An Overview of Pixel-Level Multi-Modal Medical Image Fusion: Modalities, Methods and Recent Trends

Abstract

Image fusion is a methodology of registering and merging data from manifold sources of the same location taken at different time, from various sensors, or at separate positions. It improves the quality of an image, reduces randomness and redundancy thereby attracting wide variety of application areas. Medical image fusion generates a final fused image which is more descriptive and instructive than the independent source images. It is emerging as a powerful research area as it progresses the clinical accuracy by combining images of different modalities for diagnosis and evaluation of medical problems. Firstly, an extensive survey of image fusion levels and traditional image fusion approaches are presented to give thorough view of the image fusion process. In addition, the merits and demerits of various fusion methods are presented. Then, the existing metrics used to quantify the performance of image fusion results are summarized. Next, the three benchmark datasets are considered for experimental analysis of various image fusion methods and quantitative analysis is done using standard metrics followed by discussion. Eventually, this review paper concludes that although numerous fusion methods outperformed the fusion process but still a lot of challenges are associated in the field of medical imaging. Hence, the technical challenges are highlighted, and the patent paper is concluded giving insight into several future directions in the research field. Hence, efforts are made on summarizing main approaches in image fusion process at pixel level that would produce valuable help to developers and researchers owing to explore this research field.