A Method of Image Noise Removal About Oct and Octa

An image noise and image technology, applied in the field of eyeball micro-movement noise, can solve problems such as eyeball micro-movement noise, inaccurate analysis and diagnosis of eye diseases, unsuitable denoising methods, etc., to achieve suppression of unnecessary structures, the ultimate Good performance, good visualization effects

Active Publication Date: 2021-12-17
CIXI INST OF BIOMEDICAL ENG NINGBO INST OF MATERIALS TECH & ENG CHINESE ACAD OF SCI +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, eye fretting noise in the form of bright short lines along the fast scan direction still appears on many clinical datasets
Therefore, the extraction and quantification of vessel-related features will be affected by this noise, which further brings inaccuracy to the analysis and diagnosis of ocular diseases.
[0006] Unlike isotropic and randomly distributed noise such as pepper noise and speckle noise, eye movement noise is highly directional and thus may not be suitable for conventional denoising methods such as Kalman filtering

Method used

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  • A Method of Image Noise Removal About Oct and Octa
  • A Method of Image Noise Removal About Oct and Octa
  • A Method of Image Noise Removal About Oct and Octa

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Embodiment 1

[0034] A method for denoising an en face image of OCTA using a two-dimensional transform domain Fourier filter, the method is based on Fourier transform (FT) for denoising, and specifically includes steps:

[0035] Step S0, image preprocessing step. The en face image of OCTA is preprocessed to facilitate subsequent unified processing by standard methods. include:

[0036] Step S0-1, converting the en face image of OCTA into a grayscale image;

[0037] Step S0-2, normalize the grayscale image to obtain a grayscale image .

[0038] Step S1, extracting a frequency domain image from the image obtained in step S0. In this embodiment, for grayscale images Perform two-dimensional Fourier transform to realize grayscale image Transform from the spatial domain to the frequency domain to obtain a frequency domain image.

[0039] Step S2, removing stripe information in the frequency domain image obtained in step S1, and then obtaining a denoised frequency domain image. It can be...

Embodiment 2

[0045] A method for denoising an en face image of OCTA using a two-dimensional transform domain Fourier filter, the method is based on wavelet transform (WT) for denoising, and specifically includes steps:

[0046] Step S0, image preprocessing step. The en face image of OCTA is preprocessed to facilitate subsequent unified processing by standard methods. include:

[0047] Step S0-1, converting the en face image of OCTA into a grayscale image;

[0048] Step S0-2, normalize the grayscale image to obtain a grayscale image .

[0049] Step S1, extracting a frequency domain image from the image obtained in step S0. In this embodiment, first in step S1-0, the grayscale image obtained from step S1 Decompose to obtain a horizontal subband image containing stripe information; and then perform two-dimensional Fourier transform on the horizontal subband image obtained in step S1-0 through step S1-1 to obtain a frequency domain image. This embodiment adopts wavelet transform to dec...

Embodiment 3

[0061] A method for denoising an en face image of OCTA using a two-dimensional transform domain Fourier filter, the method is based on non-subsampled contourlet transform (NSCT) for denoising, specifically including steps:

[0062] Step S0, image preprocessing step. The en face image of OCTA is preprocessed to facilitate subsequent unified processing by standard methods. include:

[0063] Step S0-1, converting the en face image of OCTA into a grayscale image;

[0064] Step S0-2, normalize the grayscale image to obtain a grayscale image .

[0065] Step S1, extracting a frequency domain image from the image obtained in step S0. In this embodiment, first in step S1-0, the grayscale image obtained from step S1 Decompose to obtain a horizontal subband image containing stripe information; and then perform two-dimensional Fourier transform on the horizontal subband image obtained in step S1-0 through step S1-1 to obtain a frequency domain image. This embodiment uses non-subsa...

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Abstract

The invention belongs to the technical field of medical image processing, and in particular relates to a method for removing OCT and OCTA image noise, which is used for eyeball micro-movement noise in en face images of OCT and OCTA. The method includes: step S1, extracting a frequency domain image; step S2, removing stripe information in the frequency domain image to obtain a denoised frequency domain image; step S3, reconstructing a denoised image based on the denoised frequency domain image. The eyeball fretting noise is a bright short line in the horizontal direction in the en face image, and the horizontal fretting artifact can be extracted from the image in the 2D transform domain. In the above technical solution of the present invention, firstly, the facial OCTA image is converted or decomposed to obtain a frequency-domain image, then the frequency-domain image is denoised by a filter, and finally a noise-removed angiographic image is reconstructed based on the denoised frequency-domain image.

Description

[0001] A Method of Image Noise Removal About OCT and OCTA technical field [0002] The invention belongs to the technical field of medical image processing, and in particular relates to a method for removing OCT and OCTA image noise, which is used for eyeball micro-movement noise in en face images of OCT and OCTA. Background technique [0003] Optical coherence tomography angiography (OCTA) is a non-invasive angiographic imaging technique that has been widely used in the research and diagnosis of diseases in the fundus region in recent years. However, OCTA requires longer acquisition durations than photography-based modalities such as slit lamps and fundus cameras. Domestic and foreign studies have shown that the human eye is in a state of micro-motion when observing the scene, and there are three modes of micro-motion: high-frequency tremor, drifting motion and flicker. Therefore, the micro-movement of the eyeball during the acquisition process has become the main source o...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B3/12A61B3/14G06T5/00G06T11/00
CPCG06T5/002G06T11/003A61B3/102A61B3/1241A61B3/14A61B3/1233G06T2207/10101
Inventor 杨建龙方黎洋王浩郭雨荟胡衍刘江
Owner CIXI INST OF BIOMEDICAL ENG NINGBO INST OF MATERIALS TECH & ENG CHINESE ACAD OF SCI
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