Medical Image Fusion Is The Process Of Combining Images From Different Modalities To Make The Fused Image More Informative Than Any Of The Source Images. Images Of Different Modalities Include PET, CT, SPECT And MRI.Medical Image Fusion Can Combine Information From Multi-modality Images And Express Them Through A Single Image. How To Design A Fusion Method To Preserve More Information Becomes A Hot Topic. In This Paper, We Propose A Novel Multi-modality Medical Image Fusion Method Based On Synchronized-Anisotropic Diffusion Equation (S-ADE). First, The Modified S-ADE Model Which Is More Suitable For Magnetic Resonance Imaging (MRI) And Computed Tomography (CT) Images Is Employed To Decompose Two Source Images. We Get The Base Layers And Texture Layers. Next, The “Maximum Absolute Value” Rule Is Used For Base Layers Fusion. One Input Image Will Have High Spatial Resolution And Low Spectral Information And Another Image Will Have High Spectral Resolution And Vice Versa. The Aim Of Medical Image Fusion Is To Have A Single Image Having Both Spatial And Spectral Resolution. Most Commonly Used Transform Domain Methods Like Curvelet Transform Is Applied To Extract More Specific Information From The Source Images. Experimental Results Demonstrate That Fused Image Will Have Sharpened Image Resolution And The Fusion Performance Is Evaluated With Image Quality Assessment Metrics.
