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A novel region-based active contour model via local patch similarity measure for image segmentation

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Abstract

It is always difficult to accurately segment images with intensity inhomogeneity because most of the representative local-based models only take into account rough local information and do not consider the spatial relationship between the central pixel and its neighborhood. In fact, the pixels on an image are closely correlated to their local neighborhood. Therefore, the spatial relationship of neighboring pixels is a crucial feature that can play a vital role in image segmentation. In this paper, we propose a novel region-based active contour model via local patch similarity measure for image segmentation. In the model, we make full use of the spatial constraints on local region-based models for controlling the amplitude of spatial neighborhood to the center pixel in the image domain. Specifically, we first construct a local patch similarity measure as the spatial constraint, which balances the noise suppression and the image details reservation. Second, we construct the novel model by integrating the patch similarity measure into a region-based active contour model. Finally, we add a regularization information term to the objective function to ensure the smoothness and stability of the curve evolution. Experimental results show that the model is better than other classical local region-based models.

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References

  1. Alsmirat MA, Jararweh Y, Al-Ayyoub M et al (2017) Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU-GPU implementations. Multimed Tool Appl 76(3):3537–3555

    Article  Google Scholar 

  2. Bo C, Lu H, Wang D (2017) Spectral-spatial K-Nearest neighbor approach for hyperspectral image classification. Multimed Tool Appl, https://doi.org/10.1007/s11042-017-4403-9

  3. Cambria E, Schuller B, Liu B et al (2013) Statistical approaches to concept-level sentiment analysis. IEEE Intell Syst 28(3):6–9

    Article  Google Scholar 

  4. Chambolle A, Pock T (2011) A first-order primal-dual algorithm for convex problems with applications to imaging. J Math Imaging Vision 40(1):120–145

    Article  MathSciNet  MATH  Google Scholar 

  5. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  MATH  Google Scholar 

  6. Chen J, Zeng H, Fan N (2015) Nonlinear distance function learning using neural network: an iterative framework. Multimed Tool Appl 74(3):671–688

    Article  Google Scholar 

  7. Chen Y, He F, Wu Y et al (2017) A local start search algorithm to compute exact Hausdorff distance for arbitrary point sets. Pattern Recogn 67:139–148

    Article  Google Scholar 

  8. Cui J, Liu Y, Xu Y et al (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst Hum 43(4):996–1002

    Article  Google Scholar 

  9. Fister I, Perc M, Ljubiċ K et al (2015) Particle swarm optimization for automatic creation of complex graphic characters. Chaos, Solitons Fractals 73:29–35

    Article  MathSciNet  Google Scholar 

  10. Goldenberg R, Kimmel R, Rivlin E et al (2001) Fast geodesic active contours. IEEE Trans Image Process 10(10):1467–1475

    Article  MathSciNet  Google Scholar 

  11. Goldstein T, Bresson X, Osher S (2010) Geometric applications of the split Bregman method: segmentation and surface reconstruction. J Sci Comput 45 (1-3):272–293

    Article  MathSciNet  MATH  Google Scholar 

  12. Gomes J, Faugeras O (2000) Reconciling distance functions and level sets. J Vis Commun Image Represent 11(2):209–223

    Article  Google Scholar 

  13. Gong M, Liang Y, Shi J et al (2013) Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process 22 (2):573–584

    Article  MathSciNet  MATH  Google Scholar 

  14. Gu J, Jiao L, Yang S et al (2017) Fuzzy double C-Means clustering based on sparse Self-Representation. IEEE Trans Fuzzy Syst 119:113–125

    Google Scholar 

  15. Jiang J, Chen C, Ma J et al (2017) SRLSP: A face image super-resolution algorithm using smooth regression with local structure prior. IEEE Trans Multimed 19 (1):27–40

    Article  Google Scholar 

  16. Kim CG, Kim JG, Lee DH (2014) Optimizing image processing on multi-core CPUs with Intel parallel programming technologies. Multimed Tool Appl 68(2):237–251

    Article  Google Scholar 

  17. Krissian K, Westin CF (2005) Fast sub-voxel re-initialization of the distance map for level set methods. Pattern Recogn Lett 26(10):1532–1542

    Article  Google Scholar 

  18. Li C, Huang R, Ding Z et al (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 20(7):2007–2016

    Article  MathSciNet  MATH  Google Scholar 

  19. Li C, Kao CY, Gore JC et al (2007) Implicit active contours driven by local binary fitting energy. presented at the Computer Vision and Pattern Recognition

  20. Li C, Kao CY, Gore JC et al (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17(10):1940–1949

    Article  MathSciNet  MATH  Google Scholar 

  21. Li C, Xu C, Gui C et al (2015) Level set evolution without re-initialization: a new variational formulation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2005, vol 1, pp 430–436

  22. Li K, He F, Chen X (2016) Real-time object tracking via compressive feature selection. Front Comp Sci 10(4):689–701

    Article  Google Scholar 

  23. Li K, He F, Yu H (2017) Robust Visual Tracking based on convolutional features with illumination and occlusion handing. J Comput Sci Technol. https://doi.org/10.1007/s11390-017-1764-5

  24. Li K, He F, Yu H et al (2017) A correlative classiers approach based on particle filter and sample set for tracking occluded target. Appl Math-A J Chinese Universities 32(3):294–312

    Article  MathSciNet  MATH  Google Scholar 

  25. Li K, He F, Yu H et al (2018) A parallel and robust object tracking approach synthesizing adaptive bayesian learning and improved incremental subspace learning. Frontiers Comput Sci. https://doi.org/10.1007/s11704-018-6442-4

  26. Liu L, Cheng L, Liu Y et al (2016) Recognizing complex activities by a probabilistic interval-based model. AAAI 30:1266–1272

    Google Scholar 

  27. Liu S, Peng Y (2012) A local region-based Chan Vese model for image segmentation. Pattern Recogn 45(7):2769–2779

    Article  MATH  Google Scholar 

  28. Liu Y, Cui J, Zhao H et al (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In: 2012 21st International Conference on Pattern Recognition (ICPR). IEEE, pp 898–901

  29. Liu Y, Nie L, Han L et al (2015) Action2activity: recognizing complex activities from sensor data. In: 2015 International Joint Conference on Artificial Intelligence(IJCAI), pp 1617–1623

  30. Liu Y, Nie L, Liu L et al (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  31. Liu Y, Zhang L, Nie L et al (2016) Fortune teller: predicting your career path. AAAI 2016:201–207

    Google Scholar 

  32. Liu Y, Zhang X, Cui J et al (2010) Visual analysis of child-adult interactive behaviors in video sequences. In: 16th International Conference on Virtual Systems and Multimedia (VSMM). IEEE, pp 26–33

  33. Liu Y, Zhang X, Cui J et al (2010) Visual analysis of child-adult interactive behaviors in video sequences. In: 16th International Conference on Virtual Systems and Multimedia (VSMM). IEEE, pp 26–33

  34. Liu Y, Zheng Y, Liang Y et al (2016) Urban water quality prediction based on multi-task multi-view learning. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence

  35. Lu T, Pan L, Jiangs J et al (2017) DLML: deep Linear mappings learning for face super-resolution with nonlocal-patch. In: 2017 IEEE International Conference on Multimedia and Expo (ICME). IEEE, pp 1362–1367

  36. Lu T, Xiong Z, Zhang Y et al (2017) Robust face super-resolution via locality-constrained low-rank representation. IEEE Access 5:13103–13117

    Article  Google Scholar 

  37. Luo Y, Zhou L, Wang S et al Video Satellite Imagery Super Resolution via Convolutional Neural Networks. IEEE Geosci Remote Sens Lett

  38. Lv X, He F, Cai W et al (2017) A string-wise CRDT algorithm for smart and large-scale collaborative editing systems. Adv Eng Inform 33(3):397–409

    Article  Google Scholar 

  39. Lv X, He F, Cai W et al (2018) Supporting selective undo of string-wise operations for collaborative editing systems. Futur Gener Comput Syst 82(5):41–62

    Article  Google Scholar 

  40. Ma J, Zhao J, Guo H et al (2017) Locality preserving matching. In: 2017 International Joint Conference on Artificial Intelligence(IJCAI), pp 4492–4498

  41. Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42(5):577–685

    Article  MathSciNet  MATH  Google Scholar 

  42. Ni B, He F, Pan Y et al (2016) Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computer-aided therapy. Appl Math-A J Chinese Universities 31(1):37–52

    Article  MathSciNet  MATH  Google Scholar 

  43. Niu S, Chen Q, de Sisternes L et al (2017) Robust noise region-based active contour model via local similarity factor for image segmentation. Pattern Recogn 61:104–119

    Article  Google Scholar 

  44. Paragios N, Deriche R (2000) Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans Pattern Anal Mach Intell 22 (3):266–280

    Article  Google Scholar 

  45. Shen T, Li H, Huang X (2011) Active volume models for medical image segmentation. IEEE Trans Med Imaging 30(3):774–791

    Article  Google Scholar 

  46. Sun J, He F, Chen Y et al (2016) A multiple template approach for robust tracking of fast motion target. Appl Math-A J Chinese Universities 31(2):177–197

    Article  MathSciNet  MATH  Google Scholar 

  47. Wang B, Gao X, Tao D et al (2014) A nonlinear adaptive level set for image segmentation. IEEE Trans Cybern 44(3):418–428

    Article  Google Scholar 

  48. Wang J, Kong J, Lu Y et al (2008) A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints. Comput Med Imaging Graph 32(8):685–698

    Article  Google Scholar 

  49. Wang L, He L, Mishra A et al (2009) Active contours driven by local Gaussian distribution fitting energy. Signal Process 89(12):2435–2447

    Article  MATH  Google Scholar 

  50. Wang XF, Min H, Zou L et al (2015) A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement. Pattern Recogn 48(1):189–204

    Article  Google Scholar 

  51. Wang Y, Loe KF, Wu JK (2006) A dynamic conditional random field model for foreground and shadow segmentation. IEEE Trans Pattern Anal Mach Intell 28 (2):279–289

    Article  Google Scholar 

  52. Wu Y, He F, Zhang D et al (2015) Service-oriented feature-based data exchange for cloud-based design and manufacturing. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2015.2501981

  53. Xia Z, Wang X, Sun X et al (2016) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimed Tool Appl 75(4):1947–1962

    Article  Google Scholar 

  54. Xiao Z, Adel M, Bourennane S (2013) Bayesian method with spatial constraint for retinal vessel segmentation. Computational and mathematical methods in medicine

  55. Yan X, He F, Hou N et al (2017) An Efficient Particle Swarm Optimization for Large-Scale Hardware/Software Co-Design System. Int J Coop Infor Syst. https://doi.org/10.1142/S0218843017410015

  56. Yan XH, He FZ, Chen YL (2017) A novel hardware/software partitioning method based on position disturbed particle swarm optimization with invasive weed optimization. J Comput Sci Technol 32(2):340–355

    Article  MathSciNet  Google Scholar 

  57. Yang X, Gao X, Li J et al (2014) A shape-initialized and intensity-adaptive level set method for auroral oval segmentation. Inf Sci 277:794–807

    Article  Google Scholar 

  58. Yang X, Gao X, Tao D et al (2016) Shape-constrained sparse and low-rank decomposition for auroral substorm detection. IEEE Trans Netw Learn Syst 27(1):32–46

    Article  MathSciNet  Google Scholar 

  59. Yezzi A, Tsai A, Willsky A (2002) A fully global approach to image segmentation via coupled curve evolution equations. J Vis Commun Image Represent 13(1):195–216

    Article  Google Scholar 

  60. Yu HP, He F, Pan Y et al (2016) An efficient similarity-based level set model for medical image segmentation. J Advan Mech Des Syst Manuf 10(8):JAMDSM0100–JAMDSM0100

    Article  Google Scholar 

  61. Zhang D, He F, Han S et al (2017) An efficient approach to directly compute the exact Hausdorff distance for 3D point sets. Integr Comput-Aided Eng 24(3):261–277

    Article  Google Scholar 

  62. Zhang DJ, He FZ, Han SH et al (2016) Quantitative optimization of interoperability during feature-based data exchange. Integr Comput-Aided Eng 23(1):31–50

    Article  Google Scholar 

  63. Zhang K, Zhang L, Lam KM et al (2016) A level set approach to image segmentation with intensity inhomogeneity. IEEE Trans Cybern 46(2):546–557

    Article  Google Scholar 

  64. Zhang K, Zhang L, Song H et al (2010) Active contours with selective local or global segmentation: a new formulation and level set method. Image Vis Comput 28(4):668–676

    Article  Google Scholar 

  65. Zhang L, Zhu X, Zhang L et al (2016) Multidomain subspace classification for hyperspectral images. IEEE Trans Geosci Remote Sens 54(10):6138–6150

    Article  Google Scholar 

  66. Zhao F, Jiao L, Liu H et al (2011) A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation. Signal Process 91(4):988–999

    Article  MATH  Google Scholar 

  67. Zhou H, Schaefer G, Liu T et al (2010) Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm. Multimed Tool Appl 49(3):447–462

    Article  Google Scholar 

  68. Zhou Y, He F, Hou N et al (2018) Parallel ant colony optimization on multi-core SIMD CPUs. Futur Gener Comput Syst 79:473–487

    Article  Google Scholar 

  69. Zhou Y, He F, Qiu Y (2016) Optimization of parallel iterated local search algorithms on graphics processing unit. J Supercomputing 72(6):2394–2416

    Article  Google Scholar 

  70. Zhou Y, He F, Qiu Y (2017) Dynamic strategy based parallel ant colony optimization on GPUs for TSPs. Sci China Infor Sci 60(6):068102

    Article  Google Scholar 

  71. Zhou YF, Xu H, Pan X et al (2016) Parsing main structures of indoor scenes from single RGB-D image. J Advan Mech Des Syst Manuf 10(8):JAMDSM0102–JAMDSM0102

    Article  Google Scholar 

  72. Zhu W, Huang W, Lin Z et al (2016) Data and feature mixed ensemble based extreme learning machine for medical object detection and segmentation. Multimed Tool Appl 75(5):2815–2837

    Article  Google Scholar 

  73. Zou Q, Zeng J, Cao L et al (2016) A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing 173:346–354

    Article  Google Scholar 

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Acknowledgments

We would like to thank all the anonymous reviewers for their valuable comments. We are also grateful to Professor Zhang Kaihua and Professor Li Chunming to provide source code for comparison with our model. This work was supported by the National Natural Science Foundation of China (Grant Nos. 61472289 and Nos. 61502356) and the National Key Research and Development Project (Grant No. 2016YFC0106305).

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  1. School of Computer Science, Wuhan University, Wuhan, China

    Haiping Yu & Yiteng Pan

  2. State Key Lab of Software Engineering, School of Computer Science, Wuhan University, Wuhan, China

    Fazhi He

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  1. Haiping Yu
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Correspondence to Fazhi He.

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Yu, H., He, F. & Pan, Y. A novel region-based active contour model via local patch similarity measure for image segmentation. Multimed Tools Appl 77, 24097–24119 (2018). https://doi.org/10.1007/s11042-018-5697-y

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