Cone-beam computed tomography image correspondence and registration method

Abstract

The invention discloses a cone-beam computed tomography image correspondence and registration method. On the basis of a hybrid measure forest, denseness correspondence between super voxels between cone-beam CT images is generated by using a non-supervised clustering forest; and then denseness correspondence between super voxels between cone-beam CT images as well as cone-beam CT image registrationis estimated by using a forest-based measure. The step of training a hybrid measure forest is implemented as follows: extracting features of the super voxels; training an initial clustering forest and estimating similarity between the super voxels; estimating a weak mark; carrying out iterative enhancement of a forest-based measure based on the initial clustering forest; estimating flexibility consistency; carrying out on-line testing, inputting the features of the super voxels into the trained hybrid measure forest, estimating the similarity and correspondence between the super voxels, and estimating the flexibility consistency; and obtaining deformation parameters between the cone-beam CT images and realizing registration. Therefore, three-dimensional cone-beam computed tomography imagecorrespondence and registration can be established rapidly.

Description

technical field [0001] The invention relates to the technical fields of oral clinical medicine and computer vision, in particular to a method for corresponding and registering cone-beam computed tomography images. Background technique [0002] Cone-beam computed tomography (cone-beam CT) images are widely used clinically in orthodontics to evaluate treatments and measure growth and development of tissue structures. Efficient and reliable cone-beam CT image registration and dense voxel correspondence between images are the key to computer-aided intraoperative intervention and online image attribute transfer such as feature point localization and segmentation label transfer. In the field of medical image processing, 3D image registration has been studied for many years. Although many years of research have greatly promoted image registration, efficient cone-beam CT image registration and dense correspondence of voxels are still challenging. Considering that cone-beam CT usua...

AI Technical Summary

Problems solved by technology

Annotating 3D images is time-consuming and labor-intensive and subject to the subjective influence of individual annotations

Pseudo-labels defined based on super-voxel decomposition can alleviate the cost of labeling. However, pseudo-labels only come from a 3D image, and it is difficult to implement a classifier with good generalization ability in limited labeled data.

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