Intensity-based image registration using robust correlation coefficients

The ordinary sample correlation coefficient is a popular similarity measure for aligning images from the same or similar modalities. However, this measure can be sensitive to the presence of "outlier" objects that appear in one image but not the other, such as surgical instruments, the patient table, etc., which can lead to biased registrations. This paper describes an intensity-based image registration technique that uses a robust correlation coefficient as a similarity measure. Relative to the ordinary sample correlation coefficient, the proposed similarity measure reduces the influence of outliers. We also compared the performance of the proposed method with the mutual information-based method. The robust correlation-based method should be useful for image registration in radiotherapy (KeV to MeV X-ray images) and image-guided surgery applications. We have investigated the properties of the proposed method by theoretical analysis, computer simulations, a phantom experiment, and with functional magnetic resonance imaging (MRI) data.

[1]  H. Cramér.  Mathematical methods of statistics , 1947 .

[2]  Jean-Francois Mangin,et al.  What is the best similarity measure for motion correction in fMRI time series? , 2002, IEEE Transactions on Medical Imaging.

[3]  A L Boyer,et al.  An image correlation procedure for digitally reconstructed radiographs and electronic portal images. , 1995, International journal of radiation oncology, biology, physics.

[4]  M. Murphy.  An automatic six-degree-of-freedom image registration algorithm for image-guided frameless stereotaxic radiosurgery. , 1997, Medical physics.

[5]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[6]  D. Titterington.  Estimation of Correlation Coefficients by Ellipsoidal Trimming , 1978 .

[7]  D. R. Fish,et al.  A patient-to-computed-tomography image registration method based on digitally reconstructed radiographs. , 1994, Medical physics.

[8]  N. Campbell.  Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation , 1980 .

[9]  J Kim,et al.  A feasibility study of mutual information based setup error estimation for radiotherapy. , 2001, Medical physics.

[10]  Alfred O. Hero,et al.  Lower bounds for parametric estimation with constraints , 1990, IEEE Trans. Inf. Theory.

[11]  Paul Suetens,et al.  Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information , 1999, Medical Image Anal..

[12]  L Dong,et al.  A portal image alignment and patient setup verification procedure using moments and correlation techniques. , 1996, Physics in medicine and biology.

[13]  Fabrice Heitz,et al.  Registration of MR/MR and MR/SPECT Brain Images by Fast Stochastic Optimization of Robust Voxel Similarity Measures , 1998, NeuroImage.

[14]  David J. Hawkes,et al.  Voxel Similarity Measures for 3D Serial MR Brain Image Registration , 2000, IEEE Trans. Medical Imaging.

[15]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[16]  Jay B. West,et al.  Predicting error in rigid-body point-based registration , 1998, IEEE Transactions on Medical Imaging.

[17]  Jeffrey A. Fessler.  Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): applications to tomography , 1996, IEEE Trans. Image Process..

[18]  R. Viswanathan,et al.  An introduction to statistical signal processing with applications , 1979 .

[19]  D J Heeger,et al.  Robust multiresolution alignment of MRI brain volumes , 2000, Magnetic resonance in medicine.

[20]  A. Hero,et al.  Image registration using entropic graph-matching criteria , 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002..

[21]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[22]  G. Marchal,et al.  Multi-modal volume registration by maximization of mutual information , 1997 .

[23]  David J. Hawkes,et al.  Voxel similarity measures for 3-D serial MR brain image registration , 1999, IEEE Transactions on Medical Imaging.

[24]  Jeongtae Kim.  Intensity based image registration using robust similarity measure and constrained optimization: Applications for radiation therapy. , 2004 .

[25]  Jürgen Weese,et al.  Voxel-based 2-D/3-D registration of fluoroscopy images and CT scans for image-guided surgery , 1997, IEEE Transactions on Information Technology in Biomedicine.

[26]  Charles R. Meyer,et al.  Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations , 1997, Medical Image Anal..

[27]  E. L. Lehmann,et al.  Theory of point estimation , 1950 .

[28]  J. Pauly,et al.  A homogeneity correction method for magnetic resonance imaging with time-varying gradients. , 1991, IEEE transactions on medical imaging.

[29]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[30]  Alfred O. Hero,et al.  Image registration with minimum spanning tree algorithm , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[31]  P. Rousseeuw,et al.  Unmasking Multivariate Outliers and Leverage Points , 1990 .

[32]  Jeffrey A. Fessler,et al.  Image registration using robust correlation , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[33]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.