2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

Shan Liu,Lirong Yin,Wenfeng Zheng,Bo Yang,Yang Wang,Jiawei Tian

doi:10.3390/app12062828

Abstract

Image-guided surgery (IGS) can reduce the risk of tissue damage and improve the accuracy and targeting of lesions by increasing the surgery’s visual field. Three-dimensional (3D) medical images can provide spatial location information to determine the location of lesions and plan the operation process. For real-time tracking and adjusting the spatial position of surgical instruments, two-dimensional (2D) images provide real-time intraoperative information. In this experiment, 2D/3D medical image registration algorithm based on the gray level is studied, and the registration based on normalized cross-correlation is realized. The Gaussian Laplacian second-order differential operator is introduced as a new similarity measure to increase edge information and internal detail information to solve single information and small convergence regions of the normalized cross-correlation algorithm. The multiresolution strategy improves the registration accuracy and efficiency to solve the low efficiency of the normalized cross-correlation algorithm.

Highlights

Image-guided surgery, which involves computer vision, biomedicine, imaging, automatic control, and other disciplines, is an interdisciplinary research direction [1–5]
This paper mainly studies the 2D/3D medical image registration method based on iterative regression, mostly the rigid registration algorithm in 2D/3D medical image registration
The normalized cross-correlation based on the Sobel operator (NCCS) is proposed by combining gradient vector angle with normalized cross-correlation [39]

Summary

Introduction

Image-guided surgery, which involves computer vision, biomedicine, imaging, automatic control, and other disciplines, is an interdisciplinary research direction [1–5]. Through the comprehensive application of a variety of medical image information, it carries out the preoperative diagnosis, disease analysis, planning of surgical path, intraoperative localization of the lesion, real-time tracking of surgical instruments, and adjustment of the spatial position of surgical instruments to achieve an accurate diagnosis [6,7]. Image navigation surgery’s success largely depends on the registration accuracy of preoperative image data and intraoperative image data and the accuracy of the 2D/3D registration algorithm. Image registration is developed for the integration of multisource image information. There are many research and clinical applications, the image registration procedures in image navigation surgery still need further improvement [9–13]. Developing more advanced registration methods is necessary to accurately and effectively register medical images

Methods

Findings

Discussion

Conclusion