A Two-Way Active Contour Model for Incomplete Contour Segmentation
Pages 6437 - 6458
Abstract
A two-way segmentation model is proposed in this article. The model is used to solve the problem that the objective contour can not be completely extracted from image due to occlusion between objects within similar image groups or image sequences. The proposed model first decomposes incomplete contours into sub-segments using local features identified by seed points set along each path. Then, locate the occluded part of the target object and reconstruct the target. Finally, a new vector field is generated based on the reconstructed object from the proposed model, followed by iterative evolution. The experimental results show that the proposed algorithm can better handle the problem of occlusion or misleading features of targets in composite images and medical images. Not only does it facilitate subsequent measurement and analysis, but it also preserves the original shape of the object during the segmentation process without prior information. It is worth noting that the accuracy of the proposed model is robust to our initialization strategy.
References
[1]
Caselles V, Catte F, and Coll T A geometric model for active contours in image processing Numer. Math. 1993 66 1-31
[2]
Caselles V, Kimmel R, and Sapiro D Geodesic active contours Int. Conf. Comput. Vis. 1995 22 1 694-699
[3]
Everingham M, Gool L, and Williams C The pascal visual object classes (voc) challenge Int. J. Comput Vis. 2010 88 2 303-338
[4]
Fang J, Liu H, and Liu J Fuzzy region-based active contour driven by global and local fitting energy for image segmentation Appl. Soft Comput. 2021 100
[5]
Fang J, Liu H, and Zhang L Region-edge-based active contours driven by hybrid and local fuzzy region-based energy for image segmentation Inform. Sci. 2021 546 397-419
[6]
Fang LL, Pan XH, Yao YB, Zhang LR, and Guo DM A hybrid active contour model for ultrasound image segmentation Soft Comput. Fus. Found. Methodol. Appl. 2020 24 24 18611-18625
[7]
Fan X, Wu W, and Zhang L Shading-aware shadow detection and removal from a single image Vis. Comput. 2020 36 2 2175-2188
[8]
Fu HN, Wang BZ, and Liu HZ Fast background estimation on long video sequence Electron. Lett. 2019 55 16 888-890
[9]
Gargi S and Rajeev S An efficient modification of generalized gradient vector flow using directional contrast for salient object detection and intelligent scene analysis Multimed. Tools Appl. 2020 79 19/20 13599-13619
[10]
Han B and Wu YQ A hybrid active contour model driven by novel global and local fitting energies for image segmentation Multimed. Tools Appl. 2018 77 21 29193-29208
[11]
Huang Z and Wang CY Semantic image segmentation by scale-adaptive networks IEEE Trans. Image Process. 2020 29 2066-2077
[12]
Karimi D and Dou H Medical image segmentation using transformer networks IEEE Access 2022 10 1 29322-29332
[13]
Kass M, Witkin AP, and Terzopoulos D Snakes: Active contour models Int. J. Comput. Vis. 1988 1 4 321-331
[14]
Kim B, Kim BC, and Lee HJ Determination of the length of target recognition sequence in SGRNA required for CRISPR interference Microbiol. Biotechnol. Lett. 2021 49 4 534-542
[15]
Li B and Acton S Active contour external force using vector field convolution for image segmentation IEEE Trans. Image Process. 2007 16 8 2096-2106
[16]
Li Q, Deng T, and Xie W Active contours driven by divergence of gradient vector flow Signal Process. 2016 120 185-199
[17]
Lin AL, Chen BZ, and Xu JY Ds-transunet: dual swin transformer u-net for medical image segmentation IEEE Trans. Instrum. Meas. 2022 71 400561-115
[18]
Liu GQ and Deng M Parametric active contour based on sparse decomposition for multi-objects extraction Signal Process. 2018 148 314-321
[19]
Liu HX and Fang JX Localised edge-region-based active contour for medical image segmentation IET Image Process. 2021 15 7 1567-1582
[20]
X. Liu, D.L. Langer, M.D. Haider, Unsupervised segmentation of the prostate using MR images based on level set with a shape prior, in 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA, pp. 3613–3616 (2009)
[21]
Liu Z, Zhang JN, Jog V, and Loh P Robustifying deep networks for medical image segmentation J. Dig. Imaging Off. J. Soc. Comput. Appl. Radiol. 2021 34 5 1279-1293
[22]
Liu GQ, Zhou ZH, and Zhong H Gradient descent with adaptive momentum for active contour models IET Comput. Vis. 2014 8 4 287-298
[23]
Lu L Design of visual communication based on deep learning approaches, soft computing: a fusion of foundations Methodol. Appl. 2020 24 11 7861-7872
[24]
Lu Y, Xie F, Wu Y, and Meng R No reference uneven illumination assessment for dermoscopy images IEEE Signal Process. Lett. 2015 22 5 534-538
[25]
P. Lv, Q.J. Zhao, D. GuB, Segmenting similar shapes via weighted group similarity active contours, in IEEE International Conference on Image Processing, pp. 4032–4036 (2015)
[26]
Malladi R, Sethian J, and Vemuri B Shape modeling with front propagation: a level set approach IEEE Trans. Pattern Anal. Mach. Intell. 1995 17 2 158-175
[27]
Mallat S and Zhang Z Matching pursuits with time-frequency dictionaries IEEE Trans. Signal Process. 1993 41 12 3397-3415
[28]
Niu Y, Qin L, and Wang XC Structured graph regularized shape prior and cross-entropy induced active contour model for myocardium segmentation in cta images Neurocomputing 2019 357 10 215-230
[29]
Peng YJ, Wang N, Wang YH, and Wang ML Segmentation of dermoscopy image using adversarial networks Multimed. Tools Appl. 2019 78 8 10965-10981
[30]
Pham VT, Tran TT, Shyu KK, Lin C, Wang PC, and Lo MT Shape collaborative representation with fuzzy energy based active contour model Eng. Appl. Artif. Intell. Int. J. Intell. Real-Time Autom. 2016 56 60-74
[31]
Seyed MH and Erfan G Atex: a benchmark for image classification of water in different waterbodies using deep learning approaches J. Water Resour. Plan. Manag. 2022 148 11 1-10
[32]
Sharma A, Alsadoon A, and Prasad PW A novel augmented reality visualization in jaw surgery: enhanced ICP based modified rotation invariant and modified correntropy Multimed. Tools Appl. 2021 80 16 23923-23947
[33]
Shen LZ, Tao HF, and Ni YZ Improved yolov3 model with feature map cropping for multi-scale road object detection Meas. Sci. Technol. 2023 34 4
[34]
Song XN, Wu NN, and Song S Bipartite synchronization for cooperative competitive neural networks with reaction-diffusion terms via dual eventtriggered mechanism Neurocomputing 2023 550 14
[35]
Song H, Zheng Y, and Zhang K Efficient algorithm for piecewise-smooth model with approximately explicit solutions Electron. Lett. 2016 53 4 233-235
[36]
Tan J, Zhang TP, and Zhao LC A robust image representation method against illumination and occlusion variations Image Vis. Comput. 2021 112 4 104212-111
[37]
Tao HF, Cheng L, and Qiu J Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic Meas. Sci. Technol. 2022 33 11 115005-116
[38]
Tran TT, Pham VT, and Shyu KK Image segmentation using fuzzy energy based active contur with shape prior J. Vis. Commun. Image Represent. 2014 25 7 1732-1745
[39]
Tropp J and Gilbert A Signal recovery from random measurements via orthogonal matching pursuit IEEE Trans. Inf. Theory 2007 53 12 4655-4666
[40]
Weng GR, Dong B, and Lei Y A level set method based on additive bias correction for image segmentation Expert Syst. Appl. 2021 185 1–2
[41]
S. Wesolkowski, R.D. Dony, M.E. Jernigan, Global color image segmentation strategies: Eculidean distance vs. vector angle. Neural Networks for Signal Processing IX, IEEE Signal Processing Society Workshop, pp. 419–428 (1999)
[42]
Wu YC and Wang Y A research on underwater target recognition neural network for small samples J. Northwest. Polytech. Univ. 2022 40 40-46
[43]
Xia WQ, Duan QW, and Deng JQ A target anti-occlusion method based on image processing and trajectory prediction in electro-optical tracking system IET Radar Sonar Navig. 2022 16 6 970-985
[44]
Xie XD and Lam KM An efficient illumination normalization method for face recognition Pattern Recognit. Lett. 2006 27 6 609-617
[45]
Xie SL, Zeng DL, and Zhou ZH Arranging and interpolating sparse unorganized feature points with geodesic circular arc IEEE Trans. Image Process. 2009 18 3 582-595
[46]
C. Xu, J. Prince, Gradient vector flow: a new external force for snakes. IEEE Conference on Computer Vision and Pattern Recognition, pp. 66–71 (1997)
[47]
Yang C, Wu WG, Su YQ, and Wu YW Image segmentation via active contour model driven by interregion contrast J. Electron. Imaging 2016 25 5 53020-113
[48]
Zhang GM, Jiang S, and Chu J Affine registration based on chord height point and genetic algorithm Acta Autom. Sin. 2014 39 9 1447-1457
[49]
Zhang K, Zhang L, and Lam K A level set approach to image segmentation with intensity inhomogeneity IEEE Trans. Cybern. 2016 46 2 546-557
[50]
Zhi XH and Shen HB Saliency driven region-edge-based top down level set evolution reveals the asynchronous focus in image segmentation Pattern Recognit. 2018 80 241-255
[51]
X.W. Zhou, X.J. Huang, J.S. Duncan, Active contours with group similarity, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 2969-2976 (2013)
Index Terms
- A Two-Way Active Contour Model for Incomplete Contour Segmentation
Index terms have been assigned to the content through auto-classification.
Recommendations
A novel multiphase active contour model for inhomogeneous image segmentation
The problem of image segmentation has been investigated with a focus on inhomogeneous multiphase image segmentation. Intensity inhomogeneity is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) and natural images ...
Active Contour Based Lung Field Segmentation
IHMSC '09: Proceedings of the 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 01Conventional methods that perform lung segmentation in CT rely on a large contrast in Hounsfield units between the lung and surrounding tissues. But the segmentation of lungs affected by high density pathologies that are connected to the lung border and ...
Brain tumor segmentation with radius contraction and expansion based initial contour detection for active contour model
AbstractThis paper proposes a novel brain tumor segmentation algorithm that uses Active Contour Model and Fuzzy-C-Means optimization. In Active Contour model, the initial Contour selection is a challenging task for MRI brain tumor segmentation because the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Birkhauser Boston Inc.
United States
Publication History
Published: 29 June 2024
Accepted: 29 May 2024
Revision received: 27 May 2024
Received: 03 December 2023
Author Tags
Qualifiers
- Research-article
Funding Sources
- National Key Research and Development Program of China
- Guangdong Basic and Applied Basic Research Foundation
- Guangdong Provincial Key Laboratory of Human Digital Twin
- Guangzhou City Science and Technology Research Projects
- Jiangmen Science and Technology Research Projects
- Shaoguan Science and Technology Research Project
- Foshan Science and Technology Research Project
- Zhuhai Science and Technology Research Project
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 28 Sep 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in