# 2D/3D Multimode Medical Image Alignment Based on Spatial Histograms
## 5 Conclusions  
This paper introduces two weighted histograms of gradient directions and second-order spatial histogram concepts, and analyzes the advantages and limitations of weighted histograms of gradient directions in 2D/3D registration. The weighted histogram of gradient directions is sensitive to rotation and scaling, but insensitive to translation. It leads to a complex registration process and a small convergence range of translation parameters, and the algorithm has certain limitations. This paper introduces a spatial histogram into the registration process. It proposes a 2D/3D registration algorithm based on a weighted spatial histogram of gradient directions to solve these problems.  
The algorithm uses the spatial weighted histogram of gradient directions to extract the statistical characteristics of the image. The pixels' positional information is added based on retaining the weighted histogram of gradient directions' sensitivity to the rotation. By weighting the distance measure of the weighted histogram of gradient directions with the coordinate mean and square difference, the spatial information and the gradient information are effectively combined.  
Our experimental results show that the spatial weighted histogram of gradient directions has a high sensitivity to translation and rotation. As a result, the convergence range of the algorithm is larger. When the organizational structure in the image's foreground exceeds the image's field of view, it can still achieve successful registration.  
There are still some aspects that can be improved and expanded. The experimental data used in this paper were two-dimensional simulated X-ray images and three-dimensional CT images. Their imaging principles are similar, but many types of medical images are used in clinical practice. Moreover, there is a significant difference between some types of images in terms of imaging principles [38]. Therefore, the 2D/3D registration method based on DRR is not always applicable.  
Deep learning technology is developing rapidly [39], and has strong performance in feature extraction. However, there are few studies on 2D/3D registration based on deep learning. Therefore, combining deep learning technology with 2D/3D registration should be a research focus in the future.  
Conceptualization, W.Z., B.Y. and S.L.; methodology, B.Y. and L.Y.; software, Y.W.; validation, S.L.; formal analysis, Y.W. and L.Y.; investigation, B.Y.; resources, Y.B. and S.L.; data curation, Y.W.; writing--original draft preparation, Y.B. and L.Y.; writing--review and editing, Y.B., M.L., S.L. and L.Y.; visualization, Y.W. and L.Y.; supervision, B.Y.; project administration, W.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
Supported by the Sichuan Science and Technology Program (Grant: 2021 YPQ0003).  
Not applicable.  
Publicly available datasets were analyzed in this study. These data can be found here: [[[https://data.kitware.com/#collection/57b5c9e58d777f126827f5a1](https://data.kitware.com/#collection/57b5c9e58d777f126827f5a1)]([https://data.kitware.com/#collection/57b5c9e58d777f126827f5a1](https://data.kitware.com/#collection/57b5c9e58d777f126827f5a1))] accessed on 23 February 2020.  
The authors declare no conflict of interest.