To solve the problem that classical Mutual Information (MI) image registration may lead to local extremum, a registration method for multispectral magnetic resonance images based on Cross Cumulative Residual Entropy (CCRE) was proposed. Firstly, the gray level of reference and floating images were compressed into 5 and 7 bits. Then the Hanning windowed Sinc interpolation was used to calculate the CCRE of 5-bit grayscale images, and the Brent algorithm was used to search the CCRE to get the initial transformation parameters of pre-registration. Finally, the Partial Volume (PV) interpolation was adopted to calculate the CCRE of 7-bit grayscale images, and the Powell algorithm was applied to optimize the CCRE to get final parameters from the pre-registration parameters. The experimental results show that the robustness of the proposed method is improved compared with the CCRE registration of PV interpolation, while the registration time is saved about 90% and accuracy is improved compared with the CCRE of Hanning windowed Sinc interpolation. The presented method ensures robustness, efficiency and accuracy, so it is suitable for multi-spectral image registration.

To solve the problem of Fine Particulate Matter (PM2.5) concentration prediction, a PM2.5 concentration prediction model was proposed. First, through introducing the comprehensive meteorological index, the factors of wind, humidity, temperature were comprehensively considered; then the feature vector was conducted by combining the actual concentration of SO2, NO2, CO and PM10; finally the Least Squares Support Vector Machine (LS-SVM) prediction model was built based on feature vector and PM2.5 concentration data. The experimental results using the data from the city A and city B environmental monitoring centers in 2013 show that, the forecast accuracy is improved after the introduction of a comprehensive weather index, error is reduced by nearly 30%. The proposed model can more accurately predict the PM2.5 concentration and it has a high generalization ability. Furthermore, the author analyzed the relationship between PM2.5 concentration and the rate of hospitalization, hospital outpatient service amount, and found a high correlation between them.

Since the isotropic diffusion will easily blur edge features,and coherence-enhancing diffusion will produce pseudo striations in the background regions during the denoising process, a weighted diffusion algorithm was proposed to reduce the Rician noise of Magnetic Resonance Imaging (MRI) image according to the distribution of noise. A threshold value was calculated by the Rician noise variance in the background region of MRI image, which might be used to distinguish the image background and the edge of Region-Of-Interest (ROI). A weighting function combining the isotropic diffusion and the coherence-enhancing diffusion based on the calculated value was constructed. The constructed function could adaptively adjust the weight values of two kinds of diffusion in different structural regions in order to give full play to the advantages while overcoming the disadvantages of the above two kinds of diffusion.The experimental results show that it is better than some classical diffusion algorithms in Peak Signal-to-Noise Ratio (PSNR) and Mean Structural Similarity(MSSIM).Thus, it has better performance on noise reduction and edge preservation or enhancement.

Multiple Sclerosis (MS) is a chronic disease that affects the central nervous system and MS lesions are visible in conventional Magnetic Resonance Imaging (cMRI). A new method for the automatic segmentation of MS White Matter Lesions (WML) on cMRI was presented, which enabled the efficient processing of images. Firstly the Kernel Fuzzy C-Means (KFCM) clustering was applied to the preprocessed T1-weight (T1-w) image for extracting the white matter image. Then region growing algorithm was applied to the white matter image to make a binary mask. This binary mask was then superimposed on the corresponding T2-weight (T2-w) image to yield a masked image only containing white matter, lesions and background. The KFCM was reapplied to the masked image to obtain WML. The testing results show that the proposed method is able to segment WML on simulated images of low noise quickly and effectively. The average Dice similarity coefficient of segmentation result is above 80%.