Laser three-dimensional point cloud and CT three-dimensional point cloud registration method

Abstract

The invention discloses a laser three-dimensional point cloud and CT three-dimensional point cloud registration method, and the method comprises the steps: carrying out the statistical filtering, point cloud region growth processing and voxel filtering of the body surface laser three-dimensional point cloud data of a patient, and reconstructing the 4D-CT data of the patient into CT three-dimensional point cloud data; selecting a point set S1 from the laser three-dimensional point cloud data, and selecting a point set S2 from the CT three-dimensional point cloud data; extracting a feature pointset Q from S1 and extracting a feature point set V from S2 by using a Point RegNet network; setting all the feature points in Q to be connected with one another to form a set ES of feature edges, setting all the feature points in V to be connected with the feature points adjacent to K to form a set E of feature edges, and registering the graphs (V, E) with the graphs (Q, ES); distributing weightsto the points in the S1 by utilizing the motion amplitude information, establishing a GMM model on the basis of the S2, and constructing a probability likelihood function by combining the GMM model with the points in the S1 after the weights are distributed. According to the technical scheme, the problem that the prior art cannot be directly applied to body surface point cloud registration is solved.

Description

technical field [0001] The invention relates to the field of medical point cloud registration, in particular to a registration method for laser three-dimensional point cloud and CT three-dimensional point cloud. Background technique [0002] The rapid registration of laser 3D point cloud and CT 3D point cloud is a hot topic in current research, and it has important clinical application value in the positioning of patients in radiotherapy and the registration of robotic surgical navigation systems. Based on the CT image data made before or during the operation, the location of the lesion in the patient's CT coordinate system is extracted, and the patient's CT three-dimensional point cloud data is extracted. Through the laser 3D imaging system mounted on the robotic arm, the patient's laser 3D point cloud data in the coordinate system of the robotic arm can be scanned. By registering the 3D point cloud scanned by the robot arm with the CT 3D point cloud data, the spatial posi...

AI Technical Summary

Problems solved by technology

However, due to the relatively flat surface of the human body and the influence of respiratory motion, the problem of accurate registration of 3D point clouds on the body surface is not a strict rigid registration problem.

Therefore, traditional point cloud registration methods cannot be directly applied to the registr

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