Fast local Laplacian filtering based enhanced medical image fusion using parameter-adaptive PCNN and local features-based fuzzy weighted matrices

Generally, the anatomical CT/MRI modalities exhibit the brain tissue anatomy with a high spatial resolution, where PET/SPECT modalities show the metabolic features with low spatial resolution. Therefore, the integration of these two classes significantly improves several clinical applications and computer-aided diagnoses. In the proposed scheme, a fast local Laplacian filter (FLLF) is first applied to the source images to enhance the edge information and suppress the noise artifacts. Second, the RGB images are converted to YUV color space to separate the Y-component. Then to capture the spatial and spectral features of the source images, the NSST is applied to decompose the input (grayscale and/or Y-component) images into one low (LFS) and several high-frequency subbands (HFS). Third, an improved salience measure and matching factor (SMF) method by the local features-based fuzzy-weighted matrix (FW-SMF) is introduced to fuse the LFS coefficient. Due to the fast convergence with fewer iterations and robust pixels selection procedure, the PA-PCNN model is adopted to fuse the HFS coefficients. Fourth, the final fused image is obtained by applying inverse NSST and YUV format. Visual and statistical analysis performed on various experiments prove that the proposed scheme not only integrates the spatial and texture features details of the source images but also enhances the visual quality and contrast of the fused image compared to the existing state-of-arts.