Variational Methods for Multimodal Image Matching
International Journal of Computer Vision
Tracking brain deformations in time sequences of 3D US images
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Tracking Brain Deformations in Time-Sequences of 3D US Images
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Classification of SPECT Images of Normal Subjects versus Images of Alzheimer's Disease Patients
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Robust Head Pose Estimation Using Textured Polygonal Model with Local Correlation Measure
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Iconic feature based nonrigid registration: the PASHA algorithm
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Multifield-Graphs: An Approach to Visualizing Correlations in Multifield Scalar Data
IEEE Transactions on Visualization and Computer Graphics
Iconic feature registration with sparse wavelet coefficients
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
A unified framework for segmentation-assisted image registration
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
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Non-rigid registration of medical images is usually presented as a physical model driven by forces deriving from a measure of similarity of the images. These forces can be computed using a gradient-descent scheme for simple intensity-based similarity measures. However, for more complex similarity measures, using for instance local statistics, the forces are usually found using a block-matching scheme. In this article, we introduce a Gaussian window scheme, where the local statistics (here the sum of local correlation coefficients) are weighted with Gaussian kernels. We show that the criterion can be deducted easily to obtain forces to guide the registration. Moreover, these forces can be computed very efficiently by global convolutions inside the real image of the Gaussian window in a time independent of the size of the Gaussian window. We also present two minimization strategies by gradient descent to optimize the similarity measure: a linear search and a Gauss-Newton-like scheme. Experiments on synthetic and real 3D data show that the sum of local correlation coefficients optimized using a Gauss-Newton scheme is a fast and accurate method to register images corrupted by a non-uniform bias.