Hybrid multiscale landmark and deformable image registration.

An image registration technique is presented for the registration of medical images using a hybrid combination of coarse-scale landmark and B-splines deformable registration techniques. The technique is particularly effective for registration problems in which the images to be registered contain large localized deformations. A brief overview of landmark and deformable registration techniques is presented. The hierarchical multiscale image decomposition of E. Tadmor, S. Nezzar, and L. Vese, A multiscale image representation using hierarchical (BV;L(2)) decompositions, Multiscale Modeling and Simulations, vol. 2, no. 4, pp. 554{579, 2004, is reviewed, and an image registration algorithm is developed based on combining the multiscale decomposition with landmark and deformable techniques. Successful registration of medical images is achieved by first obtaining a hierarchical multiscale decomposition of the images and then using landmark-based registration to register the resulting coarse scales. Corresponding bony structure landmarks are easily identified in the coarse scales, which contain only the large shapes and main features of the image. This registration is then fine tuned by using the resulting transformation as the starting point to deformably register the original images with each other using an iterated multiscale B-splines deformable registration technique. The accuracy and efficiency of the hybrid technique is demonstrated with several image registration case studies in two and three dimensions. Additionally, the hybrid technique is shown to be very robust with respect to the location of landmarks and presence of noise.

[1]  S. Frantza,et al.  Validating Point-based MR/CT Registration Based on Semi-automatic Landmark Extraction , 1999 .

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

[3]  L. Xing,et al.  Image interpolation in 4D CT using a BSpline deformable registration model. , 2006, International journal of radiation oncology, biology, physics.

[4]  D A Jaffray,et al.  Accuracy of finite element model-based multi-organ deformable image registration. , 2005, Medical physics.

[5]  Balraj Naren,et al.  Medical Image Registration , 2022 .

[6]  D. Hill,et al.  Non-rigid image registration: theory and practice. , 2004, The British journal of radiology.

[7]  B. Daniel,et al.  Integrating deformable MRI/MRSI and CT image registration into the prostate IMRT treatment planning , 2003 .

[8]  B. Daniel,et al.  Mapping of the prostate in endorectal coil-based MRI/MRSI and CT: a deformable registration and validation study. , 2004, Medical physics.

[9]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[10]  Stanislav Kovacic,et al.  Similarity measures for non-rigid registration , 2001 .

[11]  Jan Modersitzki,et al.  Numerical Methods for Image Registration , 2004 .

[12]  Sung Yong Shin,et al.  Image Metamorphosis with Scattered Feature Constraints , 1996, IEEE Trans. Vis. Comput. Graph..

[13]  Eitan Tadmor,et al.  A Multiscale Image Representation Using Hierarchical (BV, L2 ) Decompositions , 2004, Multiscale Model. Simul..

[14]  Lei Xing,et al.  Multiscale deformable registration of noisy medical images. , 2008, Mathematical biosciences and engineering : MBE.

[15]  Achim Schweikard,et al.  Fully automatic detection of corresponding anatomical landmarks in volume scans of different respiratory state. , 2006, Medical physics.

[16]  T. Mackie,et al.  Fast free-form deformable registration via calculus of variations , 2004, Physics in medicine and biology.

[17]  Karl Rohr,et al.  Landmark-Based Image Analysis , 2001, Computational Imaging and Vision.

[18]  Lei Xing,et al.  Multiscale image registration. , 2006, Mathematical biosciences and engineering : MBE.

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

[20]  Lei Xing,et al.  Image registration with auto-mapped control volumes. , 2006, Medical physics.

[21]  Karl Rohr,et al.  An adaptive irregular grid approach for 3D deformable image registration , 2006, Physics in medicine and biology.

[22]  Sung Yong Shin,et al.  Scattered Data Interpolation with Multilevel B-Splines , 1997, IEEE Trans. Vis. Comput. Graph..

[23]  K. Paulsen,et al.  Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. , 1998, Neurosurgery.

[24]  Li Yuan-yuan A SURVEY OF MEDICAL IMAGE REGISTRATION , 2006 .

[25]  Ron Kikinis,et al.  Tracking Volumetric Brain Deformation During Image Guided Neurosurgery , 2001 .

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

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