FPGA Implementation of Medical Image Fusion using PCA

Recently, digital image processing techniques have been greatly used in medical applications. Using an image fusion technology built on FPGA, this study analyses medical pictures to find distinct aspects and for clinical diagnostics, Computed Tomography (CT) gives the best information on denser tissue with less defacement. With Magnetic Resonance Imaging (MRI), soft tissue details are both more accurate and distorted. In clinical applications, combining images from multiple equipment has become essential. Image fusion with Effective feature modification of the image was achieved by using the maximum selection fusion method with PCA. The research' findings show that the technique is capable of obtaining crisp images by extracting crucial data from combined photos. Images are transformed into pixel-format files and simulation results are observed using MATLAB. The merged images must be viewed with XPS and VB. When compared to DWT, feature extraction in PCA-based image fusion is remarkable. When compared to DWT architecture, which was implemented on the FPGA, the Quality metrics of PCA have increased by up to 11.7%.