Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers.

翻译：图像配准（IR）是一种通过形变图像使其与参考空间对齐的过程，有助于医学从业者在标准化参考坐标系（如统一旋转和缩放）下检查各类医学图像。本文通过简单数值示例介绍图像配准，给出其定义及面向空间的符号表示。综述涵盖多种图像变换类型，包括仿射变换、可变形变换、可逆变换与双向变换，以及Voxelmorph、Demons、SyN、迭代最近点算法和SynthMorph等医学图像配准算法。本文还探讨了基于图谱的配准及多阶段图像配准技术（如粗细配准与金字塔方法）。此外，本综述讨论了医学图像配准的分类体系、数据集、评估指标（包括基于相关性的度量、基于分割的度量、处理时间与模型规模），并探索了其在图像引导手术、运动追踪和肿瘤诊断中的应用。最后，本文展望了未来研究方向，包括Transformer的进一步发展。