Elastic matching of diffusion tensor images
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Diffeomorphic Matching of Diffusion Tensor Images
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Affine coregistration of diffusion tensor magnetic resonance images using mutual information
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Affine registration of diffusion tensor MR images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Algebraic methods for direct and feature based registration of diffusion tensor images
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Saturn: A software application of tensor utilities for research in neuroimaging
Computer Methods and Programs in Biomedicine
Adaptive noise filtering for accurate and precise iffusion estimation in fiber crossings
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
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Registration algorithms are usually based on measures that must be optimized, whose choice has a great influence in the final result. For diffusion tensor imaging, scalar measures cannot be directly applied, and therefore new cost functions must be defined, regarding the special features of these data. We present a new pointwise similarity measure, named diffusion type based (DTB), that considers the physical meaning of the diffusion tensor. Theoretically, it is proved that DTB corrects the drawbacks of previous analogous measures, as well as it provides a more realistic result than generic measures. These conclusions are corroborated by experiments, where registration algorithms are driven by DTB and other broadly used measures. It is shown both quantitatively and visually that DTB leads to better results, and is more robust in presence of noise.