## 4 Conclusions and General Requirements
We divided them into two types: Those which can be exploited in the frequency domain and others used in the spatial domain where the pixels of the input images undergo improvements. In reality, the latter type of technique was preferred in many studies. However, the conventional methods applied for multimodal fusion suffer from several drawbacks and do not meet the requirements of current medical applications. In this context too, hybrid models combining the advantages of conventional techniques have been applied for the purpose of improving the performance of CAD systems based on multimodal fusion. We noticed that the efficiency of the CAD systems proposed by researchers is demonstrated by estimating the percentage of several performance measures, such as sensitivity (SE), which represents the true positive rate; specificity (SP), which estimates the true negative rate; and precision (AC), which determines the proportion of true results in the database, whether true positive or true negative. The authors tried to prove that the proposed methods performed fairly well compared to a selection of other methods. Indeed, we have observed that certain techniques have obvious advantages over others, based on the speed of processing time, or on the lesser complexity or even on the lower memory requirements. However, it is not possible to draw absolute conclusions about what is best or worst without first performing in-depth tests on all the proposed methods. Furthermore, a potential limitation of combining biomarkers is limited by the practical clinical implications as imposed by the medical experts based on the requirements of specific medical studies. In addition to medical reasons, there exists technical challenges in image fusion resulting from image noise, resolution difference between images, inter-image variability between the images, lack of sufficient number of images per modality, high cost of imaging and increased computational complexity with increasing image space and time resolution. Many of these challenges remain open and the problem is much more significant in developing fusion image algorithms for real-time medical applications such as robotic-guided surgery. Nonetheless, even under these challenging situations, the fused images provide the human observers improved viewing and interpretation of medical images; and the image fusion in multimodal medical imaging environment has proved to be useful and the trust in its techniques is on the rise. It is expected that the innovation and practical advancements would continue to grow in the upcoming years. In this context, some general requirements on the application of multimodality fusion that emerge from this survey, especially for fusion algorithms, could be suggested as follows: (1) The algorithm should be able to extract and integrate complimentary features from the input images; (2) it must not introduce artifacts or inconsistencies according to the human visual system; (3) it should be robust and reliable. Generally, these can be evaluated subjectively or objectively. The former relies on human visual characteristics and the specialized knowledge of the observer, hence are vague and time-consuming but typically accurate if performed correctly. The other one is relatively formal and is easily realized by the computer algorithms, which generally evaluate the similarity between the fused and source images. However, selecting a consistent criterion with the subjective assessment of the image quality is rigorous. Hence, there is a need to establish an evaluation system and an evaluation index system is set up to evaluate the proposed fusion algorithm. Conceptualization, L.L. ; methodology, L.L. ; formal analysis, L.L. ; investigation, L.L. ; resources, L.L. ; data curation, L.L. ; writing--original draft preparation, L.L. ; writing--review and editing, L.L. ; visualization, L.L. ; Supervision, M.B. ; Funding acquisition, L.L., M.B. and O.A.M. All authors have read and agreed to the published version of the manuscript.