Image fusion is a process of combining two or more images into an image. It can extract features from source images, and provide more information than one image can. In this research, we propose a novel method for multimodality medical image fusion. Low spatial resolution limits the diagnostic potential of brain positron emission tomography (PET) imaging. As a possible remedy for this problem we propose a technique for the fusion of PET and MR images, which requires for a given patient the PET data and the T1- weighted MR image. Basically, after the registration steps, the high-frequency part of the MR, which would be unrecoverable by the set PET acquisition system is extracted and added to the PET image. This paper introduces new application of the human vision system model in multispectral medical image fusion. The methodological approaches proposed in this paper result in merged images with improved quality with respect to those obtained by HSI, DWT, wavelet à trous algorithm and wavelet based sharpening methods. Results show proposed method preserves more spectral features with less spatial distortion.