Based on research of principal component analysis, the principal component analysis is introduced to medical image fusion. The K-L transform is used to multi-mode images. Then a new matrix is composed. A eigenvector which accounts for above 90 percent in contribution of variance about the new matrix is adopted to obtain principal components. Using principal components can carry on image fusion. The result indicates that the method has many characters, such as fast execution, great information entropy and broad dynamic range.
Photoacoustic imaging is a new non-destructive biomedical imaging method. When limited independent data is available, the restoration of the initial pressure rise distribution is often an ill-posed problem. In this paper, based on the study of photoacoustic effects, the sparse prior information of photoacoustic images is integrated into the reconstruction process by using the compressed sensing (CS) theory and the L 2 norm optimization technique, combining the augmented Langrange weighting of the alternating direction method of multipliers (ADMM) with the total variation (TV) minimization problem, and the reconstruction artifacts are effectively eliminated. The simulation data from the real numerical model show that compared with the common time reversal algorithm, interpolation algorithm and truncated back projection algorithm, the total variational regularization method based on ADMM can effectively improve the quality of reconstructed images under the condition of limited viewing angles and incomplete projection data.INDEX TERMS photoacoustic imaging, alternating direction method of multipliers, total variation, image reconstruction.
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