针对像素级多模态医学图像融合信息丢失的问题，提出了一种基于非下采样剪切波变换（NSST）的像素相关性分析（PCAS）的图像融合方法。首先，对源图像进行NSST分解，获得高低频子带。然后，利用提出的中心像素方差计算邻域像素与中心像素的强度相关因子，构建邻域像素相关系数矩阵，并提出将相关性拉普拉斯能量和作为高频方向子带的融合规则。再次，计算低频子带中心像素能量以及邻域像素能量梯度信息，得到低频融合决策图。最后，通过逆变换得到融合结果图像。磁共振图像（MRI）和计算机断层扫描（CT）、单光子发射计算机断层成像（PET）、正电子发射断层成像（SPECT）的脑部图像融合实验结果表明，本文融合方法可以很好地保留源图像的显著信息和纹理细节。

To solve the problem of information loss in pixel-level multimodal medical image fusion, an image fusion method using pixel correlation analysis (PCA) in Non-subsampled Shearlet Transform (NSST) domain is proposed. First, NSST decomposition is performed on the source images to obtain high and low frequency sub-bands. The intensity correlation factor between neighborhood pixels and central pixel is calculated using the proposed center pixel variance, and the correlation coefficient matrix of neighborhood pixels is constructed. The proposed correlation-sum of modified laplacian (C-SML) is used as the fusion rule for high-frequency sub-bands. The energy of the central pixel and the energy gradient information of the neighboring pixels of the low-frequency sub-bands are calculated to obtain the fusion decision map for low-frequency sub-bands. Finally, the fused image is obtained by inverse NSST. The experimental results about magnetic resonance imaging (MRI) and computed tomography (CT), positron emission tomography (PET), single-photon emission computed tomography （SPECT） brain images indicate that the proposed fusion method can well retain the significant information and texture details of the source images.