Multimodality medical image fusion algorithm based on gradient minimization smoothing filter and pulse coupled neural network

We propose a novel multimodality medical image fusion algorithm which involves L₀ gradient minimization smoothing filter (GMSF) and pulse coupled neural network (PCNN). Firstly, an excellent multi-scale edge-preserving decomposition framework based on GMSF is proposed to decompose each source image into one base image and a series of detail images. For extracting and preserving more salient features and detail information, different fusion rules are designed to fuse the separated subimages. The base images are fused using the regional weighted sum of pixel energy and gradient energy, and a biologically inspired feedback neural network is used to fuse the detail images. The final fused image is obtained by synthesizing the fused base image and detail images. Experimental results on several datasets of CT and MRI images show that the proposed algorithm outperforms other compared methods in terms of both subjective and objective assessment.