Currently,the majority of quality assessment algorithms are primarily applied to natural image fusion scenarios,resulting in a lack of dedicated evaluation datasets and quality assessment algorithms for multimodal medical fused images.To address these issues,this paper constructs a subjective dataset of medical images using 17 classical medical image fusion methods and proposes an objective medical image quality assessment method based on color similarity(CS)and information similarity(IS).The CS module is utilized to measure local color distortion,and a background separation module is incorpo-rated into the traditional pooling layer to accommodate the multi-background interference characteristics specific to medical images.Additionally,the IS module is employed to evaluate information distortion by improving the calculation method of image entropy and introducing a filtering module for noise removal.Experimental results demonstrate that the proposed eval-uation method yields better consistency with objective scores from the subjective dataset and aligns more closely with human visual perception.
维普
万方数据