This chapter examines advanced pixel-level fusion techniques that integrate anatomical information from CT or MRI imaging with functional data from PET or SPECT. This approach facilitates the amalgamation of effective frameworks and the generation of enriched images of superior quality. A notable aspect is the decomposition of images into small bands (LL, LH, HL, HH) and the application of an adaptive fusion rule, primarily in the low-frequency LL sub-band, to enhance visual quality. However, this process necessitates complex calculations and parameter tuning to optimize metrics such as PSNR and entropy. The chapter also explores region-based fusion methods employing k-means clustering, quadtree decomposition, and Bézier interpolation, which effectively preserve structural integrity. Clustering techniques are instrumental in identifying pathological conditions or anatomical structures within the fused image by grouping similar pixels or regions. These methods yield robust and reliable segmentation results while addressing boundary delineation challenges. Collectively, these innovations enhance diagnostic accuracy and support complex decision-making.