Recent years have witnessed that the multimodal medical image fusion (MMIF) plays critical roles in clinical diagnostics and treatment. Many MMIF algorithms have been proposed to improve the MMIF images quality. The quality of multimodal medical fused images will significantly affect the results of the clinical diagnosis. However, little work has been designed to evaluate the effectiveness of MMIF algorithms and the quality of MMIF images. To this end, this paper presents a perceptual quality assessment method for MMIF. A MMIF image database (MMIFID) is first built to employ the classical MMIF algorithms, and the subjective experiment is conducted to assess the quality of each fused image. Then, a no-reference objective method is proposed for the perceptual quality evaluation of MMIF images,which uses Pulse Coupled Neural Network (PCNN) in Non-subsampled Contourlet Transform (NSCT). A fused image is decomposed by NSCT into low frequency sub-band (LFS) and high frequency sub-band (HFS). It is used to motivate the PCNN processing, and large firing times are employed to measure LFS and HFS. Finally, two components evaluation results are combined to obtain the overall objective quality score. Experimental results based on the MMIFID indicate that our presented method outperforms the existing image fusion quality evaluation metrics, and it provides a satisfactory correlation with subjective scores, which shows effectiveness in the quality assessment of medical fused images.

近年来，目睹了多模式医学图像融合（MMIF）在临床诊断和治疗中起着至关重要的作用。已经提出了许多MMIF算法来改善MMIF图像质量。多模式医学融合图像的质量将显着影响临床诊断结果。但是，很少有工作可以评估MMIF算法的有效性和MMIF图像的质量。为此，本文提出了一种针对MMIF的感知质量评估方法。首先建立MMIF图像数据库（MMIFID）以采用经典的MMIF算法，然后进行主观实验以评估每个融合图像的质量。然后，提出了一种无参考的客观方法来评估MMIF图像的感知质量，它在非下采样Contourlet变换（NSCT）中使用脉冲耦合神经网络（PCNN）。通过NSCT将融合图像分解为低频子带（LFS）和高频子带（HFS）。它用于激励PCNN处理，并且使用较大的触发时间来测量LFS和HFS。最后，将两个成分的评估结果进行组合以获得总体客观质量得分。基于MMIFID的实验结果表明，我们提出的方法优于现有的图像融合质量评估指标，并且与主观评分之间具有令人满意的相关性，显示了在医学融合图像质量评估中的有效性。它用于激励PCNN处理，并且使用较大的触发时间来测量LFS和HFS。最后，将两个成分的评估结果进行组合以获得总体客观质量得分。基于MMIFID的实验结果表明，我们提出的方法优于现有的图像融合质量评估指标，并且与主观评分之间具有令人满意的相关性，显示了在医学融合图像质量评估中的有效性。它用于激励PCNN处理，并使用较大的触发时间来测量LFS和HFS。最后，将两个成分的评估结果进行组合以获得总体客观质量得分。基于MMIFID的实验结果表明，我们提出的方法优于现有的图像融合质量评估指标，并且与主观评分之间具有令人满意的相关性，显示了在医学融合图像质量评估中的有效性。