Robust spiking cortical model and total-variational decomposition for multimodal medical image fusion

Abstract

In field of medical image, a large number of related works demonstrated that the decomposition theories (such as multi-scale transform (MST) techniques) have better performance in preserving details and structures information. However, the most of hierarchical structure-based methods fail to appropriately separate energy and detailed information, they even produce some unsatisfied effects such as low-contrast resolution and anamorphose. To address the above issues, we present a modified image decomposition algorithm to get a good trade-off between speed and performance. Specifically, it utilizes the total-variational transform into moving least squares method (TV-MLS), which can make the result more robust to noise and fully reserve the dominant structure. Firstly, we perform total-variational decomposition on the source images, and yield a series of detail layers and base layers. Next, the layers are fused using the robust adaptive dual-channel spiking cortical model (RA-DCSCM), CNNs and the fine-designed fusion rule. Eventually, we provide extensive qualitative analysis for the proposed fusion scheme in both subjective visual inspection and quality metrics to verify that our method is more competitive against other existing methods.