Bayesian technique for image classifying registration

IEEE Transactions on Image Processing

Volumn 21, Issue 9, 2012, Pages 4080-4091

  Hachama, Mohamed ^a   Desolneux, Agnès ^b   Richard, Frédéric J P ^c  

^a UNIVERSITY OF KHEMIS MILIANA   (Algeria)

^b UNIVERSITÉ PARIS SACLAY   (France)

^c AIX MARSEILLE UNIVERSITY   (France)

Author keywords

Image registration; lesion detection; mixture models

Indexed keywords

BAYESIAN FRAMEWORKS; BAYESIAN TECHNIQUES; CONTRAST AGENT; CONTRAST-ENHANCED IMAGES; ENERGY MINIMIZATION PROBLEM; GRADIENT DESCENT ALGORITHMS; IMAGE DEFORMATION; IMAGE INTENSITIES; LESION DETECTION; LOCAL DEVIATION; MAXIMUM A POSTERIORI ESTIMATION; MEDICAL DATA; MIXTURE COMPONENTS; MIXTURE MODEL; NORMAL TISSUE; PROBLEM FORMULATION; REAL APPLICATIONS; REGISTRATION ACCURACY; REGISTRATION PROBLEMS; SIMILARITY CRITERIA; SPATIAL VARIATIONS; TEMPLATE-BASED;

COMPUTER SIMULATION; IMAGE REGISTRATION; MEDICAL IMAGING; PIXELS; PROBABILITY DISTRIBUTIONS; TISSUE;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; BAYES THEOREM; COMPUTER SIMULATION; DIAGNOSTIC IMAGING; IMAGE PROCESSING; MAMMOGRAPHY; METHODOLOGY;

ALGORITHMS; BAYES THEOREM; COMPUTER SIMULATION; DIAGNOSTIC IMAGING; IMAGE PROCESSING, COMPUTER-ASSISTED; MAMMOGRAPHY;

EID: 84865440816     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2200495     Document Type: Article

References (67)

1. L. Brown, "A survey of image registration techniques," ACM Comput. Surveys, vol. 24, no. 4, pp. 325-376, 1992.
2. D. Hill, P. Batchelor, M. Holden, and D. J. Hawkes, "Medical image registration," Phys. Med. Biol., vol. 46, no. 3, pp. R1-R45, 2001. (Pubitemid 32218849)
3. J. Maintz and M. Viergever, "A survey of medical image registration," Med. Image Anal., vol. 2, no. 1, pp. 1-36, 1998. (Pubitemid 128500719)
4. P. Pluim, J. Maintz, and M. Viergever, "Mutual-information-based registration of medical images: A survey," IEEE Trans. Med. Imaging, vol. 22, no. 8, pp. 986-1004, Aug. 2003.
5. J. Modersitzki, Numerical Methods for Image Registration (Numerical Mathematics and Scientific Computation), C. S. G. H. Golub and E. Suli, Eds. New York: Oxford Univ. Press, 2004.
6. R. Woods, J. Mazziotta, and S. R. Cherry, "MRI-PET registration with automate algorithm," J. Comput. Assisted Tomography, vol. 17, no. 4, pp. 536-46, Jul.-Aug. 1993.
7. T. M. Buzug and J. Weese, "Voxel-based similarity measures for medical image registration in radiological diagnosis and image guided surgery," J. Comput. Inf. Technol., vol. 6, no. 2, pp. 165-179, 1998.
8. A. Roche, G. Malandain, X. Pennec, and N. Ayache, "The correlation ratio as a new similarity measure for multimodal image registration," in Proc. 1st Int. Conf. Med. Image Comput. Comput.-Assisted Intervent., vol. 1496. Oct. 1998, pp. 1115-1124. (Pubitemid 128152762)
9. C. Studholme, D. Hill, and D. J. Hawkes, "An overlap invariant entropy measure of 3D medical image alignment," Pattern Recognit., vol. 1, no. 32, pp. 71-86, 1999.
10. A. Roche, G. Malandain, and N. Ayache, "Unifying maximum likelihood approaches in medical image registration," Int. J. Comput. Vis. Imaging Syst. Technol., vol. 11, no. 1, pp. 71-80, 2000.
11. G. Malandain, "Les mesures de similarité pour le recalage des images médicales," Habilitation à Diriger Recherches, Univ. Nice Sophia-Antipolis, Nice, France, Tech. Rep., Mar. 2006.
12. P. Hayton, M. Brady, L. Tarassenko, and N. Moore, "Analysis of dynamic MR breast images using a model of contrast enhancement," Med. Image Anal., vol. 1, no. 3, pp. 207-224, 1997. (Pubitemid 127502899)
13. C. Tanner, J. A. Schnabel, D. Chung, M. J. Clarkson, D. Rueckert, D. Hill, and D. J. Hawkes, "Volume and shape preservation of enhancing lesions when applying non-rigid registration to a time series of contrast enhancing MR breast images," in Proc. 3rd Int. Conf. Med. Image Comput. Comput.-Assisted Intervent., London, U.K., 2000, pp. 327-337.
14. T. Rohlfing and C. R. Maurer, Jr., "Intensity-based non-rigid registration using adaptive multilevel free-form deformation with an incompressibility constraint," in Proc. 4th Int. Conf. Medical Image Comput. Comput.-Assisted Intervent., vol. 2208. 2001, pp. 111-119. (Pubitemid 33352403)
15. N. Sebe and M. Lew, Robust Computer Vision: Theory and Applications (Computational Imaging and Vision), vol. 26. New York: Springer-Verlag, 2003.
16. P. Rousseeuw and A. Leroy, Robust Regression and Outlier Detection. New York: Wiley, 1987.
17. F. Hampel, E. Ronchetti, P. Rousseeuw, and W. Stahel, Robust Statistics: The Approach Based on Influence Functions. New York: Wiley, 1986.
18. D. Hawkins, Identifications of Outliers. New York: Chapman & Hall, 1980.
19. P. Huber, Robust Statistics. New York: Wiley, 1981.
20. F. Richard, "A new approach for the registration of images with inconsistent differences," in Proc. Int. Conf. Pattern Recognit., vol. 4. Cambridge, U.K., 2004, pp. 649-652.
21. C. Nikou, F. Heitz, and J.-P. Armspach, "Robust voxel similarity metrics for the registration of dissimilar single and multimodal images," Pattern Recognit., vol. 32, no. 8, pp. 1351-1368, 1999. (Pubitemid 29435181)
22. J. Kim, "Intensity based image registration using robust similarity measure and constrained optimization: Applications for radiation therapy," Ph.D. dissertation, Dept. Electr. Eng., Univ. Michigan, Ann Arbor, 2004.
23. M. Black, "Robust incremental optical flow," Ph.D. thesis, Dept. Comput. Sci., Yale Univ., New Haven, CT, Res. Rep. YALEU-DCSRR- 923, 1992.
24. P. Meer, D. Mintz, A. Rosenfeld, and D. Kim, "Robust regression methods for computer vision: A review," Int. J. Comput. Vis., vol. 6, no. 1, pp. 59-70, 1991. (Pubitemid 23621985)
25. M. Black and A. Rangarajan, "On the unification of line processes, outlier rejection, and robust statistics with applications in early vision," Int. J. Comput. Vis., vol. 19, no. 1, pp. 57-91, 1996. (Pubitemid 126713881)
26. N. S. Netanyahu and I. Weiss, "Analytic outlier removal in line fitting," in Proc. 12th IAPR Int. Conf. Comput. Vis. Image Process., vol. 2B. 1994, pp. 406-408.
27. P. Schroeter, J.-M. Vesin, T. Langenberger, and R. Meuli, "Robust parameter estimation of intensity distributions for brain magnetic resonance images," IEEE Trans. Med. Imaging, vol. 17, no. 2, pp. 172-186, Apr. 1998. (Pubitemid 128747671)
28. P. S. Torr, R. Szeliski, and P. Anandan, "An integrated Bayesian approach to layer extraction from image sequences," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 3, pp. 297-303, Mar. 2001. (Pubitemid 32286636)
29. D. Hasler, L. Sbaiz, S. Susstrunk, and M. Vetterli, "Outlier modeling in image matching," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 3, pp. 301-315, Mar. 2003.
30. P. Biber, S. Fleck, and W. Straßer, "A probabilistic framework for robust and accurate matching of point clouds," in Proc. 26th Pattern Recognit. Symp., 2004, pp. 480-487.
31. A. Jepson and M. Black, "Mixture models for optical flow computation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 1993, pp. 760-761.
32. M. Hachama, A. Desolneux, and F. Richard, "Combining registration and abnormality detection in mammography," in Proc. Workshop Biomed. Image Regist., vol. 4057. 2006, pp. 178-185. (Pubitemid 44113214)
33. H. Hachama, F. Richard, and A. Desolneux, "A mammogram registration technique dealing with outliers," in Proc. IEEE Int. Symp. Biomed. Imaging, Apr. 2006, pp. 458-461. (Pubitemid 44732352)
34. M. Hachama, F. Richard, and A. Desolneux, "A probabilistic approach for the simultaneous mammogram registration and abnormality detection," in Proc. Int. Workshop Digital Mammography, vol. 4046. 2006, pp. 205-212. (Pubitemid 44113113)
35. N. Rougon, A. Discher, and F. Preteux, "Region-driven statistical non-rigid registration: Application to model-based segmentation and tracking of the heart in perfusion MRI," Proc. SPIE, vol. 5916, pp. 148-159, Aug. 2005.
36. C. Studholme, D. Hill, and D. J. Hawkes, "Incorporating connected region labeling into automated image registration using mutual information," in Proc. IEEE Workshop Math. Methods Biomed. Image Anal., San Francisco, CA, Jun. 1996, pp. 23-31.
37. J. Chappelow, B. Bloch, N. Rofsky, E. Genega, R. Lenkinski, W. DeWolf, and A. Madabhushi, "Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information," Med. Phys., vol. 38, no. 4, pp. 2005-2018, 2011.
38. P. Patel, J. Chappelow, J. Tomaszewski, M. Feldman, M. Rosen, N. Shih, and A. Madabhushi, "Spatially weighted mutual information (SWMI) for registration of digitally reconstructed ex vivo whole mount histology and in vivo prostate MRI," in Proc. IEEE Int. Conf. Eng. Med. Biol. Soc., Sep. 2011, pp. 6269-6272.
39. M. Hachama, A. Desolneux, C. Cuenod, and F. Richard, "A classifying registration technique for the estimation of enhancement curves of DCE-CT scan sequences," Med. Image Anal., vol. 14, no. 2, pp. 185-194, 2010.
40. J. Ashburner and K. Friston, "Unified segmentation," Neuroimaging, vol. 26, no. 3, pp. 839-851, 2005. (Pubitemid 40825026)
41. K. Pohl, J. Fisher, W. Grimson, R. Kikinis, and W. Wells, "A Bayesian model for joint segmentation and registration," Neuroimage, vol. 31, no. 1, pp. 228-239, 2006. (Pubitemid 44250881)
42. P. Wyatt and A. Noble, "MAP MRF joint segmentation and registration of medical images," Med. Image Anal., vol. 7, no. 4, pp. 539-552, 2003. (Pubitemid 37322316)
43. C. Xiaohua, M. Brady, J. L.-C. Lo, and N. Moore, "Simultaneous segmentation and registration of contrast-enhanced breast MRI," in Information Processing in Medical Imaging. New York: Springer-Verlag, 2005, pp. 126-137. (Pubitemid 41425310)
44. Y. Zheng, J. Yu, C. Kambhamettu, S. Englander, M. Schnall, and D. Shen, "De-enhancing the dynamic contrast-enhanced breast MRI for robust registration," in Proc. MICCAI, vol. 4791. 2007, pp. 933-941.
45. D. Mumford, "The Bayesian rationale for energy functionals," in Geometry-Driven Diffusion in Computer Vision, vol. 46. Boston, MA: Kluwer, 1994, pp. 141-153.
46. C. A. Glasbey and K. V. Mardia, "A review of image-warping methods," J. Appl. Stat., vol. 25, no. 2, pp. 155-171, Apr. 1998. (Pubitemid 28388650)
47. C. Broit, "Optimal registration of deformed images," Ph.D. dissertation, Dept. Comput. Sci., Univ. Pennsylvania, Philadelphia, 1981.
48. R. Bajcsy, R. Lieberson, and M. Reivich, "Computerized system for the elastic matching of deformed radiographic images to idealized atlas images," J. Comput. Assisted Tomography, vol. 7, no. 4, pp. 618-625, 1983. (Pubitemid 13035945)
49. R. Bajcsy and S. Kovacic, "Multiresolution elastic matching," Comput. Vis., Graph., Image Process., vol. 46, no. 1, pp. 1-21, 1989.
50. M. Miller, G. Christensen, Y. Amit, and U. Grenander, "Mathematical textbook of deformable neuroanatomies," Proc. Nat. Acad. Sci., vol. 90, no. 24, pp. 11944-11948, 1993. (Pubitemid 24008756)
51. Y. Amit, U. Grenander, and M. Piccioni, "Structural image restoration through deformable templates," Amer. Stat. Assoc., vol. 86, no. 414, pp. 376-387, Jun. 1991.
52. F. L. Bookstein, "Principal warps: Thin-plate splines and the decomposition of deformations," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 6, pp. 567-585, Jun. 1989.
53. S. Allassonnière, Y. Amit, and A. Trouvé, "Toward a coherent statistical framework for dense deformable template estimation," J. Royal Stat. Soc. Ser. B, vol. 69, no. 1, pp. 3-29, 2007.
54. F. Richard, A. Samson, and C. Cuenod, "A SAEM algorithm for the estimation of template and deformation parameters in medical image sequences," Stat. Comput., vol. 19, no. 4, pp. 465-478, 2009.
55. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 6, no. 6, pp. 721-741, Nov. 1984. (Pubitemid 15453722)
56. B. Chalmond, Modelling and Inverse Problems in Image Analysis (Applied Mathematical Sciences), vol. 155. New York: Springer-Verlag, 2003.
57. M. Staring, S. Klein, and J. Pluim, "Nonrigid registration with tissue-dependent filtering of the deformation field," Phys. Med. Biol., vol. 52, no. 23, pp. 6879-6892, 2007. (Pubitemid 350150619)
58. P. G. Ciarlet, The Finite Element Method for Elliptic Problems. Amsterdam, The Netherlands: North-Holland, 1978.
59. J. Bonnans, J. Gilbert, C. Lemaréchal, and C. Sagastizábal, Numerical Optimization - Theoretical and Practical Aspects. Berlin, Germany: Springer-Verlag, 2006.
60. H. Rue and L. Held, Gaussian Markov Random Fields: Theory and Applications (Monographs on Statistics and Applied Probability), vol. 104. London, U.K.: Chapman & Hall, 2005.
61. F. Hechtand, O. Pironneau, A. L. Hyaric, and K. Ohtsuka. (2005, Oct.). FreeFEM++ Manual [Online]. Available: http://www.freefem.org/
62. R. Woods, "Validation of registration accuracy," in Handbook of Medical Imaging, I. Bankman, Ed. Orlando, FL: Academic, 2000, pp. 491-497.
63. P. Hellier, C. Barillot, I. Corouge, B. Gibaud, G. L. Goualher, D. Collins, A. Evans, G. Malandain, N. Ayache, G. Christensen, and H. Johnson, "Retrospective evaluation of intersubject brain registration," IEEE Trans. Med. Imaging, vol. 22, no. 9, pp. 1120-1130, Sep. 2003.
64. J. Suckling, J. Parker, D. R. Dance, S. Astley, I. Hutt, C. Boggis, I. Ricketts, E. Stamatakis, N. Cerneaz, S. L. Kok, P. Taylor, D. Betal, and J. Savage, "The mammographie image analysis society digital mammogram database," in Proc. 2nd Int. Workshop Digital Mammography, 1994, pp. 375-378.
65. T. Soong, Fundamentals of Probability and Statistics for Engineers. New York: Wiley, 2004.
66. M. Sallam and K. Bowyer, "Registration and difference analysis of corresponding mammogram images," Med. Image Anal., vol. 3, no. 2, pp. 103-118, 1999. (Pubitemid 129641352)
67. C. Metz, "ROC methodology in radiologic imaging," Invest. Radiol., vol. 21, no. 9, pp. 720-733, 1986. (Pubitemid 16015664)