A nonlocal energy minimization approach to brain image segmentation with simultaneous bias field estimation and denoising

Machine Vision and Applications

Volumn 25, Issue 2, 2014, Pages 529-544

  Chen, Zengsi ^a   Wang, Jinwei ^b   Kong, Dexing ^c   Dong, Fangfang ^d  

^a ZHEJIANG CHINESE MEDICAL UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c ZHEJIANG UNIVERSITY   (China)

^d ZHEJIANG GONGSHANG UNIVERSITY   (China)

Author keywords

Bias field; Energy functional; Image segmentation; Membership functions; Nonlocal regularization

Indexed keywords

EID: 84894902227     PISSN: 09328092     EISSN: 14321769     Source Type: Journal    
DOI: 10.1007/s00138-013-0546-5     Document Type: Article

References (37)

1. Thompson, P.; Mega, M.; Woods, R.; Zoumalan, C.; Lindshield, C.; Blanton, R.; Moussai, J.; Holmes, C.; Cummings, J.; Toga, A.: Cortical change in alzheimer's disease detected with a disease-specific population-based brain atlas. Cereb. Cortex 11(1), 1-16 (2001)
2. Acton, P.; Newberg, A.: Artificial neural network classifier for the diagnosis of parkinson's disease using [99mtc] trodat-1 and spect. Phys. Med. Biol. 51, 3057-3066 (2006)
3. Tincher, M.; Meyer, C.; Gupta, R.; Williams, D.: Polynomial modeling and reduction of rf body coil spatial inhomogeneity in MRI. IEEE Trans. Med. Imaging 12(2), 361-365 (1993)
4. Liew, A.; Yan, H.: An adaptive spatial fuzzy clustering algorithm for 3-d mr image segmentation. IEEE Trans. Med. Imaging 22(9), 1063-1075 (2003)
5. Li, C.; Gatenby, C.; Wang, L, Gore, J.: A robust parametric method for bias field estimation and segmentation of mr images. IEEE Conference on Computer Vision and Pattern Recognition, pp. 218-223 (2009)
6. García-Sebastián, M.; Fernández, E.; Graña, M.; Torrealdea, F.: A parametric gradient descent mri intensity inhomogeneity correction algorithm. Pattern Recognit. Lett. 28(13), 1657-1666 (2007)
7. Van Leemput, K.; Maes, F.; Vandermeulen, D.; Suetens, P.: Automated model-based bias field correction of mr images of the brain. IEEE Trans. Med. Imaging 18(10), 885-896 (1999)
8. Gispert, J.; Reig, S.; Pascau, J.; Vaquero, J.; García-Barreno, P.; Desco, M.: Method for bias field correction of brain t1-weighted magnetic resonance images minimizing segmentation error. Human Brain Mapp. 22(2), 133-144 (2004)
9. Wells III, W.; Grimson, W.; Kikinis, R.; Jolesz, F.: Adaptive segmentation of mri data. IEEE Trans. Med. Imaging 15(4), 429-442 (1996)
10. Guillemaud, R.; Brady, M.: Estimating the bias field of mr images. IEEE Trans. Med. Imaging 16(3), 238-251 (1997)
11. Sled, J.; Pike, G.: Understanding intensity non-uniformity in mri. Medical Image Computing and Computer-Assisted InterventationMICCAI98 pp. 614-622 (1998)
12. Likar, B.; Viergever, M.; Pernus, F.: Retrospective correction of mr intensity inhomogeneity by information minimization. IEEE Trans. Med. Imaging 20(12), 1398-1410 (2001)
13. Ahmed, M.; Yamany, S.; Mohamed, N.; Farag, A.; Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of mri data. IEEE Trans. Med. Imaging 21(3), 193-199 (2002)
14. Mayer, A.; Greenspan, H.: An adaptive mean-shift framework for mri brain segmentation. IEEE Trans. Med. Imaging 28(8), 1238-1250 (2009)
15. Li, C.; Xu, C.; Anderson, A.; Gore, J.: Mri tissue classification and bias field estimation based on coherent local intensity clustering: A unified energy minimization framework. In: Information Processing in Medical Imaging, pp. 288-299. Springer, Berlin (2009)
16. Li, C.; Li, F.; Kao, C.; Xu, C.: Image segmentation with simultaneous illumination and reflectance estimation: An energy minimization approach. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 702-708 (2009)
17. Ji, Z.; Chen, Q.; Sun, Q.; Xia, D.; Heng, P.: Mr image segmentation and bias field estimation using coherent local and global intensity clustering. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Vol. 2. IEEE, pp. 578-582 (2010)
18. Ji, Z.; Xia, Y.; Sun, Q.; Chen, Q.; Xia, D.; Feng, D.: Fuzzy local gaussian mixture model for brain mr image segmentation. IEEE Trans. Inf. Technol. Biomed. 16(3), 339-347 (2012)
19. Ji, Z.; Xia, Y.; Sun, Q.; Xia, D.; Feng, D.: Local gaussian distribution fitting based FCM algorithm for brain MR image segmentation. Intelligent Science and Intelligent Data Engineering, pp. 318-325 (2012)
20. Derganc, J.; Likar, B.; Pernus, F.: Nonparametric segmentation of multispectral mr images incorporating spatial and intensity information. In: Proceedings of SPIE, vol 4684, p. 391 (2002)
21. Vovk, U.; Pernus, F.; Likar, B.: A review of methods for correction of intensity inhomogeneity in mri. IEEE Trans. Med. Imaging 26(3), 405-421 (2007)
22. Bresson, X.; Chan, T.: Non-local unsupervised variational image segmentation models. UCLA cam report 08-67 (2008)
23. Jung, M.; Vese, L.: Nonlocal variational image deblurring models in the presence of gaussian or impulse noise. Scale Space and Variational Methods in Computer Vision, pp. 401-412 (2009)
24. Caldairou, B.; Rousseau, F.; Passat, N.; Habas, P.; Studholme, C.; Heinrich, C.: A non-local fuzzy segmentation method: Application to brain mri. In: Computer Analysis of Images and Patterns, pp. 606-613, Springer, Berlin (2009)
25. Buades, A.; Coll, B.; Morel, J.M.: A non-local algorithm for image denoising. CVPR 2005. IEEE Computer Society Conference, vol. 2, pp. 60-65 (2005)
26. Gilboa, G.; Osher, S.: Nonlocal operators with applications to image processing. Multiscale Model. Simul. 7(3), 1005-1028 (2008)
27. Caldairou, B.; Passat, N.; Habas, P.; Studholme, C.; Rousseau, F.: A non-local fuzzy segmentation method: application to brain mri. Pattern Recognit. 44(9), 1916-1927 (2011)
28. Pham, D.; Prince, J.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imaging 18(9), 737-752 (1999)
29. Pham, D.: Spatial models for fuzzy clustering. Comput. Vis. Image Underst. 84(2), 285-297 (2001)
30. Rudin, L.; Osher, S.; Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60(1-4), 259-268 (1992)
31. Chen, Y.; Zhang, J.; Macione, J.: An improved level set method for brain mr images segmentation and bias correction. Comput. Med. Imag. Graph. 33(7), 510-519 (2009)
32. Zhou, D.; Schölkopf, B.: A regularization framework for learning from graph data. In: ICML Workshop on Statistical Relational Learning and its Connections to other Field. Banff, Alberta, Canada, pp. 132-137 (2004)
33. Goldstein, T.; Osher, S.: The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci. 2(2), 323-343 (2009)
34. Osher, S.; Burger, M.; Goldfarb, D.; Xu, J.; Yin, W.: An iterative regularization method for total variation-based image restoration. Multiscale Model. Simul. 4(2), 460 (2005)
35. Cocosco, C.; Kollokian, V.; Kwan, R.; Pike, G.; Evans, A.: Brainweb: Online interface to a 3d mri simulated brain database. In: NeuroImage, Citeseer (1997)
36. Worth, A.J.: Internet brain segmentation repository. http://www.cma.mgh. harvard.edu/ibsr/
37. Wang, J.; Guo, Y.; Ying, Y.; Liu, Y.; Peng, Q.: Fast non-local algorithm for image denoising. In: 2006 IEEE International Conference on Image Processing, pp. 1429-1432 (2006)