4D Warping for Analysing Morphological Changes in Seed Development of Barley Grains

NMR imaging allows to obtain 3D-images by non-invasive treatment of biological structures. In this study intensity-based warping is evaluated by comparing it to landmark-based warping for a four-dimensional analysis of morphological changes in seed development of barley. The datasets of barley grains are obtained at certain development stages by NMR. Warping algorithms reconstruct intermediate physically nonmeasured stages. The landmark-based procedure consists of automatic definition of landmarks and subsequent distance-weighted warping. The intensity-based approach uses iterative intensity-based warping for definition of the displacement vector field and distance-weighted volume warping for generation of the virtual intermediate dataset. The approaches were tested with four datasets of barley at different development stages. As a result, the intensity-based approach is highly applicable for analysis of morphological changes in NMR datasets and serves as a tool for an extensive 4D analysis of seed development in barley grains.

[1]  Gary E. Christensen,et al.  Consistent landmark and intensity-based image registration , 2002, IEEE Transactions on Medical Imaging.

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

[3]  Paul M. Thompson,et al.  A framework for computational anatomy , 2002 .

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

[5]  G. Subsol.  Crest Lines for Curve-Based Warping , 1999 .

[6]  A. Toga,et al.  Brain Warping Via Landmark Points and Curves with a Level Set Representation , 2004 .

[7]  Karl J. Friston,et al.  High-Dimensional Image Registration Using Symmetric Priors , 1999, NeuroImage.

[8]  S. Glidewell,et al.  NMR imaging of developing barley grains , 2006 .

[9]  C. Davatzikos.  Spatial normalization of 3D brain images using deformable models. , 1996, Journal of computer assisted tomography.

[10]  Morten Bro-Nielsen,et al.  Fast Fluid Registration of Medical Images , 1996, VBC.

[11]  Hongyu Guo,et al.  Multiple Landmark Warping Using Thin-plate Splines , 2006, IPCV.

[12]  Jean-Philippe Thirion,et al.  Non-rigid matching using demons , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Dinggang Shen,et al.  Measuring temporal morphological changes robustly in brain MR images via 4-dimensional template warping , 2004, NeuroImage.

[14]  Dinggang Shen,et al.  4D image warping for measurement of longitudinal brain changes , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[15]  F. Bookstein.  Thin-plate splines and decomposition of deformation , 1989 .

[16]  Karl Rohr.  On 3D differential operators for detecting point landmarks , 1997, Image Vis. Comput..

[17]  A. Toga,et al.  Temporal dynamics of brain anatomy. , 2003, Annual review of biomedical engineering.

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

[19]  Paul M. Thompson,et al.  Anatomically Driven Strategies for High-Dimensional Brain Image Warping and Pathology Detection , 1999 .

[20]  Michael I. Miller,et al.  Deformable templates using large deformation kinematics , 1996, IEEE Trans. Image Process..

[21]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

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