Ocular fundus image registration method estimated based on distance transformation parameter and rigid transformation parameter

Abstract

The invention provides an ocular fundus image registration method estimated based on distance transformation parameter and rigid transformation parameter, comprising the following main steps: (1) ocular fundus images are loaded; (2) the optic disk center is extracted to estimate image translation parameters; (3) gradient vectors of pixel points in the adjacent zone of the optic disk are calculated, vessel segmentation is carried out, vessel distribution probability characteristics are calculated, and the estimation of image rotation parameters are obtained by minimizing two probability distribution relative entropies (Kullback-Leibler Divergence); (4) the Euclidean distance transformation of vessels segmented in step 3 is calculated; (5) accurate registration of images is carried out. The invention is a quick, precise, robust and automatic ocular fundus image registration algorithm, and has great application value on the aspect of ocular fundus image registration.

Description

technical field [0001] The invention relates to image processing and pattern recognition technology, in particular to an automatic fundus image registration technology based on distance transformation and rigid transformation parameter estimation. Background technique [0002] At present, the mainstream automatic fundus image registration methods mainly include registration based on blood vessel features and registration methods based on mutual information. These methods still have some shortcomings. For the registration based on blood vessel features, it is difficult to accurately extract the blood vessel features for some low-quality images. The registration based on mutual information takes a long time for registration and may fall into a Local extremum, can not get accurate registration results. Therefore, many doctors still use manual registration. The success rate and accuracy of manual registration are relatively high, but its biggest disadvantage is that it increase...

AI Technical Summary

Problems solved by technology

These methods still have some shortcomings. For the registration based on blood vessel features, it is difficult to accurately extract the blood vessel features for some low-quality images. The registration based on mutual information takes a long time for registration and may fall into a Local extremum, unable to obtain accurate registration results

Therefore, many doctors still use manual registration. The success rate and accuracy of manual registration are relatively high, but its biggest disadvantage is that it increases the burden on doctors and the registration speed is very slow.

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