Elastic Medical Image Registration Based on Image Intensity

An automatic elastic medical image registration approach is proposed, based on image intensity. The algorithm is divided into two steps. In Step 1, global affine registration is first used to establish an initial guess and the resulting images can be assumed to have only small local elastic deformations. The mapped images are then used as inputs in Step 2, during which, the study image is modeled as elastic sheet by being divided into sub-images. Moving the individual sub-image in the reference image, the local displacement vectors are found and the global elastic transformation is achieved by assimilating all of the local transformation into a continuous transformation. The algorithm has been validated by simulated data, noisy data and clinical tomographic data. Both experiments and theoretical analysis have demonstrated that the proposed algorithm has a superior computational performance and can register images automatically with an improved accuracy.

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

[2]  D. Hill,et al.  Medical image registration , 2001, Physics in medicine and biology.

[3]  Christian Roux,et al.  Genetic algorithms for a robust 3-D MR-CT registration , 2000, IEEE Transactions on Information Technology in Biomedicine.

[4]  R. Maciunas,et al.  Interactive image-guided neurosurgery , 1992, IEEE Transactions on Biomedical Engineering.

[5]  Paul M. Thompson,et al.  A surface-based technique for warping three-dimensional images of the brain , 1996, IEEE Trans. Medical Imaging.

[6]  Scott T. Grafton,et al.  Automated image registration: I. General methods and intrasubject, intramodality validation. , 1998, Journal of computer assisted tomography.

[7]  Ma Bin-rong,et al.  A review of medical image registration , 1999 .

[8]  Nicholas Ayache,et al.  A scheme for automatically building three-dimensional morphometric anatomical atlases: application to a skull atlas , 1998, Medical Image Anal..

[9]  J. Mazziotta,et al.  MRI‐PET Registration with Automated Algorithm , 1993, Journal of computer assisted tomography.

[10]  Guy Marchal,et al.  Automated Multimodality Medical Images Registration using Information Theory , 1995 .

[11]  Martin J. Yaffe,et al.  Biomechanical 3-D finite element modeling of the human breast using MRI data , 2001, IEEE Transactions on Medical Imaging.

[12]  J. F. Bradshaw,et al.  The principal axes transformation--a method for image registration. , 1990, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[13]  B. Ardekani,et al.  A Fully Automatic Multimodality Image Registration Algorithm , 1995, Journal of computer assisted tomography.

[14]  Max A. Viergever,et al.  Mutual information matching in multiresolution contexts , 2001, Image Vis. Comput..

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

[16]  Gary E. Christensen,et al.  Consistent image registration , 2001, IEEE Transactions on Medical Imaging.

[17]  J. Hajnal,et al.  Detection of Subtle Brain Changes Using Subvoxel Registration and Subtraction of Serial MR Images , 1995, Journal of computer assisted tomography.

[18]  Simon R. Arridge,et al.  A survey of hierarchical non-linear medical image registration , 1999, Pattern Recognit..

[19]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[20]  Michael I. Miller,et al.  Individualizing Neuroanatomic Atlases Using a Massively Parallel Computer , 1996, Computer.

[21]  Patrick Clarysse,et al.  A review of cardiac image registration methods , 2002, IEEE Transactions on Medical Imaging.

[22]  Ruzena Bajcsy,et al.  Multiresolution elastic matching , 1989, Comput. Vis. Graph. Image Process..

[23]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  M. J. D. Powell,et al.  An efficient method for finding the minimum of a function of several variables without calculating derivatives , 1964, Comput. J..

[25]  Max A. Viergever,et al.  Evaluation of Ridge Seeking Operators for Multimodality Medical Image Matching , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  M. Viergever,et al.  Medical image matching-a review with classification , 1993, IEEE Engineering in Medicine and Biology Magazine.

[27]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[28]  M. Fitzpatrick.  A Review of Medical Image Registration , 1993 .

[29]  J. Mazziotta,et al.  Automated image registration , 1993 .

[30]  C. Pelizzari,et al.  Accurate Three‐Dimensional Registration of CT, PET, and/or MR Images of the Brain , 1989, Journal of computer assisted tomography.

[31]  D. Feng,et al.  Objective assessment of image registration results using statistical confidence intervals , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[32]  Michael Unser,et al.  Optimization of mutual information for multiresolution image registration , 2000, IEEE Trans. Image Process..

[33]  Michael Unser,et al.  Unwarping of unidirectionally distorted EPI images , 2000, IEEE Transactions on Medical Imaging.

[34]  A Collignon,et al.  Automated multimodality image registration using information theory , 1995 .

[35]  Sung-Cheng Huang,et al.  A general technique for interstudy registration of multifunction and multimodality images , 1994 .

[36]  Karl Rohr,et al.  Elastic Registration of Multimodal Medical Images: A Survey , 2000, Künstliche Intell..

[37]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..