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Block-matching based multimodal medical image fusion via PCNN with SML

Multimodal medical image fusion has become a powerful tool for medical diagnoses. To further enhance its effect, we adopt a new approach similar with the block-matching and 3D filtering algorithm (BM3D) for image de-noising. The algorithm first block the input images and group the blocks into 3D arrays by the block-matching step before multi-scale transform, and then use a 3D transform which consist of a 2D multi-scale transform (e.g. non-subsampled shearlet transform, NSST) and a 1D transform (e.g. DCT)to obtain the coefficients. An averaging fusion rule is used on the obtained low frequency component; for high-frequency components, the algorithm first calculate the Sum of Modified Laplacian (SML), and then use them to calculate ignition maps by Pulse Coupled Neural Network (PCNN) and decide the fused image's high-frequency coefficients by the maps. The final fused image obtained after the inverse transform by using an aggregation process. Experimental result shows the proposed algorithm has better results than some state-of-the-art methods on both subjective and objective evaluation.