Nowadays, Medical imaging modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Single Photon Emission Tomography (SPECT), and Computed Tomography (CT) play a crucial role in clinical diagnosis and treatment planning. The images obtained from each of these modalities contain complementary information of the organ imaged. Image fusion algorithms are employed to bring all of this disparate information together into a single image, allowing doctors to diagnose disorders quickly. This paper proposes a novel technique for the fusion of MRI and PET images based on YUV color space and wavelet transform. Quality assessment based on entropy showed that the method can achieve promising results for medical image fusion. The paper has done a comparative analysis of the fusion of MRI and PET images using different wavelet families at various decomposition levels for the detection of brain tumors as well as Alzheimer’s disease. The quality assessment and visual analysis showed that the Dmey wavelet at decomposition level 3 is optimum for the fusion of MRI and PET images. This paper also compared the results of several fusion rules such as average, maximum, and minimum, finding that the maximum fusion rule outperformed the other two.

Due to the characteristics of color vegetation canopy images which have multiple details and Gaussion noise interference, the adaptive mean filtering (AMF) algorithm is used to perform the denoising experiments on noised images in RGB and YUV color space. Based on the single color characteristics of color vegetation canopy images, a simplified AMF algorithm is proposed in this paper to shorten the overall running time of the denoising algorithm by simplifying the adaptive denoising processing of the component V, which contains less image details. Experimental results show that this method can effectively reduce the running time of the algorithm while maintaining a good denoising effect.