OCT eye fundus image data registration method

Abstract

The invention discloses an OCT eye fundus image data registration method, which comprises the following steps: at first, extracting retina edges of an OCT eye fundus image by virtue of a Canny edge detection method, and collecting the edges in a format of point cloud data; then, extracting characteristic points of the point cloud data by adopting a space grid division method; next, calculating a transformation matrix of point clouds to be registered to eliminate obvious position errors by virtue of an SVD (singular value decomposition) algorithm; finally, performing accurate registration by virtue of an improved iterative closest point algorithm, and applying an obtained rotation matrix and translation matrix to the original OCT eye fundus image to obtain a final result. When a dense point cloud with a relatively large volume of data is processed, the method has obvious advantages in terms of time complexity and registration accuracy. Under most conditions, the efficiency of a conventional iterative closest point algorithm is improved by 70 percent by the method, not only can the OCT eye fundus image be effectively registered and spliced, but also the accuracy of a large-vision eye fundus retina accuracy is ensured.

Description

technical field [0001] The present application relates to image registration technology, in particular, to a method for registration of three-dimensional fundus image data using OCT imaging. Background technique [0002] Image registration technology usually refers to finding a series of spatial transformations for an image so that it has the same spatial position as the feature information corresponding to another image or multiple images. For the fundus data of OCT three-dimensional imaging, the result of registration should make the layers of the retina in different fundus images aligned with each other without obvious faults. [0003] Currently, image registration techniques are mainly divided into two categories: feature-based image registration and mutual information-based image registration. The feature-based image registration method first extracts the feature information of the image, and then uses these features as a model for registration. The result of feature ...

AI Technical Summary

Problems solved by technology

However, due to the bottleneck of the optical coherence tomography equipment itself and the involuntary movement of the human eye during the scanning process, the obtained optical coherence tomography fundus data will be doped with some small non-rigid transformations (Chen Guolin. Non-rigid medical image Research and Implementation of Registration Method [D]. Master Thesis of Nanjing University of Science and Technology, 2009)

Therefore, the above method has certain limitations when dealing with such special clinical ophthalmology optical coherence tomography images.

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