Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Least-squares fitting of multiple M-dimensional point sets
The Visual Computer: International Journal of Computer Graphics
Extraction of anatomic structures from medical volumetric images
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Robust surface registration using a gaussian-weighted distance map in PET-CT brain images
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Distance-Intensity for image registration
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Simultaneous rotation and translation fitting of two 3-D point sets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A topology preserving level set method for geometric deformable models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Likelihood maximization approach to image registration
IEEE Transactions on Image Processing
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In this paper, we propose a new image registration technique using two kinds of information known as object shapes and voxel intensities. The proposed approach consists of two registration steps. First, an initial registration is carried out for two volume images by applying Procrustes analysis theory to the two sets of 3D feature points representing object shapes. During this first stage, a volume image is segmented by using a geometric deformable model. Then, 3D feature points are extracted from the boundary of a segmented object. We conduct an initial registration by applying Procrustes analysis theory with two sets of 3D feature points. Second, a fine registration is followed by using a new measure based on the entropy of conditional probabilities. Here, to achieve the final registration, we define a modified conditional entropy (MCE) computed from the joint histograms for voxel intensities of two given volume images. By using a two step registration method, we can improve the registration precision. To evaluate the performance of the proposed registration method, we conduct various experiments for our method as well as existing methods based on the mutual information (MI) and maximum likelihood (ML) criteria. We evaluate the precision of MI, ML and MCE-based measurements by comparing their registration traces obtained from magnetic resonance (MR) images and transformed computed tomography (CT) images with respect to x-translation and rotation. The experimental results show that our method has great potential for the registration of a variety of medical images.