CN112184576A - High-reflection bright spot enhancement method in spectral domain optical coherence tomography - Google Patents
High-reflection bright spot enhancement method in spectral domain optical coherence tomography Download PDFInfo
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- 238000012014 optical coherence tomography Methods 0.000 title claims abstract description 39
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Abstract
The invention discloses a high-reflection bright spot enhancement method in spectral domain optical coherence tomography, which utilizes a transfer function (sigmoid) to control the range compression of an input image. Histogram equalization is applied to the output image of the sigmoid (sigmoid) transfer function. And performing histogram equalization and logarithmic transformation on the image. Simultaneously, the two methods are used for transforming the domain function, and the histogram matching combines two parallel processes together through data mapping; inverse logarithm and inverse orthogonality are applied to the mapping data for data transformation to obtain an enhanced image. The method can clearly enhance the high-reflection bright spots so as to achieve the purposes of visibility, segmentation and quantification.
Description
Technical Field
The invention relates to the field of medical image processing in computer science, in particular to a method for combining a spatial domain and a transform domain of an image, and more particularly to a high-reflection bright spot enhancement method in spectral domain optical coherence tomography.
Background
Optical Coherence Tomography (OCT) is a new tomography technology with the greatest development prospect in recent years, especially in the aspects of biopsy and imaging of biological tissues, and has attractive application prospects, and attempts are made to apply the OCT technology to clinical diagnosis in ophthalmology, dentistry and dermatology. After the human eye is scanned by OCT (optical coherence tomography), the acquired spectral signals of the eye need to be processed into OCT signals, and finally an OCT image is obtained.
After obtaining the OCT image, an image processing method is generally used to extract morphological features, texture features, or other features in a layered state to complete pathological diagnosis. With the development of a time-frequency analysis method, in combination with the broadband property of a light source in OCT equipment, the spectrum of a specified region inside a detection object can be extracted from an OCT image step by the time-frequency change method at present, and the application field of the OCT technology is greatly expanded.
However, in practical detection applications, OCT apparatuses are required to obtain cross-sectional imaging of samples with high resolution, high speed, and sensitivity. The current method mainly extracts image contrast and functional information by a spatial enhancement method, and maps spectral features on a coherent image. To generate depth resolved spectral information from longitudinal scans, short-time fourier transform (STFT) or Continuous Wavelet Transform (CWT) is typically employed to obtain the information, subject to the wide-domain time-frequency distribution (TFD) itself. Ultimately resulting in a mutual constraint between time and frequency, i.e. this trade-off between time (depth) resolution and frequency (wavelength) resolution.
In the field of OCT detection, light sources with high spatial and temporal correlation, such as superluminescent diodes (SLDs), are commonly used. However, light using such light sources can typically only achieve small depth resolution. Furthermore, in the case of simultaneous use of spatially resolved planar sensors, so-called ghosts may occur as a result of coherent crosstalk, which can only be avoided by an approximately complete destruction of the spatial correlation, which requires a certain technical outlay on the one hand and is only achieved conditionally despite the technical outlay on the other hand. In the case where a plurality of different SLDs are superimposed to become one light source, ghost may be additionally caused due to a side lobe maximum in the spectrum. Furthermore, in the case of SLD, a sufficient signal-to-noise ratio cannot always be achieved for high image quality because of its relatively small power of at most about 20mW, which furthermore decreases with increasing spectral bandwidth.
In industrial application, the OCT image detection rate is high, and each object has more dynamic changes, so that the analysis amount of the OCT image is very large, and the efficiency is very low by correcting the OCT image. Therefore, one correction of the image is required for the subsequent automatic analysis. According to retrieval, in OCT spectrum extraction, the algorithm reports of automatic detection are few, and various researches are still in the beginning stage.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a high-reflection bright spot enhancement method in spectral domain Optical Coherence Tomography (OCT), which can clearly enhance the high-reflection bright spot of an OCT image so as to achieve the purposes of visibility, segmentation and quantification.
The technical scheme for realizing the purpose of the invention is as follows:
a high-reflection bright spot enhancement method in spectral domain optical coherence tomography comprises the following steps:
(1) inputting an image, applying orthogonal transformation to the OCT, performing orthogonal transformation on the input image of the spectral domain OCT by adopting Discrete Cosine Transform (DCT), and converting the intensity information of the image into the orthogonal transformation through mapping;
(2) applying a logarithmic transformation to the orthogonally transformed amplitude values, creating a new matrix in which the transformed image phase is preserved and used for phase recovery by the transform coefficients, resulting in the logarithm of the transform coefficient modulus:
in the formula,
is a shift coefficient, set to 1;
（
) In order to be a function of the mapping,
is the image intensity;
is an output image;
(3) parallelly applying an S-shaped transfer function to a spectral domain OCT input image, and then applying histogram equalization to the output of the S-shaped transfer function image;
in the formula,
is a gray scale of the image and,
for the position of the input image, the range is
；
Is statistical information of the image;
is an enhanced pixel value;
(4) applying an orthogonal transform of the DCT to the histogram equalized image;
(5) applying a logarithmic transform to the orthogonally transformed amplitude values of step (4);
(6) mapping the input image data of step (2) using histogram mapping to match the histogram equalized image obtained in step (5);
(7) applying an inverse logarithmic transformation to the matched data, and restoring the converted image phase;
(8) an inverse orthogonal transform is applied to the inverse logarithm processed data to generate an enhanced image, and the image is output.
The step (3) is
Is represented as follows:
in the formula,
is defined as the local mean value of the average,
is arranged as
Offset pixel values of (a); use of
Sliding window, local mean as follows:
wherein,
is the standard deviation of the gaussian distribution and,
is composed of
。
The invention has the advantages that:
(1) a new algorithm for detecting high-reflection bright spots is improved; enhancing high-reflection bright spots in the OCT image in a combined spatial frequency domain by using the dynamic range and the contrast of the image;
(2) the operation process of high-reflection bright spot enhancement is further improved by combining a new method of a spatial frequency domain;
(3) and the mapping technology of the image from high to low dynamic range ensures that the details of the high-reflection bright spots become clearer. In addition, the compressibility of the original image is kept unchanged, and the operation time is short;
(4) the algorithm for enhancing the high-reflection bright spots in the OCT image is put forward for the first time, the perceptibility and the interpretability of high-reflection bright spot information in the OCT image are improved, so that more effective treatment and disease monitoring are carried out, and better data input is provided for medical algorithms (such as automatic high-reflection bright spot segmentation). Clinicians and computer vision programmers can exploit the results of the algorithm to perform advanced image analysis, such as target (e.g., high-reflection speckle) detection or statistical analysis (e.g., quantification and measurement of high-reflection speckle).
Drawings
FIG. 1 is a flowchart of an algorithm according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples, but is not limited to the content of the invention.
Example (b):
as shown in fig. 1, the spatial transformation uses a transfer function (sigmoid) to control the range compression of the input image; applying histogram equalization to an output image of a sigmoid (sigmoid) transfer function; carrying out histogram equalization and logarithmic transformation on the image; meanwhile, the domain function is transformed by the two methods; histogram matching is the combination of two parallel processes by data mapping; inverse logarithm and inverse orthogonality are applied to the mapping data for data transformation to obtain an enhanced image.
A method for high reflection bright spot enhancement in spectral domain optical coherence tomography, comprising the steps of:
1) inputting an image, applying orthogonal transformation to the OCT, performing orthogonal transformation on the input image of the spectral domain OCT by adopting Discrete Cosine Transform (DCT), and converting the intensity information of the image into the orthogonal transformation through mapping;
2) a logarithmic transformation is applied to the orthogonally transformed amplitude values. However, the histograms generated by the logarithmic transformation are compressed and difficult to understand. To solve this problem, we create a new matrix in which the transformed image phase is preserved and used for phase recovery by the transform coefficients, resulting in the logarithm of the transform coefficient modulus:
in the formula,
is the shift coefficient, set to 1. To avoid discontinuities, a displacement coefficient factor is added to the equation. ln (
) Is a function of the mapping of the data to the image,
is the image intensity;
3) the S-shaped transfer function is applied to the spectral domain OCT input image in parallel:
in the formula,
is the gray scale of the image and,
is the position of the input image, in the range of
；
Is the statistical information of the image;
is the enhanced pixel value. In addition, the first and second substrates are,
is represented as follows:
in the formula,
is defined as the local mean value of the average,
is arranged as
The offset pixel value of (2). Use of
Sliding window, local mean as follows:
wherein,
is the standard deviation of the gaussian distribution.
Then applying histogram equalization to the output of the sigmoid transfer function image;
4) applying an orthogonal transform of the DCT to the histogram equalized image;
5) applying a logarithmic transformation to the orthogonally transformed amplitude values of step 4);
6) mapping the input image data of step 2) to match the histogram equalized image of step 5) using histogram mapping;
7) an inverse logarithmic transformation is applied to the matched data. The converted image phase is restored in this step;
8) an inverse orthogonal transform is applied to the inverse logarithm processed data to generate an enhanced image, and the image is output.
Claims (2)
1. A high-reflection bright spot enhancement method in spectral domain optical coherence tomography is characterized by comprising the following steps: the method comprises the following steps:
(1) inputting an image, applying orthogonal transformation to the OCT, performing orthogonal transformation on the input image of the spectral domain OCT by adopting Discrete Cosine Transform (DCT), and converting the intensity information of the image into the orthogonal transformation through mapping;
(2) applying a logarithmic transformation to the orthogonally transformed amplitude values, creating a new matrix in which the transformed image phase is preserved and used for phase recovery by the transform coefficients, resulting in the logarithm of the transform coefficient modulus:
in the formula,
is a shift coefficient, set to 1;
（
) In order to be a function of the mapping,
is the image intensity;
is an output image;
(3) parallelly applying an S-shaped transfer function to a spectral domain OCT input image, and then applying histogram equalization to the output of the S-shaped transfer function image;
in the formula,
is a gray scale of the image and,
for the position of the input image, the range is
；
Is statistical information of the image;
is an enhanced pixel value;
(4) applying an orthogonal transform of the DCT to the histogram equalized image;
(5) applying a logarithmic transform to the orthogonally transformed amplitude values of step (4);
(6) mapping the input image data of step (2) using histogram mapping to match the histogram equalized image obtained in step (5);
(7) applying an inverse logarithmic transformation to the matched data, and restoring the converted image phase;
(8) an inverse orthogonal transform is applied to the inverse logarithm processed data to generate an enhanced image, and the image is output.
2. The method of claim 1, wherein: the step (3) is
Is represented as follows:
in the formula,
is defined as the local mean value of the average,
is arranged as
Offset pixel values of (a); use of
Sliding window, local mean as follows:
wherein,
is the standard deviation of the gaussian distribution and,
is composed of
。
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040213478A1 (en) * | 2001-05-02 | 2004-10-28 | Vyacheslav Chesnokov | Image enhancement methods and apparatus therefor |
| WO2013049153A2 (en) * | 2011-09-27 | 2013-04-04 | Board Of Regents, University Of Texas System | Systems and methods for automated screening and prognosis of cancer from whole-slide biopsy images |
| CN107730565A (en) * | 2017-10-12 | 2018-02-23 | 浙江科技学院 | In Spectra feature extraction method in a kind of material based on OCT image |
| WO2020165196A1 (en) * | 2019-02-14 | 2020-08-20 | Carl Zeiss Meditec Ag | System for oct image translation, ophthalmic image denoising, and neural network therefor |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040213478A1 (en) * | 2001-05-02 | 2004-10-28 | Vyacheslav Chesnokov | Image enhancement methods and apparatus therefor |
| WO2013049153A2 (en) * | 2011-09-27 | 2013-04-04 | Board Of Regents, University Of Texas System | Systems and methods for automated screening and prognosis of cancer from whole-slide biopsy images |
| CN107730565A (en) * | 2017-10-12 | 2018-02-23 | 浙江科技学院 | In Spectra feature extraction method in a kind of material based on OCT image |
| WO2020165196A1 (en) * | 2019-02-14 | 2020-08-20 | Carl Zeiss Meditec Ag | System for oct image translation, ophthalmic image denoising, and neural network therefor |
Non-Patent Citations (1)
| Title |
|---|
| IDOWU PAUL OKUWOBI等: "Automated Quantification of Hyperreflective Foci in SD-OCT With Diabetic Retinopathy", IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, vol. 24, no. 4, pages 1125 - 1136, XP011781537, DOI: 10.1109/JBHI.2019.2929842 * |
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