Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Digital Image Processing
IEEE Computer Graphics and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D brain surface matching based on geodesics and local geometry
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Efficient partial-surface registration for 3D objects
Computer Vision and Image Understanding
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
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
Likelihood maximization approach to image registration
IEEE Transactions on Image Processing
Robust and fast shell registration in PET and MR/CT brain images
Computers in Biology and Medicine
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Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images may cause misalignments, particularly in brain PET and CT images that have low correspondence rates between features due to differences in image characteristics. To cope with this limitation, we propose a robust feature-based registration technique using a Gaussian-weighted distance map (GWDM) that finds the best alignment of feature points even when features of two images are mismatched. A GWDM is generated by propagating the value of the Gaussian-weighted mask from feature points of CT images and leads the feature points of PET images to be aligned on an optimal location even though there is a localization error between feature points extracted from PET and CT images. Feature points are extracted from two images by our automatic brain segmentation method. In our experiments, simulated and clinical data sets were used to compare our method with conventional methods such as normalized mutual information (NMI)-based registration and chamfer matching in accuracy, robustness, and computational time. Experimental results showed that our method aligned the images robustly even in cases where conventional methods failed to find optimal locations. In addition, the accuracy of our method was comparable to that of the NMI-based registration method.