Signal Processing for Computer Vision
Signal Processing for Computer Vision
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Multi-modal Volume Registration Using Joint Intensity Distributions
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
A Level-Set Based Approach to Image Registration
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Fast Non-rigid Multimodal Image Registration Using Local Frequency Maps
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
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Fusing of multi-modal data involves automatically estimating the coordinate transformation required to align the data sets. Most existing methods in literature are not robust and fast enough for practical use. We propose a robust algorithm, based on matching local-frequency image representations, which naturally allow for processing the data at different scales/resolutions, a very desirable property from a computational efficiency view point. This algorithm involves minimizing - over all affine transformations - the integral of the squared error (ISE or L2E) between a Gaussian model of the residual and its true density function. The residual here refers to the difference between the local frequency representations of the transformed (by an unknown transformation) source and target data. The primary advantage of our algorithm is its ability to cope with large non-overlapping fields of view of the two data sets being registered, a common occurrence in practise. We present implementation results for misalignments between CTan d MR brain scans.