Multimodal medical image fusion based on the spectral total variation and local structural patch measurement

摘要

Masses of research decompose the image into different levels of feature maps, but the structures and edges may not appropriately separated. This may cause the loss of image detail in the fusion process. Therefore, we design a robust method for multimodal medical image fusion using spectral total variation transform (STVT). In our method, the source images are first decomposed into a series of texture signatures (referred to as deviation components) and base components via STVT algorithm. Then combine the local structural patch measurement (LSPM) to fuse the deviation components, and the base components are merged using a spatial frequency (SF) dual-channel spiking cortical model (SF-DCSCM), in which the SF of base components are regarded as stimulus to activate DCSCM. Finally, the final image is reconstructed by the inverse STVT with the restored images together. Experimental results suggest that proposed scheme achieves promising results, and more competitiveness against some state-of-the-art methods.