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Issue No.10 - October (2011 vol.33)
pp: 2081-2092
Chen Xing , University of Minnesota, Minneapolis
Peihua Qiu , University of Minnesota, Minneapolis
ABSTRACT
Image registration is used widely in applications for mapping one image to another. Existing image registration methods are either feature-based or intensity-based. Feature-based methods first extract relevant image features and then find the geometrical transformation that best matches the two corresponding sets of features extracted from the two images. Because identification and extraction of image features is often a challenging and time-consuming process, intensity-based image registration, by which the mapping transformation is estimated directly from the observed image intensities of the two images, has received much attention recently. In the literature, most existing intensity-based image registration methods estimate the mapping transformation globally by solving a minimization/maximization problem defined by the two entire images to register. To this end, it needs to be assumed that the mapping transformation has a certain type of parametric form or it is a continuous bivariate function satisfying certain regularity conditions. In this paper, we propose a novel intensity-based image registration method using nonparametric local smoothing. By this method, the mapping transformation at a given pixel is estimated locally in a neighborhood after certain image features are accommodated in the estimation. Due to the flexibility of local smoothing, this method does not require any parametric form for the mapping transformation. It even allows the transformation to be a discontinuous function. Numerical examples show that it is effective in various applications.
INDEX TERMS
Degeneration, discontinuity, edge detection, local smoothing, mapping, nonparametric transformation, weighted least squares estimation.
CITATION
Chen Xing, Peihua Qiu, "Intensity-Based Image Registration by Nonparametric Local Smoothing", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 10, pp. 2081-2092, October 2011, doi:10.1109/TPAMI.2011.26
REFERENCES
[1] R.J. Althof, M.G.J. Wind, and J.T. Dobbins, "A Rapid and Automatic Image Registration Algorithm with Subpixel Accuracy," IEEE Trans. Medical Imaging, vol. 16, no. 3, pp. 308-316, June 1997.
[2] B. Avants, C. Epstein, M. Grossman, and J. Gee, "Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain," Medical Image Analysis, vol. 12, pp. 26-41, 2008.
[3] J. Barron, D. Fleet, and S. Beauchemin, "Performance of Optical Flow Techniques," Int'l J. Computer Vision, vol. 12, no. 1, pp. 43-77, 1994.
[4] F. Beg, M. Miller, A. Trouv'e, and L. Younes, "Computing Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms," Int'l J. Computer Vision, vol. 6, pp. 139-157, 2005.
[5] L.G. Brown, "A Survey of Image Registration Technique," ACM Computing Surveys, vol. 24, pp. 326-376, 1992.
[6] M. Davis, A. Khotanzad, D. Flamig, and S. Harms, "Physics-Based Coordinate Transformation for 3D Image Matching," IEEE Trans. Medical Imaging, vol. 16, no. 3, pp. 317-328, June 1997.
[7] E. Denton, L. Sonoda, D. Rueckert, S. Rankin, C. Hayes, M. Leach, D. Hill, and D. Hawkes, "Comparison and Evaluation of Rigid, Affine, and Nonrigid Registration of Breast MR Images," J. Computer Assisted Tomography, vol. 23, pp. 800-805, 1999.
[8] F. Dufaux and J. Konrad, "Efficient, Robust, and Fast Global Motion Estimation for Video Coding," IEEE Trans. Image Processing, vol. 9, no. 3, pp. 497-501, Mar. 2000.
[9] J. Fan and I. Gijbels, Local Polynomial Modeling and Its Applications. Chapman and Hall, 1996.
[10] L. Freire, A. Roche, and J.F. Mangin, "What Is the Best Similarity Measure for Motion Correction in FMRI Time Series?" IEEE Trans. Medical Imaging, vol. 21, no. 5, pp. 470-484, May 2002.
[11] A. Goshtasby and G.C. Stockman, "Point Pattern Matching Using Convex Hull Edges," IEEE Trans. Systems, Man, and Cybernetics, vol. 15, no. 5, pp. 631-637, 1985.
[12] P. Hall and P. Qiu, "Blind Deconvolution and Deblurring in Image Analysis," Statistical Sinica, vol. 17, pp. 1483-1509, 2007.
[13] B. Horn and B. Schunck, "Determining Optical Flow," Artificial Intelligence, vol. 17, nos. 1-3, pp. 185-204, 1981.
[14] M. Irani and S. Peleg, "Motion Analysis for Image Enhancement: Resolution, Occlusion and Transparency," J. Visual Comm. and Image Representation, vol. 4, no. 4, pp. 324-335, Dec. 1993.
[15] Y. Keller, A. Averbuch, and M. Israeli, "Pseudopolar-Based Estimation of Large Translation, Rotations and Scalings in Images," IEEE Trans. Image Processing, vol. 14, no. 1, pp. 12-22, Jan. 2005.
[16] A. Klein et al., "Evaluation of 14 Nonlinear Deformation Algorithms Applied to Human Brain MRI Registration," NeuroImage, vol. 46, pp. 786-802, 2009.
[17] D. Lavine, B. Lambird, and L. Kanal, "Recognition of Spatial Point Patterns," Pattern Recognition, vol. 16, pp. 289-295, 1983.
[18] H. Li, B.S. Manjunath, and S.K. Mitra, "A Contour-Based Approach to Multisensor Image Registration," IEEE Trans. Image Processing, vol. 4, no. 3, pp. 320-334, Mar. 1995.
[19] L. Liu, T. Jiang, J. Yang, and C. Zhu, "Fingerprint Registration by Maximization of Mutual Information," IEEE Trans. Image Processing, vol. 15, no. 5, pp. 1100-1110, May 2006.
[20] F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, "Registration by Maximization of Mutual Information," IEEE Trans. Medical Imaging, vol. 16, no. 2, pp. 187-198, Apr. 1997.
[21] J. Modersitzki, Fair: Flexible Algorithms for Image Registration. SIAM, 2009.
[22] C. Nikou, F. Heitz, J.-P. Armspach, I.-J. Namer, and D. Grucker, "Registration of MR/MR and MR/Spect Brain Images by Fast Stochastic Optimization of Robust Voxel Similarity Measures," Neuroimage, vol. 8, pp. 30-43, 1998.
[23] W. Pan, K. Qin, and Y. Chen, "An Adaptable-Multilayer Fractional Fourier Transform Approach for Image Registration," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 3, pp. 400-412, Mar. 2009.
[24] X. Papademetris, A. Jackowski, R. Schultz, L. Staib, and J. Duncan, "Integrated Intensity and Point-Feature Nonrigid Registration," Proc. Seventh Int'l Conf. Medical Image Computing and Computer-Assisted Intervention, pp. 763-770, Sept. 2004.
[25] A. Peter and A. Rangarajan, "Maximum Likelihood Wavelet Density Estimation with Applications to Image and Shape Matching," IEEE Trans. Image Processing, vol. 17, no. 4, pp. 458-468, Apr. 2008.
[26] J.P.W. Pluim, J.B.A. Maintz, and M.A. Viergever, "Mutual-Information-Based Registration of Medical Images: A Survey," IEEE Trans. Medical Imaging, vol. 22 no. 8, pp. 986-1004, Aug. 2003.
[27] P. Qiu, Image Processing and Jump Regression Analysis. John Wiley and Sons, 2005.
[28] P. Qiu, "A Nonparametric Procedure for Blind Image Deblurring," Computational Statistics and Data Analysis, vol. 52, pp. 4828-4841, 2008.
[29] P. Qiu and S. Bhandarkar, "An Edge Detection Technique Using Local Smoothing and Statistical Hypothesis Testing," Pattern Recognition Letters, vol. 17, pp. 849-872, 1996.
[30] P. Qiu and T. Nguyen, "On Image Registration in Magnetic Resonance Imaging," Proc. Int'l Conf. BioMedical Eng. and Informatics, pp. 753-757, May 2008.
[31] P. Qiu and B. Yandell, "Jump Detection in Regression Surfaces," J. Computational and Graphical Statistics, vol. 6, no. 3, pp. 332-354, 1997.
[32] A. Rajwade, A. Banerjee, and A. Rangarajan, "Probability Density Estimation Using Isocontours and Isosurfaces: Application to Information-Theoretic Image Registration," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 3, pp. 475-491, Mar. 2009.
[33] N. Ritter, R. Owens, J. Cooper, R. Eikelboom, and P.V. Saarloos, "Registration of Stereo and Temporal Images of the Retina," IEEE Trans. Medical Imaging, vol. 18, no. 5, pp. 404-418, May 1999.
[34] N. Saeed, "Magnetic Resonance Image Segmentation Using Pattern Recognition, and Applied to Image Registration and Quantitation," NMR in Biomedicine, vol. 11, pp. 157-167, 1998.
[35] J. Sun and P. Qiu, "Jump Detection in Regression Surfaces Using Both First-Order and Second-Order Derivatives," J. Computational and Graphical Statistics, vol. 16, no. 2, pp. 289-311, 2007.
[36] R. Szeliski and J. Coughlan, "Spline-Based Image Registration," Int'l Computer Vision, vol. 22, no. 3, pp. 199-218, 1997.
[37] N. Tustison, B. Avants, and J. Gee, "Directly Manipulated Free-Form Deformation Image Registration," IEEE Trans. Image Processing, vol. 18, no. 3, pp. 624-635, Jan. 2009.
[38] Y. Wang and L. Staib, "Physical Model Based Non-Rigid Registration Incorporating Statistical Shape Information," Medical Image Analysis, vol. 4, no. 1, pp. 7-20, 2000.
[39] G. Wu, F. Qi, and D. Shen, "Learning-Based Deformable Registration of MR Brain Images," IEEE Trans. Medical Imaging, vol. 25, no. 9, pp. 1145-1157, Sept. 2006.
[40] B. Zitova and J. Flusser, "Image Registration Methods: A Survey," Image and Vision Computing, vol. 21, pp. 977-1000, 2003.
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