classification and evaluation of muti modal medical image registration methods and similarity measures

Image registration is a fundamental issue in medical image analysis. it refers to the matching process between two or more images using the optimization of a similarity metric to find an optimal transformation function. in recent decades, many studies have been done on the medical image registration topic. therefore, this paper has investigated four main methodologies to solve the registration problem in medical applications. one of the most important topics in this area is the registration of multi-modal images. in this paper, we have reviewed various multimodal image registration techniques based on deep learning and proposed a classification for these methods. also one of the essential components of the medical image registration framework is the similarity measure function. there are different similarity metrics in this area and choosing an appropriate measure according to the application is a challenging problem. this paper is to present a review of different similarity measures in medical applications and a classification of these methods. based on this classification, techniques are investigated and each subclass is evaluated using performance criteria. therefore, the main goals of this article are as follows: 1) investigating the most significant image registration approaches. 2) systematic review of deep learning-based multimodal medical image registration and classify them. 3) providing classification for various similarity measure techniques according to registration applications. 4) creating an appropriate platform for evaluating these approaches and introducing the main challenges.