ABSTRACT
With the growing demand for clinic applications, the study of multimodality medical image fusion has gotten a lot of interest in recent years. The complementing data in images of several modalities, for example, are frequently beneficial to a radiotherapy strategy. The computed tomography (CT) information is used to calculate the dose, but the equivalent magnetic resonance (MR) scan is often superior for tumor delineation. CT gives the finest data on denser tissue with a smaller amount of distortion, MRI offers improved data on soft tissue with greater distortion, and positron emission tomography (PET) delivers improved information on flood movement and blood flow with a low resolution for medical analysis. The quality of fused images improves significantly when image fusion is based on multimodal medical imaging. Without introducing any defects or unwanted distortions, an excellent image fusion technique creates output images by maintaining all of the feasible and prominent data obtained from the basis images. The structural standard deviation, root-mean-square error, entropy, correlation coefficient, similarity index, edge detection, average gradient, high pass correlation, peak signal-to-noise ratio, and other performance measures are used to measure images for prospective assessment and image fusion. This review chapter presents a realistic citation of approaches as well as a summary of the broad scientific challenges that medical image fusion faces. Despite numerous unrestricted scientific and technological challenges, this review concludes that the fusion of medical imaging has proven to be beneficial for improving the medical dependability of using medical imaging for medical analysis and diagnostics and is a technical discipline with the potential to grow meaningfully in the coming era. There is still a need for a comprehensive review and bibliometric analysis of the most recent approaches in various fusion scenarios. The review concludes with several open challenges for researchers. As a result, the descriptive analysis presented in this work will serve as a catalyst for inspiring and nurturing advanced image fusion research ideas.
