Computational geometry: an introduction
Computational geometry: an introduction
Multiresolution elastic matching
Computer Vision, Graphics, and Image Processing
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Rigid, affine and locally affine registration of free-form surfaces
International Journal of Computer Vision
3D-2D projective registration of free-form curves and surfaces
Computer Vision and Image Understanding
Adaptive Segmentation of MRI Data
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Automatic Retrieval of Anatomical Structures in 3D Medical Images
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Auxiliary variables for deformable models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
3D brain surface matching based on geodesics and local geometry
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Registration of 3D Points Using Geometric Algebra and Tensor Voting
International Journal of Computer Vision
Registration of 2D Points Using Geometric Algebra and Tensor Voting
Journal of Mathematical Imaging and Vision
| Hi-index | 0.00 |
We present in this paper a new registration and gain correction algorithm for 3D medical images. It is intensity based. The basic idea is to represent the images 4D points (Xj, Yj, Zj, Ij) and to define a global energy function based on this representation. For minimization, we propose a technique which does not require to compute the derivatives of this criterion with respect to the parameters. It can be understood as an extension of the Iterative Closest Point algorithm, or as an application of the formalism proposed in [9]. Two parameters allow us to have a coarse to fine strategy both for resolution and deformation. Our technique presents the advantage to minimize a well defined global criterion, to deal with various classes of transformations (for example rigid, affine and volume spline), to be simple to implement and to be efficient in practice. Results on real brain and heart 3D images are presented to demonstrate the validity of our approach.