Medical Image Registration and Fusion Using Principal Component Analysis

Principal Component Analysis (PCA) is widely used in the field of medical image processing. In this paper, PCA is applied to align and fuse the images. When alignment, first, the centroids of the static and moving images are derived by computing the image moments and taken as the translation values for registration, then the subtraction of two rotation angles produced by using PCA to solve the covariance matrice of image coordinates is counted as the rotation values for registration, finally the moving image is aligned with the static one. The closest iterative point (ICP) algorithm exists some problems which worth improving. Therefore, we combine PCA with ICP to align the images in this paper. The translation and rotation values derived by PCA are views as the initial request parameters of ICP, which is conducive to further advancing the registration accuracy. The experimental results show that the combination method has a fairly simple implementation, low computational load, good registration accuracy, and also can efficiently avoid trapping in the local optima. When fusion, a slipping window with size being is first moved across the fusing images to construct sub-block with size also being , then the eigenvectors of the covariance matrix created by using PCA to each sub-block are acquired, finally the absolute values of the eigenvectors are added to compute the fusion coefficient of the central pixel of each sub-block and the images are fused. The results reveal that this proposed fusion method is superior to the traditional PCA-based image fusion.