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.