MRI and PET image fusion by combining IHS and retina-inspired models

Image fusion has become a widely used tool for increasing the interpretation quality of images in medical applications. The acquired data might exhibit either good functional characteristic (such as PET) or high spatial resolution (such as MRI). The MRI image shows the brain tissue anatomy and contains no functional information. The PET image indicates the brain function and has a low spatial resolution. Hence, the image fusion task is carried out to enhance the spatial resolution of the functional images by combining them with a high-resolution anatomic image. A perfect fusion process preserves the original functional characteristics and adds spatial characteristics to the image with no spatial distortion. The intensity-hue-saturation (IHS) algorithm and the retina-inspired model (RIM) fusion technique can preserve more spatial feature and more functional information content, respectively. The presented algorithm integrates the advantages of both IHS and RIM fusion methods to improve the functional and spatial information content. Visual and statistical analyses show that the proposed algorithm significantly improves the fusion quality in terms of: entropy, mutual information, discrepancy, and average gradient; compared to fusion methods including, IHS, Brovey, discrete wavelet transform (DWT), à-trous wavelet and RIM.