Wavelet-Based Signal Processing Techniques for Medical Image Fusion

This book is about how wavelet-based signal processing can be used to fuse multiple medical images from different scanning technologies into one single, information-rich image that helps physical doctors make faster and more accurate diagnoses. It explains how tools like MRI, CT, PET, and Ultrasound each reveal different aspects of the human body. Still, none of them alone tells the complete clinical story, and how wavelet mathematics solves this by decomposing each image into frequency components, merging the most diagnostically valuable information from each source, and reconstructing one unified image that contains everything the physician needs to see. The book covers walks through the complete image fusion pipeline from preprocessing and registration through decomposition and fusion rules to final image reconstruction, and then applies these techniques to real clinical scenarios, including brain tumor detection, cardiac disease, Alzheimer's diagnosis, orthopedic injury, and cancer radiation planning. It also presents objective quality metrics like PSNR, SSIM, and Petrovec algorithm.