Fundus image registering method based on SIFT characteristics

Abstract

The invention discloses a fundus image registering method based on SIFT characteristics. The method comprises: conducting angle classification to a batch of inputted fundus images; calculating the transformative relationship among the images; converting the images onto the same background; and through the rapid switching of the images, finding out which parts of the fundus change. The invention mainly uses a fuzzy convergence optic disc positioning algorithm and conducts angle classification to the batch of inputted fundus images according to the position of the optic disc wherein the angle classification is referred as two types: the left side and the right side; and then in each image classification, a selected and uploaded first image is used as a reference for other images to register with; the SIFT characteristic points of all the images are extracted and the matching relation between every two points is calculated. Finally, the RANSAC algorithm is used to calculate the transformation model parameters between every two images. The images are converted onto the same background according to the transformation model; and an image switching interval is configured so that through the switching of the images, it is possible to find out the change among the images rapidly and accurately.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a fundus image registration method based on SIFT features. Background technique [0002] Fundus image diagnosis is an objective and standard diagnostic method in ophthalmology. Fundus images are of great significance for the early detection, diagnosis and guidance of treatment of fundus diseases such as diabetes and hypertension, as well as fundus diseases such as macular degeneration, fundus arteriosclerosis and retinopathy. In general, each patient will have multiple visit records, resulting in multiple fundus images. By comparing the images at different times, it is possible to quickly and accurately track and discover the patient's fundus lesions. [0003] Fundus images are rich in blood vessels and texture structures, which are of great diagnostic value for eye diseases such as drusen and macula, as well as systemic diseases such as diabetes, l...

AI Technical Summary

Problems solved by technology

However, because fundus images contain rich blood vessels and texture structures, it is not only time-consuming and laborious to compare the changes between images with the naked eye, but it is also difficult to find subtle changes

Traditional manual registration has the defects of low accuracy, heavy workload, and non-repeatability, and the large amount of data of fundus scanning images is a nightmare for manual registration. Therefore, automatic registration of fundus images has great application value

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