A high-precision automatic neuronavigation spatial registration method

Abstract

The invention belongs to the field of medical image processing and application and relates to a high-precision automatic neuronavigation space registration method. The method comprises, through a global optimization point set registration method based on Gaussian mixture models, transforming coordinates of a point cloud obtained by a laser scanner and a point cloud obtained through CT (computed tomography) to achieve primary registration; then by means of a disordered point automatic registration method, performing secondary registration on the result of the primary registration and patient space reconstructed by the CT during neuronavigation. Usage results indicate that the high-precision automatic neuronavigation space registration method is easy to implement, reliable in precision, applicable to clinical application and capable of being integrated in an existing navigation system, thereby greatly improving the precision of the navigation system.

Description

technical field [0001] The invention belongs to the field of medical image processing and application, and relates to a neuronavigation space registration method; in particular, a high-precision automatic neuronavigation space registration method. This method can make surgical navigation more accurate, practical and convenient in clinical application. Background technique [0002] As a medical auxiliary positioning device, the neuronavigation system can provide doctors with real-time intraoperative information, effectively make up for the shortcomings of traditional neurosurgery methods, and improve the quality of surgery. It can be seen from the clinical application that the core function of the neuronavigation system is to use images to locate and guide surgical instruments. Therefore, the positioning and registration accuracy of the real surgical scene and the image-guided space will directly affect the performance of the neuronavigation system. [0003] At present, the ...

AI Technical Summary

Problems solved by technology

However, in clinical applications, it is often difficult to determine the one-to-one correspondence between single points and the correlation between points and multiple points; A point set registration method is proposed

This type of method is mainly based on statistics and measurement theory, and uses probability density distribution to describe the point set space; however, there is a common problem in various methods of point set registration—converging to a local optimal solution rather than a global optimal solution is still exist

Images