School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China.
Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla 41012, Spain.
Int J Neural Syst. 2021 Jan;31(1):2050050. doi: 10.1142/S0129065720500501. Epub 2020 Aug 18.
Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-modality medical images and proposes a novel image fusion method. Based on two CNP systems with local topology, an image fusion framework in nonsubsampled shearlet transform (NSST) domain is designed, where the two CNP systems are used to control the fusion of low-frequency NSST coefficients. The proposed fusion method is evaluated on 20 pairs of multi-modality medical images and compared with seven previous fusion methods and two deep-learning-based fusion methods. Quantitative and qualitative experimental results demonstrate the advantage of the proposed fusion method in terms of visual quality and fusion performance.
耦合神经网络 P (CNP) 系统是一种最近开发的图灵完备的、分布式和并行计算模型，结合了神经元的尖峰和耦合机制。本文专注于如何将 CNP 系统应用于处理多模态医学图像的融合，并提出了一种新的图像融合方法。基于两个具有局部拓扑的 CNP 系统，设计了一种非下采样剪切波变换 (NSST) 域中的图像融合框架，其中两个 CNP 系统用于控制低频 NSST 系数的融合。在 20 对多模态医学图像上对所提出的融合方法进行了评估，并与七种先前的融合方法和两种基于深度学习的融合方法进行了比较。定量和定性实验结果表明，所提出的融合方法在视觉质量和融合性能方面具有优势。
