The proposed method completed 3D TMJ registration for different physiological structure. The result of this method was accurate, reproducible and not relied on the experience of operators.
The reliability of star sensor in a harsh environment has recently become a research hotspot. In some harsh environment such as plume interference, a large number of false stars can be observed, leading to failure in star identification. In this paper, we propose a false star filtering algorithm, which can be used as a preprocessing algorithm for any existing star identification algorithm. By utilizing the difference between the motion of false stars and true stars, the algorithm performs angular distance tracking and star voting on multiple consecutive frames of star images and achieves false star filtering. The software simulation results show that for the star images containing more than 700 false stars, the algorithm is able to find out all true stars in less than 10 frames, and the success rate of the algorithm remains high when the star sensor rotates at up to 1 • /s. The algorithm is also implemented on an existing star senor and evaluated with star images generated by a dynamic star simulator. The experimental results indicate that with the help of the proposed algorithm, the robustness of a normal star identification algorithm can be significantly improved. INDEX TERMS Star sensor, harsh environment, false star filtering, star identification.
An atlas-based multimodal registration method for 2-dimension images with discrepancy structures was proposed in this paper. Atlas was utilized for complementing the discrepancy structure information in multimodal medical images. The scheme includes three steps: floating image to atlas registration, atlas to reference image registration, and field-based deformation. To evaluate the performance, a frame model, a brain model, and clinical images were employed in registration experiments. We measured the registration performance by the squared sum of intensity differences. Results indicate that this method is robust and performs better than the direct registration for multimodal images with discrepancy structures. We conclude that the proposed method is suitable for multimodal images with discrepancy structures. Graphical Abstract An Atlas-based multimodal registration method schematic diagram.
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