A new class of similarity measures for robust image registration
Computer Vision, Graphics, and Image Processing
Robust regression and outlier detection
Robust regression and outlier detection
A survey of image registration techniques
ACM Computing Surveys (CSUR)
3D Symmetry Detection Using The Extended Gaussian Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Normal Brain Dissymmetry of Males and Females in MR Images
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Accurate Robust Symmetry Estimation
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Towards a Better Comprehension of Similarity Measures Used in Medical Image Registration
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
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We present a new symmetry-based method allowing to automatically compute, reorient and recenter the mid-sagittal plane in anatomical and functional 3D images of the brain. Our approach is composed of two steps. At first, the computation of local similarity measures between the two hemispheres of the brain allows to match homologous anatomical structures or functional areas, by way of a block matching procedure. The output is a set of point-to-point correspondences: the centers of homologous blocks. Subsequently, we define the mid-sagittal plane as the one best superposing the points in one side of the brain and their counterparts in the other side by reflective symmetry. The estimation of the parameters characterizing the plane is performed by a least trimmed squares optimization scheme. This robust technique allows normal or abnormal asymmetrical areas to be treated as outliers, and the plane to be mainly computed from the underlying gross symmetry of the brain. We show on a large database of synthetic images that we can obtain a subvoxel accuracy in a CPU time of about 3 minutes, for strongly tilted heads, noisy and biased images. We present results on anatomical (MR, CT), and functional (SPECT and PET) images.