Disposal And Research Of Registration And Amalgamation For Medical Image

In this paper, the medical image registration is at first deeply researched, and two methods, SUSAN-based image registration and contour-based image registration are discussed in details. SUSAN-based image registration :First,a fast adaptive SUSAN principle which utilizes the local gray-level feature directly, is proposed for detecting the candidate corners. This improved method can detect features, such as corners, edges and intersections, in different contrast image automatically. For detecting the corners on blurry edges, the candidate corners would include some edge points as a result of reducing the detection threshold. These candidate corners, which include true corners, some edge points and a few false points, are arrayed along the boundary trend by the method of edge element. Through these arrayed points, the angles between approximate straight edge lines are calculated to be as the criterion of determining a corner. Those edge points are removed since they have not significant discontinuous changes in the direction of boundary, i. e. the angles of them are not acute enough, and the false corners due to quantization also are removed by our method. After these steps, the true corners are reserved; Then matched corner points are selected through coarse matching and fine matching, based on such corner points pairs, medical images are registered automatically. Contour-based image registration : First, we extract contour of image; then, we determine connection by SVD; At last, images are registered. We also analyzed their features. After image registration, the fusion method is presented. The pixel-gray level fusion and mathematical morphological pyramid fusion methods are introduced. The pixel-gray level fusion is a simple method based gray. Mathematical morphological pyramid fusion is a multi-resolution method. Results show that this method is a good and effective method, and it has broad using prospect. In this paper, many images and a big amount of data have been used to prove the accuracy of our methods. At last, we discuss the further research goal and direction.