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In landmark-based image registration, estimating the landmark correspondence plays an important role. In this letter, a novel landmark correspondence estimation technique using mean shift algorithm is proposed. Image corner points are detected as landmarks and mean shift iterations are adopted to find the most probable corresponding point positions in two images. Mutual information between intensity of two local regions is computed to eliminate mis-matching points. Multi-level estimation (MLE) technique is proposed to improve the stability of corresponding estimation. Experiments show that the precision in location of correspondence landmarks is exact. The proposed technique is shown to be feasible and rapid in the experiments of various mono-modal medical images.

Multisensor image fusion could improve system performances such as detection, tracking, and identification greatly. In this paper, a long distance target detection approach is presented based on multisensor image features fusion. This method extracts two different features from visual and infrared (IR) image sequences respectively to detect regions of motion information content. Temporal change feature is extracted from the visual image sequence using temporal decomposition based on wavelet, which reflects the dynamical content variation at a pixel at any time. And correlation features between local regions are extracted from IR image sequence to distinguish regions with potential moving targets. All these features are merged into a multi-dimensional space and the support vector machine is trained to select regions that have the potential target at each pixel location. The method is robust and feasible to detect long distance targets in clutter background scene.

Mutual information (MI) based image registration has been found to be quite effective in many medical image applications. However, standard MI hampers the convergence of registration transformation parameters since it contains local maxima. In this paper, a novel registration method is proposed. At first, MI based on edge width matching is computed to avoid great change of joint probability distribution and get less local maxima. Particle swarm optimization (PSO), which combines local search methods with global ones balancing exploration and exploitation, is done to search the optimal registration parameter. PSO has less computational complexity as its complex behavior follows only a few simple rules. It could avoid local maxima and reach global optimal results. This method is applicable to a variety of multimodal images, and suitable to different interpolation methods. Theoretical analysis and experiments show that this method is effective and accurate to register multimodal medical images.