Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods.

中文翻译：

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基于多尺度分解的 CT-MR 神经图像融合使用优化的仿生脉冲神经模型和元启发式优化

多模态医学图像融合在临床诊断中发挥着重要作用，是临床医生的辅助模型。在本文中，使用优化的仿生脉冲前馈神经网络在不同的分解域中提出了计算机断层扫描-磁共振 (CT-MR) 图像融合模型。首先，源图像被分解为基础（低频）和细节（高频）层组件。使用纹理能量度量来融合低频子带，以捕获融合图像中的局部能量、对比度和小边缘。高频系数使用像素激活神经模型获得的激发图与优化参数融合，分别使用三种不同的优化技术，如差分进化、布谷鸟搜索和灰狼优化。在优化模型中，基于合成融合图像的边缘索引计算适应度函数，这有助于提取和保留源 CT 和 MR 图像中可用的锐利边缘。为了验证融合性能，在定量和定性措施以及计算复杂性方面，对所提出的和最先进的方法进行了详细的比较分析。实验结果表明，所提出的方法产生了明显更好的融合图像视觉质量，同时优于现有方法。在定量和定性措施以及计算复杂性方面，对提议的方法和最先进的方法进行了详细的比较分析。实验结果表明，所提出的方法产生了明显更好的融合图像视觉质量，同时优于现有方法。在定量和定性措施以及计算复杂性方面，对提议的方法和最先进的方法进行了详细的比较分析。实验结果表明，所提出的方法产生了明显更好的融合图像视觉质量，同时优于现有方法。