The precision of the image acquire using medical imaging techniques is crucial to the effective diagnosis of a disease. Since medical image fusion is a “life saving tool,” it has become a more interesting area of study in the past few years. To obtain a high-resolution image incorporating additional information for diagnosis is the aim of medical imaging. A type of fusion approach for multiple mediums medical pictures is proposed in this research. There are a couple of methodologies: “Anatomy,” that offers specifics on the functional aspects of an organ’s cell activity using techniques like PET and SPECT. Function devoid structure is like a ghost, while structure with no function is like a corpse. Consequently, an investigation is conducted on the Anatomy, Physiology, and Metabolism photos. In other words, this technique fuses CT and PET scans. Its specific goal is to collect pertinent, inconsistent, and complementing data in a single order in order to improve the data sent by the images and increase the interpretive accuracy. More usability and more reliable data result from this. Furthermore, it has been said that combined data offers strong performance advantages, including enhanced dependability, less ambiguity, and higher confidence. In order to overcome their shortcomings and improve image processing qualities, this paper proposed a pixel-level based “Hybrid Concept” that combines traditional and advanced fusion approaches. Examples of these techniques include Principal Component Analysis (PCA) and Discrete Curvelet Transformation (DCT) to create a SIDWT (Shift Invariant Discrete Wavelet Transformation), SIDWT-PCA, SIDWT-DCT, and recommending SIDWT-DCT-PCA. The efficiency matrices MSE, PSNR, ENTROPY, and variance are used to analyze, detect, and contrast the outcomes between them.