Fast and globally convex multiphase active contours for brain MRI segmentation

Computer Vision and Image Understanding

Volumn 125, Issue , 2014, Pages 237-250

  Moreno, Juan C ^a   Surya Prasath, V B ^b   Proença, Hugo ^a   Palaniappan, K ^b  

^a UNIVERSITY OF BEIRA INTERIOR   (Portugal)

^b University of Missouri   (United States)

Author keywords

Active contours; Brain MRI; Dual formulation; Globally convex; Image segmentation; Multiphase

Indexed keywords

MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING;

ACTIVE CONTOURS; BRAIN MRI; DUAL FORMULATIONS; GLOBALLY CONVEX; MULTIPHASE;

IMAGE SEGMENTATION;

EID: 84900883930     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2014.04.010     Document Type: Article

References (63)

1. Chan, T.F., Esedoglu, S., Nikolova, M., Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J. Appl. Math. 66 (2006), 1632–1648.
2. Chan, T.F., Vese, L.A., Active contours without edges. IEEE Trans. Image Process. 10 (2001), 266–277.
3. Mumford, D., Shah, J., Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42 (1989), 577–685.
4. Chan, T.F., Sandberg, B.Y., Vese, L.A., Active contours without edges for vector-valued images. J. Visual Commun. Image Represent. 11 (2000), 130–141.
5. Vese, L., Chan, T.F., A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis. 50 (2002), 271–293.
6. Keegan, M.S., Sandberg, B., Chan, T.F., A multiphase logic framework for multichannel image segmentation. Inverse Problems Imag. 6 (2012), 95–110.
7. Marcus, D.S., Harwell, J., Olsen, T., Hodge, M., Glasser, M.F., Prior, F., Jenkinson, M., Laumann, T., Curtiss, S.W., Essen, D.C.V., Informatics and data mining tools and strategies for the human connectome project. Frontiers Neuroinform. 5 (2011), 1–12.
8. Osher, S., Sethian, J.A., Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79 (1988), 12–49.
9. Drapaca, C.S., Cardenas, V., Studholme, C., Segmentation of tissue boundary evolution from brain MR image sequences using multi-phase level sets. Comput. Vis. Image Understand. 100 (2005), 312–329.
10. Nath, S.K., Palaniappan, K., Fast graph partitioning active contours for image segmentation using histograms. EURASIP J. Image Video Process., 2009, 9.
11. J. Koh, P.D. Scott, V. Chaudhary, G. Dhillon, An automatic segmentation method of the spinal canal from clinical MR images based on an attention model and an active contour model, in: ISBI, Chicago, IL, USA, 2011, pp. 1467–1471. http://dx.doi.org/10.1109/ISBI.2011.5872677.
12. L. Gui, R. Lisowski, T. Faundez, P.S. Huppi, Automatic segmentation of newborn brain MRI using mathematical morphology, in: ISBI, Chicago, IL, USA, 2011, pp. 2026–2030. http://dx.doi.org/10.1109/ISBI.2011.5872810.
13. Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T., A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imag. 21 (2002), 193–199.
14. Liew, A.W.-C., Yan, H., An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans. Med. Imag. 22 (2003), 1063–1075.
15. Chuang, K.-S., Tzeng, H.-L., Chen, S., Wu, J., Chen, T.-J., Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imag. Graphics 30 (2006), 9–15.
16. Bunyak, F., Hafiane, A., Palaniappan, K., Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets. Arabnia, H.R., Tran, Q.N., (eds.) Software Tools and Algorithms for Biological Systems Advances in Experimental Medicine and Biology, vol. 41, 2011, Springer, 413–424.
17. Zhuge, Y., Udupa, J.K., Intensity standardization simplifies brain MR image segmentation. Comput. Vis. Image Understand. 113 (2009), 1095–1103.
18. Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33 (2002), 341–355.
19. Wells, W. III, Grimson, W.E.L., Kikinis, R., Jolesz, F.A., Adaptive segmentation of MRI data. IEEE Trans. Med. Imag. 15 (1996), 429–442.
20. Marroquin, J.L., Vemuri, B.C., Botello, S., Calderon, E., Fernandez-Bouzas, A., An accurate and efficient Bayesian method for automatic segmentation of brain MRI. IEEE Trans. Med. Imag. 21 (2002), 934–945.
21. Zhang, Y., Brady, M., Smith, S., Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imag. 20 (2001), 45–57.
22. Liao, L., Lin, T., Li, B., MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach. Pattern Recogn. Lett. 29 (2008), 1580–1588.
23. Cappabianco, F.A., Falcao, A.X., Yasuda, C.L., Udupa, J.K., Brain tissue MR-image segmentation via optimum-path forest clustering. Comput. Vis. Image Understand. 116 (2012), 1047–1059.
24. Atkins, M.S., Mackiewich, B.T., Fully automatic segmentation of the brain in MRI. IEEE Trans. Med. Imag. 17 (1998), 98–107.
25. Amato, U., Larobina, M., Antoniadis, A., Alfano, B., et al. Segmentation of magnetic resonance brain images through discriminant analysis. J. Neurosci. Methods 131 (2003), 65–74.
26. Shen, S., Sandham, W., Granat, M., Sterr, A., MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Trans. Inform. Technol. Biomed. 9 (2005), 459–467.
27. Pal, N.R., Pal, S.K., A review on image segmentation techniques. Pattern Recogn. 26 (1993), 1277–1294.
28. Pham, D.L., Xu, C., Prince, J.L., Current methods in medical image segmentation. Ann. Rev. Biomed. Eng. 2 (2000), 315–337.
29. Ma, Z., Tavares, J.M.R.S., Jorge, R.N.M., Mascarenhas, T., A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput. Methods Biomech. Biomed. Eng. 13 (2010), 235–246.
30. Balafar, M.A., Ramli, A., Saripan, M.I., Mashohor, S., Review of brain MRI image segmentation methods. Artif. Intell. Rev. 33 (2010), 261–274.
31. A. Hafiane, F. Bunyak, K. Palaniappan, Clustering initiated multiphase active contours and robust separation of nuclei groups for tissue segmentation, in: IEEE International Conference on Pattern Recognition, 2008, pp. 1–4. http://dx.doi.org/10.1109/ICPR.2008.4761744.
32. Dogan, G., Morin, P., Nochetto, R.H., A variational shape optimization approach for image segmentation with a Mumford-Shah functional. SIAM J. Scient. Comput. 30 (2008), 3028–3049.
33. F. Bunyak, K. Palaniappan, Efficient segmentation using feature-based graph partitioning active contours, in: 12th IEEE International Conference on Computer Vision, Kyoto, Japan, 2009, pp. 873–880. http://dx.doi.org/10.1109/ICCV.2009.5459320.
34. E. Brown, T.F. Chan, X. Bresson, A convex relation method for a class of vector-valued minimization problems with applications to Mumford-Shah segmentation, Technical Report 43, UCLA CAM, 2010.
35. Brown, E.S., Chan, T.F., Bresson, X., Completely convex formulation of the Chan-Vese image segmentation model. Int. J. Comput. Vis. 98 (2012), 103–121.
36. J.C. Moreno, Contributions to Variational Image Segmentation, Ph.D. Thesis, University of Coimbra, Portugal, 2012.
37. Kang, S.H., March, R., Existence and regularity of minimizers of a functional for unsupervised multiphase segmentation. Nonlinear Anal.: Theory, Methods Appl. 76 (2013), 181–201.
38. C. Zach, D. Gallup, J.-M. Frahm, M. Niethammer, Fast global labeling for real-time stereo using multiple plane sweeps, in: Proceedings of Vision, Modeling, and Visualization, Konstanz, Germany, 2008, pp. 243–252.
39. J. Lellman, J. Kappes, J. Yuang, F. Becker, C. Schnörr, Convex multi-class image labeling by simplex-constrained total variation, in: Scale Space and Variational Methods in Computer Vision, vol. 5567, Voss, Norway, 2009, pp. 150–162. http://dx.doi.org/10.1007/978-3-642-02256-2_13.
40. Bae, E., Yuan, J., Tai, X.-C., Global minimization for continuous multiphase partitioning problems using a dual approach. Int. J. Comput. Vis. 92 (2011), 112–129.
41. Chambolle, A., Cremers, D., Pock, T., A convex approach to minimal partitions. SIAM J. Imag. Sci. 5 (2012), 1113–1158.
42. Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J., Osher, S., Fast global minimization of the active contour/snake model. J. Math. Imag. Vis. 28 (2007), 151–167.
43. Chambolle, A., An algorithm for total variation minimization and applications. J. Math. Imag. Vis. 20 (2004), 89–97.
44. Aujol, J.-F., Gilboa, G., Chan, T., Osher, S., Structure-texture image decomposition – modeling, algorithms and parameter selection. Int. J. Comput. Vis. 67 (2006), 111–136.
45. S. Merino-Caviedes, G. Vegas-Sanchez, M. Perez, S. Aja-Fernandez, M. Martin-Fernandez, Automatic segmentation of newborn brain MRI using mathematical morphology, in: ISBI, Rotterdam, 2010, pp. 908–911. http://dx.doi.org/10.1109/ISBI.2010.5490134.
46. Ben Salah, M., Mitiche, A., Ben Ayed, I., Effective level set image segmentation with a kernel induced data term. IEEE Trans. Image Process. 19 (2010), 220–232.
47. Ayed, I.B., Mitiche, A., A region merging prior for variational level set image segmentation. IEEE Trans. Image Process. 17 (2008), 2301–2311.
48. Chambolle, A., Pock, T., A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imag. Vis. 40 (2011), 120–145.
49. Aujol, J.-F., Chambolle, A., Dual norms and image decomposition models. Int. J. Comput. Vis. 63 (2005), 85–104.
50. Li, C., Kao, C.-Y., Gore, J.C., Ding, Z., Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17 (2008), 1940–1949.
51. Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q., Zhu, Y., Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation. Comput. Vis. Image Understand. 115 (2011), 256–269.
52. Figueiredo, I.N., Moreno, J.C., Prasath, V.B.S., Figueiredo, P.N., A segmentation model and application to endoscopic images. Campilho, A., Kamel, M., (eds.) International Conference on Image Analysis and Recognition (ICIAR 2012), Aveiro, Portugal LNCS, vol. 7325, 2012, Springer, 164–171 http://dx.doi.org/10.1007/978-3-642-31298-4_20.
53. I.N. Figueiredo, J.C. Moreno, V.B.S. Prasath, Texture image segmentation with smooth gradients and local information, in: Computational Modeling of Objects Presented in Images: Fundamentals, Methods and Applications (CompIMAGE 2012), Rome, Italy, 2012, pp. 164–171.
54. V.B.S. Prasath, F. Bunyak, P.S. Dale, S.R. Frazier, K. Palaniappan, Segmentation of breast cancer tissue microarrays for computer-aided diagnosis in pathology, in: First IEEE Healthcare Technology Conference: Translational Engineering in Health & Medicine, Houston, TX, USA, 2012, pp. 40–43.
55. V.B.S. Prasath, K. Palaniappan, G. Seetharaman, Multichannel texture image segmentation using weighted feature fitting based variational active contours, in: Eighth Indian Conference on Vision, Graphics and Image Processing (ICVGIP), Mumbai, India, 2012, pp. 78:1–78:6. http://dx.doi.org/10.1145/2425333.2425411.
56. Giusti, F., Minimal Surfaces and Functions of Bounded Variation. 1984, Birkhauser, Basel, Switzerland.
57. Kwan, R.-S., Evans, A., Pike, G., MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans. Med. Imag. 18 (1999), 1085–1097.
58. Unnikrishnan, R., Pantofaru, C., Hebert, M., Toward objective evaluation of image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 29 (2007), 929–944.
59. Dice, L.R., Measures of the amount of ecologic association between species. Ecology 26 (1945), 297–302.
60. Chan, T.F., Esedoglu, S., Aspects of total variation regularized L1 function approximation. SIAM J. Appl. Math. 65 (2005), 1817–1837.
61. M. Jung, M. Kang, M. Kang, Variational image segmentation model involving non-smooth data-fidelity terms, J. Scient. Comput., 2014.
62. Attouch, H., Buttazzo, G., Michaille, G., Variational analysis in Sobolev and BV spaces: applications to PDEs and optimization Society for Industrial and Applied Mathematics, 2006, SIAM, Philadelphia, PA, USA.
63. Aubert, G., Kornprobst, P., Mathematical Problems in Image Processing: Partial Differential Equation and Calculus of Variations. 2006, Springer-Verlag, New York, USA.