Two-scale image decomposition based image fusion using structure tensor

The PET/SPECT image generates functional information of the human brain. In the field of multimodal medical image fusion, the decomposition scheme of PET/SPECT image in pseudo-color is thought to be negligible. In this article, a two-step model for PET/SPECT image decomposition is proposed to achieve contrast enhancement that inherently captures gradient to distinguish individual edge information from detail information. First, sharp structure is preserved using structure tensor on the intensity component of PET/SPECT image. Second, sharp structure in gray is transformed back into the image in pseudo-color by generalized intensity-hue-saturation, one of the color space methods. The combination of structure tensor and generalized intensity-hue-saturation based fusion technique can preserve not only more intensity information but also functional information content. Finally, we demonstrate the effectiveness of our proposed decomposition method in the context of PET/SPECT image and then make a comparison of fusion result by two-scale image fusion method in terms of the full-reference objective metric structural similarity and no-reference objective metric natural image quality evaluator.