Robust regression and outlier detection
Robust regression and outlier detection
A Comparison of Simularity Measures for use in 2D-3D Medical Image Registration
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
| Hi-index | 0.00 |
A major source of error in the analysis of functional Magnetic Resonance images is the presence of spurious activation arising on account of patient head movement at the time of image acquisition. This makes it imperative for the images to be subjected to motion correction through registration. A number of solutions to the problem currently do exist though there is always the need for faster approaches which produce better estimates of the motion parameters. In this paper, we propose a signal model for fMRI images with possible relative movement between scans and show how the least trimmed squaures estimator, which is well-known in the statistical literature for its robustness, can be used. Since data obtained from actual fMRI studies do not provide the "correct" values of the motion parameters with which the performances of various estimators may be compared, computer simulations are set up where these parameters may be controlled. Our simulations indicate that the proposed method produces smaller errot in the estimated motion parameters. when compared to the existing estimators, including another robust estimator.