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Cataract OCT image restoration method and system based on machine learning

A technology of machine learning and restoration methods, applied in image enhancement, image analysis, image data processing, etc.

Active Publication Date: 2021-04-23
SHANTOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to propose a machine learning-based cataract OCT image repair method and system to solve one or more technical problems in the prior art, at least provide a beneficial option or create conditions

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  • Cataract OCT image restoration method and system based on machine learning
  • Cataract OCT image restoration method and system based on machine learning
  • Cataract OCT image restoration method and system based on machine learning

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Embodiment Construction

[0066] The idea, specific structure and technical effects of the present invention will be clearly and completely described below in conjunction with the embodiments and accompanying drawings, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0067] Such as figure 1 Shown is a flow chart of a machine learning-based cataract OCT image repair method according to the present invention, combined below figure 1 A machine learning-based OCT image restoration method for cataracts according to an embodiment of the present invention will be described.

[0068] The present invention proposes a kind of cataract OCT image restoration method based on machine learning, specifically comprises the following steps:

[0069] S100, collecting the original image with an OCT scanner and judging whet...

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Abstract

The invention discloses a cataract OCT image restoration method and system based on machine learning. The cataract OCT image restoration method introduces an optical information processing technology, changes the frequency spectrum of an object through an optical spatial filter, carries out the amplitude, phase or composite filtering of an inputted image, and carries out the fuzzy processing of the image, thus achieving the simulation of a cataract OCT fuzzy image; then the neutral density attenuation sheet is added to an OCT scanner lens to scan healthy eyeballs, a fundus OCT blurred image is obtained to simulate a cataract image, then an OCT clear image of the same person without the attenuation sheet is scanned, and the cataract retina OCT blurred image is restored into a clear image. The ten layers of retina structures can be clearly seen from the restored image, and the number of network models is reduced, so that the workload and the total training time are reduced, and the technology of restoring the blurred image to be clear is realized only by using the Pix2pix model.

Description

technical field [0001] The invention relates to the technical field of optical coherence tomography image processing, in particular to a method and system for repairing OCT images of cataracts based on machine learning. Background technique [0002] Cataracts are one of the most common causes of visual impairment worldwide, affecting an estimated 16 million people worldwide. Retinal imaging of cataract patients using a fundus inspection mirror is a very challenging task, because the light scattering caused by the turbidity of the fundus medium will seriously affect the imaging quality. As a result, the picture is blurred and the contrast is low, so it is difficult for doctors to evaluate the condition of the fundus of cataract patients and carry out effective treatment. Therefore, it is very important and has clinical significance to develop a technology to restore clear cataract retinal blurred pictures. At present, the more traditional methods are based on contrast-limit...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T7/00
Inventor 杨玮枫张叶叶刘希望
Owner SHANTOU UNIV
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