Abstract: Image fusion technology plays an important role in computer-aided diagnosis. Detail extraction and energy preservation are two key issues in image fusion, and the traditional fusion methods address them simultaneously by designing the fusion method. However, it tends to cause information loss or insufficient energy preservation. In view of this, a fusion method was proposed to solve the problems of detail extraction and energy preservation separately. The first part of the method aimed at detail extraction. Firstly, the Non-Subsampled Shearlet Transform (NSST) was used to divide the source image into low-frequency and high-frequency subbands. Then, an improved energy-based fusion rule was used to fuse the low-frequency subbands, and an strategy based on the intuitionistic fuzzy set theory was proposed for the fusion of the high-frequency subbands. Finally, the inverse NSST was employed to reconstruct the image. In the second part, an intensity enhancement method was proposed for energy preservation. The proposed method was verified on 43 groups of images and compared with other eight fusion methods such as Principal Component Analysis (PCA) and Local Laplacian Filtering (LLF). The fusion results on two different categories of medical image fusion (Magnetic Resonance Imaging (MRI) and Positron Emission computed Tomography (PET), MRI and Single-Photon Emission Computed Tomography (SPECT)) show that the proposed method can obtain more competitive performance on both visual quality and objective evaluation indicators including Mutual Information (MI), Spatial Frequency (SF), Q value, Average Gradient (AG), Entropy of Information (EI), and Standard Deviation (SD), and can improve the quality of medical image fusion.

Key words: medical image fusion, Non-Subsampled Shearlet Transform (NSST), energy-based fusion rule, intuitionistic fuzzy set theory, intensity enhancement

摘要： 图像融合技术在计算机辅助诊断中发挥了重要作用。传统融合方法通过设计融合策略来同时解决图像融合中的两个关键问题，即细节提取和能量保存，而这容易造成信息丢失或能量保存度不足。鉴于此，提出了一种对细节提取和能量保存问题进行分别解决的融合方法。该方法的第一部分旨在进行细节提取，首先，使用非下采样剪切波变换（NSST）将源图像分解成低频和高频子带；然后，通过改进的能量策略来融合低频子带，而对于高频子带的融合，提出了一种基于直觉模糊集理论的策略；最后，利用逆NSST来重构图像。而在第二部分里，为了达成能量保存，提出了一种亮度增强方法。在43组图像上验证该方法的性能，并把该方法和主成分分析（PCA）、局部拉普拉斯滤波器（LLF）等其他八种传统融合方法进行对比，两种医学图像融合类型（核磁共振图像（MRI）和正电子发射断层图像（PET）、核磁共振图像（MRI）和单光子发射计算机断层图像（SPECT））的实验结果表明，该方法在视觉质量和互信息（MI）、空间频率（SF）、Q值、平均梯度（AG）、信息熵（EI）和标准差（SD）等客观评价指标上均具有优势，能够提高医学图像融合质量。

关键词: 医学图像融合, 非下采样剪切波变换, 能量策略, 直觉模糊集理论, 亮度增强