Perceptual quality assessment for multimodal medical image fusion

Abstract

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.