Image analysis – Applications – Biomedical applications
Reexamination Certificate
2011-03-08
2011-03-08
Werner, Brian P (Department: 2624)
Image analysis
Applications
Biomedical applications
C382S159000, C382S224000, C382S190000, C382S294000
Reexamination Certificate
active
07903857
ABSTRACT:
Disclosed is robust click-point linking, defined as estimating a single point-wise correspondence between data domains given a user-specified point in one domain or as an interactive localized registration of a monomodal data pair. To link visually dissimilar local regions, Geometric Configuration Context (GCC) is introduced. GCC represents the spatial likelihood of the point corresponding to the click-point in the other domain. A set of scale-invariant saliency features are pre-computed for both data. GCC is modeled by a Gaussian mixture whose component mean and width are determined as a function of the neighboring saliency features and their correspondences. This allows correspondence of dissimilar parts using only geometrical relations without comparing the local appearances. GCC models are derived for three transformation classes: pure translation, scaling and translation, and similarity transformation. For solving the linking problem, a variable-bandwidth mean shift method is adapted for estimating the maximum likelihood solution of the GCC.
REFERENCES:
patent: 6597801 (2003-07-01), Cham et al.
patent: 2001/0055016 (2001-12-01), Krishnan
patent: 2004/0133083 (2004-07-01), Comaniciu et al.
patent: 2004/0147840 (2004-07-01), Durrirala et al.
patent: 2004/0193036 (2004-09-01), Zhou et al.
patent: 2004/0223633 (2004-11-01), Krishnan
patent: 2004/0228529 (2004-11-01), Jerebko et al.
patent: 2005/0036710 (2005-02-01), Okada et al.
patent: 2005/0058338 (2005-03-01), Krishnan et al.
patent: 2005/0135663 (2005-06-01), Okada et al.
patent: 2005/0147303 (2005-07-01), Zhou et al.
patent: 2005/0201606 (2005-09-01), Okada et al.
patent: 2005/0251013 (2005-11-01), Krishnan et al.
patent: 2006/0008138 (2006-01-01), Zhou et al.
patent: 2006/0050958 (2006-03-01), Okada et al.
patent: 2006/0050960 (2006-03-01), Tu et al.
patent: 2006/0064007 (2006-03-01), Comaniciu et al.
patent: 2006/0074834 (2006-04-01), Dong et al.
patent: 2006/0079761 (2006-04-01), Tu et al.
patent: 2006/0171586 (2006-08-01), Georgescu et al.
patent: 2006/0239552 (2006-10-01), Tu et al.
patent: 2006/0269109 (2006-11-01), Okada et al.
Shi et al. (Jun. 1994) “Good features to track.” Proc. 1994 IEEE Conf. on Computer Vision and Pattern Recognition, pp. 593-600.
Siadat et al. (May 2004) “Bayesian landmark identification in medical images.” Proc. SPIE vol. 5370, pp. 628-639.
Betke et al. (2003) “Landmark detection in the chest and registration of lung surfaces with an application to nodule registration.” Medical Image Analysis, vol. 7 pp. 265-281.
T. Kadir and M. Brady, “Saliency, scale and image description,” International Journal of Computer Vision, vol. 45, No. 2, pp. 83-105, 2001.
X. Huang, Y. Sun, D. Metaxas, F. Sauer, and C. Xu, “Hybrid image registration based on configural matching of scale-invariant salient region features, ” in Second IEEE Workshop on Image and Video Registration, in conjuction with CVPR '04, 2004.
D. Hahn, Y. Sun, J. Homegger, C. Xu, G. Wolz, and T. Kuwert, “A practical salient region feature based 3D multimodality registration method for medical images.” in SPIE Med. Imag., 2006.
D. Comaniciu, “An algorithm for data-driven bandwidth selection,” IEEE Trans. Pat. Anal. Mach. Intell, vol. 25, No. 2, pp. 281-288, 2003.
R. Fergus, P. Perona, and A. Zisserman, “Object class recognition by unsupervised scale-invariant learning,” in IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, 2003, pp. 264-271.
B. Epshtein and S. Ullman, “Identifying semantically equivalent object fragments,” in IEEE Coni on Computer Vision and Pattern Recognition. vol. 1, 2005, pp. 2-9.
X. Pennec, N. Ayache, and J. Thirion, “Landmark-based registration using features identified through differential geometry,” in Handbook of Medical Imaging, Academic Press, 2000, pp. 499-513.
K. Okada, D. Cornaniciu, and A. Krishnan, “Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT,” IEEE Trans. Med. Imag., vol. 24, No. 3, pp. 409-423, 2005.
C. Novak, H. Shen, B. Odry, J. Ko, and D. Naidich, “System for automatic detection of lung nodules exhibiting growth,” in SPIE Med. Imag., 2004.
P. J. Besl and N. D. McKay, “A method for registration of 3-d shapes,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, No. 2, pp. 239-256, 1992.
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proc. of IEEE Int'l Cont on Computer Vision and Pattern Recognition, 2001, pp. 511-518.
B. Georgescu, X. S. Zhou, D., Comaniciu, and A. Gupta, “Database-guided segmentation of anatomical structures with complex appearance,” in Proc. of IEEE Int'l Cont on Computer Vision and Pattern Recognition, 2005, pp. 429-436.
Huang Xiaolei
Krishnan Arun
Okada Kazunori
Zhou Xiang
Drennan Barry
Paschburg Donald B.
Siemens Medical Solutions USA , Inc.
Werner Brian P
LandOfFree
Robust click-point linking with geometric configuration... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Robust click-point linking with geometric configuration..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Robust click-point linking with geometric configuration... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2743366