298 1311 23599049
- [41]Yezzi J Tsai A Willsky A Medical image segmentation via coupled curve evolution equations with global constraints IEEE Workshop on Mathematical Methods in Biomedical Image Analysis 2000 359 369
- [46]Zhao Y Rada L Chen K Harding S Zheng Y Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images IEEE Trans Image Process 34 9 2015 1797 1807 25769147
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- Authors
- Wang, L., Chang, Y., Wang, H., Wu, Z., Pu, J., & Yang, X.
- Affiliations
- University of Pittsburgh (US)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Pu, Jiantao
- Yang, Xiaodong
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 418-419
- Pages
- 61-73
- ISSN
- 0020-0255 (ISSN-L)
- PMC
- PMC5754033
- OpenAlex
- W2741033153
- Cited by
- 127
- References
- 1
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 8 · 2023: 9 · 2022: 9 · 2021: 21 · 2020: 28 · 2019: 26 · 2018: 20
- FWCI
- 8.40
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Information Sciences (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01HL096613)
- National Cancer Institute (R21CA197493)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- snakes, image segmentation, intensity inhomogeneity, region-based active contour, variational level set, contour evolution
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