Inference with Collaborative Model for Interactive Tumor Segmentation in Medical Image Sequences

IEEE Transactions on Cybernetics

Volumn 46, Issue 12, 2016, Pages 2796-2809

  Lin, Liang ^a,b   Yang, Wei ^a   Li, Chenglong ^c   Tang, Jin ^c   Cao, Xiaochun ^d  

^a SUN YAT SEN UNIVERSITY   (China)

^b SYSU CMU SHUNDE INTERNATIONAL JOINT RESEARCH INSTITUTE   (China)

^c ANHUI UNIVERSITY   (China)

^d University of Chinese Academy of Sciences   (China)

Author keywords

Collaborative model; medical image analysis; spatio Temporal inference; tumor segmentation

Indexed keywords

COMPUTERIZED TOMOGRAPHY; CONSTRAINED OPTIMIZATION; DIAGNOSIS; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE SEGMENTATION; INFERENCE ENGINES; LAGRANGE MULTIPLIERS; MAGNETIC LEVITATION VEHICLES; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; OPTIMIZATION; TUMORS;

AUGMENTED LAGRANGIAN METHODS; COLLABORATIVE MODEL; COMPUTATIONAL FRAMEWORK; INFERENCE ALGORITHM; INTERACTIVE SEGMENTATION; LUCAS-KANADE ALGORITHMS; SPATIO-TEMPORAL INFERENCE; TUMOR SEGMENTATION;

MEDICAL IMAGE PROCESSING;

EID: 85007057244     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCYB.2015.2489719     Document Type: Article

References (49)

1. B. N. Li, C. K. Chui, S. Chang, and S. H. Ong, "A new unified level set method for semi-Automatic liver tumor segmentation on contrast-enhanced CT images, " Expert Syst. Appl., vol. 39, no. 10, pp. 9661-9668, 2012.
2. A. Jemal et al., "Global cancer statistics, " CA Cancer J. Clin., vol. 61, no. 2, pp. 69-90, 2011.
3. D. Smeets et al., "Semi-Automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification, " Med. Image Anal., vol. 14, no. 1, pp. 13-20, 2010.
4. Y. Häme and M. Pollari, "Semi-Automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation, " Med. Image Anal., vol. 16, no. 1, pp. 140-149, 2012.
5. T. Kubota, "Efficient automated detection and segmentation of medium and large liver tumors: CAD approach, " in Proc. MICCAI Workshop 3-D Segmentat. Clin. Grand Challenge II, 2008.
6. R. Lu, P. Marziliano, and C. H. Thng, "Liver tumor volume estimation by semi-Automatic segmentation method, " in Proc. IEEE Int. Conf. Eng. Med. Biol. Soc., Shanghai, China, 2006, pp. 3296-3299.
7. D. Wong et al., "A semi-Automated method for liver tumor segmentation based on 2D region growing with knowledge-based constraints, " Midas J., 2008.
8. K. Haris, S. N. Efstratiadis, N. Maglaveras, and A. K. Katsaggelos, "Hybrid image segmentation using watersheds and fast region merging, " IEEE Trans. Image Process., vol. 7, no. 12, pp. 1684-1699, Dec. 1998.
9. K. Wang, L. Lin, J. Lu, C. Li, and K. Shi, "PISA: Pixelwise image saliency by aggregating complementary appearance contrast measures with edge-preserving coherence, " IEEE Trans. Image Process., vol. 24, no. 10, pp. 3019-3033, Oct. 2015.
10. L. Lin, R. Zhang, and X. Duan, "Adaptive scene category discovery with generative learning and compositional sampling, " IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 2, pp. 251-260, Feb. 2015.
11. C. Rother, V. Kolmogorov, and A. Blake, "GrabCut: Interactive foreground extraction using iterated graph cuts, " ACM Trans. Graph., vol. 23, no. 3, pp. 309-314, 2004.
12. V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours, " Int. J. Comput. Vis., vol. 22, no. 1, pp. 61-79, 1997.
13. M. V. Afonso, J. M. Bioucas-Dias, and M. A. T. Figueiredo, "An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems, " IEEE Trans. Image Process., vol. 20, no. 3, pp. 681-695, Mar. 2011.
14. T. Goldstein and S. Osher, "The split Bregman method for L1-regularized problems, " SIAM J. Imag. Sci., vol. 2, no. 2, pp. 323-343, 2009.
15. X. Bai and G. Sapiro, "Geodesic matting: A framework for fast interactive image and video segmentation and matting, " Int. J. Comput. Vis., vol. 82, no. 2, pp. 113-132, 2009.
16. J. Shotton, M. Johnson, and R. Cipolla, "Semantic texton forests for image categorization and segmentation, " in Proc. IEEE Comput. Vis. Pattern Recognit., Anchorage, AK, USA, 2008, pp. 1-8.
17. T. F. Chan and L. A. Vese, "Active contour and segmentation models using geometric PDEs for medical imaging, " in Geometric Methods in Bio-Medical Image Processing. Berlin, Germany: Springer, 2002, pp. 63-75.
18. Y. Zhang, B. J. Matuszewski, L.-K. Shark, and C. J. Moore, "Medical image segmentation using new hybrid level-set method, " in Proc. IEEE Int. Conf. BioMed. Vis., London, U.K., 2008, pp. 71-76.
19. Y. Hame, "Liver tumor segmentation using implicit surface evolution, " MIDAS J., pp. 1-10, Jul. 2008.
20. J. H. Moltz, L. Bornemann, V. Dicken, and H.-O. Peitgen, "Segmentation of liver metastases in CT scans by adaptive thresholding and morphological processing, " in Proc. MICCAI Workshop 3-D Segmentat. Clin. Grand Challenge II, vol. 41, 2008, p. 195.
21. J. Stawiaski, E. Decenciere, and F. Bidault, "Interactive liver tumor segmentation using graph-cuts and watershed, " in Proc. MICCAI Workshop 3-D Segmentat. Clin. Grand Challenge II, 2008.
22. L. Zhao, W. Wu, and J. J. Corso, "Semi-Automatic brain tumor segmentation by constrained MRFs using structural trajectories, " in Proc. Med. Image Comput. Comput. Assist. Interv., Berlin, Germany, 2013, pp. 567-575.
23. W. Wu, A. Y. C. Chen, L. Zhao, and J. J. Corso, "Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features, " Int. J. Comput. Assist. Radiol. Surg., vol. 9, no. 2, pp. 241-253, 2013.
24. M. Cvancarova, F. Albregtsen, K. Brabrand, and E. Samset, "Segmentation of ultrasound images of liver tumors applying snake algorithms and GVF, " Int. Congr. Ser., vol. 1281, pp. 218-223, May 2005.
25. F. Meng, H. Li, G. Liu, and K. N. Ngan, "Image cosegmentation by incorporating color reward strategy and active contour model, " IEEE Trans. Cybern., vol. 43, no. 2, pp. 725-737, Apr. 2013.
26. S. Balla-Arabe, X. Gao, and B. Wang, "A fast and robust level set method for image segmentation using fuzzy clustering and lattice Boltzmann method, " IEEE Trans. Cybern., vol. 43, no. 3, pp. 910-920, Jun. 2013.
27. K. Zhang, Q. Liu, H. Song, and X. Li, "A variational approach to simultaneous image segmentation and bias correction, " IEEE Trans. Cybern., vol. 45, no. 8, pp. 1426-1437, Aug. 2015.
28. J. J. Corso et al., "Efficient multilevel brain tumor segmentation with integrated Bayesian model classification, " IEEE Trans. Med. Imag., vol. 27, no. 5, pp. 629-640, May 2008.
29. L. Lin, X. Wang, W. Yang, and J.-H. Lai, "Discriminatively trained andor graph models for object shape detection, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 5, pp. 959-972, May 2015.
30. J. Shen, X. Yang, X. Li, and Y. Jia, "Intrinsic image decomposition using optimization and user scribbles, " IEEE Trans. Cybern., vol. 43, no. 2, pp. 425-436, Apr. 2013.
31. M. Freiman et al., "An iterative Bayesian approach for nearly automatic liver segmentation: Algorithm and validation, " Int. J. Comput. Assist. Radiol. Surg., vol. 3, no. 5, pp. 439-446, 2008.
32. M. Freiman, O. Cooper, D. Lischinski, and L. Joskowicz, "Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation, " Int. J. Comput. Assist. Radiol. Surg., vol. 6, no. 2, pp. 247-255, Mar. 2011.
33. S. C. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 9, pp. 884-900, Sep. 1996.
34. P. Dollar, Z. Tu, and S. Belongie, "Supervised learning of edges and object boundaries, " in Proc. IEEE Comput. Vis. Pattern Recognit., vol. 2. New York, NY, USA, 2006, pp. 1964-1971.
35. Y. Freund and R. E. Schapire, "A decision-Theoretic generalization of online learning and an application to boosting, " in Computational Learning Theory. Berlin, Germany: Springer, 1995, pp. 23-37.
36. D. G. Lowe, "Distinctive image features from scale-invariant keypoints, " Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
37. S. Osher and J. A. Sethian, "Fronts propagating with curvaturedependent speed: Algorithms based on Hamilton-Jacobi formulations, " J. Comput. Phys., vol. 79, no. 1, pp. 12-49, 1988.
38. Y. Wang, J. Yang, W. Yin, and Y. Zhang, "A new alternating minimization algorithm for total variation image reconstruction, " SIAM J. Imag. Sci., vol. 1, no. 3, pp. 248-272, 2008.
39. S. P. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
40. P. L. Combettes and J. Pesquet, "A Douglas-Rachford splitting approach to nonsmooth convex variational signal recovery, " IEEE J. Select. Topics Signal Process., vol. 1, no. 4, pp. 564-574, Dec. 2007.
41. A. Ganesh et al., "Fast algorithms for recovering a corrupted low-rank matrix, " in Proc. IEEE Int. Workshop Comput. Adv. Multi-Sensor Adap. Process. (CAMSAP), 2009, pp. 213-216.
42. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, "Distributed optimization and statistical learning via the alternating direction method of multipliers, " Found. Trends Mach. Learn., vol. 3, no. 1, pp. 1-122, 2011.
43. J. Eckstein and D. Bertsekas, "On the douglas-rachford splitting method and the proximal point algorithm for maximal monotone operators, " Math. Program., vol. 55, nos. 1-3, pp. 293-318, 1992.
44. X. Liu, L. Lin, S. Yan, H. Jin, and W. Jiang, "Adaptive object tracking by learning hybrid template on-line, " IEEE Trans. Circuits Syst. Video Technol., vol. 21, no. 11, pp. 1588-1599, Nov. 2011.
45. L. Lin, Y. Lu, C. Li, H. Cheng, and W. Zuo, "Detection-free multiobject tracking by reconfigurable inference with bundle representations, " IEEE Trans. Cybern., DOI: 10.1109/TCYB.2015.2478515, 2015.
46. B. D. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision, " in Proc. Int. Joint Conf. Artif. Intell., vol. 2. San Francisco, CA, USA, 1981, pp. 674-679.
47. C. Li, C. Xu, C. Gui, and M. D. Fox, "Distance regularized level set evolution and its application to image segmentation, " IEEE Trans. Image Process., vol. 19, no. 12, pp. 3243-3254, Dec. 2010.
48. X. Bai, J. Wang, D. Simons, and G. Sapiro, "Video SnapCut: Robust video object cutout using localized classifiers, " ACM Trans. Graph., vol. 28, no. 3, Aug. 2009, Art. ID 70.
49. R. Zhang, L. Lin, R. Zhang, W. Zuo, and L. Zhang, "Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification, " IEEE Trans. Image Process., vol. 24, no. 12, pp. 4766-4779, Dec. 2015