CN111861917A - Choroidal OCT image enhancement method and device based on signal reverse compensation - Google Patents

Choroidal OCT image enhancement method and device based on signal reverse compensation Download PDF

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CN111861917A
CN111861917A CN202010660531.9A CN202010660531A CN111861917A CN 111861917 A CN111861917 A CN 111861917A CN 202010660531 A CN202010660531 A CN 202010660531A CN 111861917 A CN111861917 A CN 111861917A
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attenuation
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choroidal
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周翔天
沈梅晓
吴昊
陈思思
金梓
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Wenzhou Medical University
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    • G06T5/70—
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A61B3/102—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for optical coherence tomography [OCT]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A61B3/12—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
    • A61B3/1225—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes using coherent radiation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06N3/00—Computing arrangements based on biological models
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G06T11/008—Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/12—Edge-based segmentation
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10101—Optical tomography; Optical coherence tomography [OCT]
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20048—Transform domain processing
    • G06T2207/20056—Discrete and fast Fourier transform, [DFT, FFT]
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30041—Eye; Retina; Ophthalmic
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Abstract

A choroid OCT image enhancement method and device based on signal reverse compensation are disclosed, wherein a choroid image signal acquired by a system is preprocessed, and a reverse signal attenuation compensation model is established based on the idea of OCT backscattering signal reverse compensation, so that the choroid signal is reversely compensated, and the signal-to-noise ratio between blood vessels and non-blood vessel tissues is enhanced, thereby solving the defect that the boundary of the choroid blood vessel OCT image is difficult to segment.

Description

Choroidal OCT image enhancement method and device based on signal reverse compensation
Technical Field
The invention relates to the technical field of OCT (optical coherence tomography), in particular to a choroidal OCT (optical coherence tomography) image enhancement method and a choroidal OCT image enhancement device based on signal reverse compensation.
Background
The choroid is a highly vascularized and pigment-rich tissue located between the retina and sclera. The main function is to provide oxygen and nutrients to the RPE and outer retina, which delivers approximately 90% of the retinal oxygen consumption, necessary to maintain the high metabolic activity of the outer retinal photoreceptor cells; the choroid is the only pathway for its exchange of material, particularly in the macular foveal avascular zone. Many diseases are closely related to the abnormality of the vascular structure of the choroid, such as age-related macular degeneration, high myopia macular degeneration, diabetic retinopathy and the like. Therefore, it is of great significance to realize quantitative analysis of choroidal vessels.
The frequency domain optical coherence tomography technology can realize three-dimensional imaging of the fundus, and complete choroidal vascular tissues and stromal tissues can be displayed through an averaged image enhancement technology or a high-penetrability sweep light source, so that a favorable imaging technology is provided for choroidal vascular analysis. However, the current system does not solve the problem of poor signal-to-noise ratio of the blood vessel in the deep part of the choroid caused by backscattering attenuation, thereby causing the difficulty of blood vessel boundary segmentation. For this reason, most of the existing algorithms for quantitatively analyzing choroidal vessels in OCT images only adopt the traditional image processing method, but because the choroid is below the RPE, light passes through the RPE and then is attenuated due to pigment absorption, and meanwhile, the choroid is rich in pigment, so that the light passes through the choroid and is attenuated in light signals at the depth, and the boundary between the choroid and the pigment epithelium layer and the boundary between the choroid and the sclera are blurred.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a choroid OCT image enhancement method and device based on signal reverse compensation.
The technical solution adopted by the invention is as follows: a choroidal OCT image enhancement method based on signal inverse compensation comprises the following steps:
(1) and (3) acquiring data: acquiring a fundus image by using OCT, preprocessing the image including appropriate cutting, and reserving an OCT choroid intensity map;
(2) reverse attenuation compensates for choroidal signals: by extracting the attenuation principle and rule of the scattered light of the choroid and constructing a signal compensation and enhancement algorithm, the visualization and the contrast of the choroid image can be improved.
The step (2) of inversely attenuating and compensating the choroidal signal specifically comprises the following steps: by extracting the attenuation principle and the attenuation rule of the choroidal scattered light and constructing a signal compensation and enhancement algorithm, the visualization and the contrast of a choroidal image can be improved, the attenuation correction processing algorithm of an OCT signal comprises two steps of attenuation compensation and image enhancement contrast for light respectively, and in an OCT system, photoelectric signals interfered between a reference arm and a sample arm can be represented by the following formula:
Figure BDA0002578323890000021
where k is the wave number, the collected signal is divided into m wave numbers with equal interval, rho is the photoelectric conversion efficiency of OCT detector, and S [ k ]m]Refers to the radiation energy of the light source in the corresponding wave band, and Δ x is the optical path difference between the reference arm and the sample arm, R RAnd RSReflectance of the reference arm and sample arm, respectively;
interaction term H [ k ] between reference arm and sample armm]Can be obtained from the above formula (1):
Figure BDA0002578323890000022
the reflectance profile function s (z) in the depth direction can be obtained by performing inverse discrete fourier transform on the above equation (2):
Figure BDA0002578323890000023
discretizing the formula, and performing attenuation correction compensation on the OCT signal of each pixel point:
Figure BDA0002578323890000024
where N is the number of pixels of the a scan, and α can be adjusted according to the tissue, the premise that the above equation (3) holds is that it is assumed that most of the beam energy is attenuated within the imaging depth range, and the attenuation outside the imaging depth range is negligible.
Performing exponentiation operation on the original signal subjected to attenuation correction compensation to enhance image contrast, wherein the signal intensity of each pixel is as follows:
Figure BDA0002578323890000031
s in formula (5)ac(z) is the attenuation corrected signal.
A choroidal OCT image enhancement device based on signal inverse compensation comprises the following modules,
an input module: for inputting an OCT choroidal intensity map;
an image processing module: constructing a signal compensation and enhancement algorithm by extracting a choroid scattered light attenuation principle and rule for improving visualization and contrast of a choroid image;
an output module: for outputting the image after removing the artifact by the attenuation correction.
The image processing module comprises the following algorithm models: by extracting the attenuation principle and the attenuation rule of the choroidal scattered light, the attenuation correction processing algorithm of the OCT signals improves the visualization and the contrast of a choroidal image, the attenuation correction processing algorithm of the OCT signals comprises two steps of attenuation compensation and image contrast enhancement respectively, and in the OCT system, photoelectric signals interfered between a reference arm and a sample arm can be represented by the following formula:
Figure BDA0002578323890000032
where k is the wave number, the collected signal is divided into m wave numbers with equal interval, rho is the photoelectric conversion efficiency of OCT detector, and S [ k ]m]Refers to the radiation energy of the light source in the corresponding wave band, and Δ x is the optical path difference between the reference arm and the sample arm, RRAnd RSReflectance of the reference arm and sample arm, respectively;
interaction term H [ k ] between reference arm and sample armm]Obtained from the above formula (1):
Figure BDA0002578323890000033
obtaining a reflectance profile function s (z) in the depth direction by performing inverse discrete fourier transform on the above expression (2):
Figure BDA0002578323890000034
discretizing the formula, and performing attenuation correction compensation on the OCT signal of each pixel:
Figure BDA0002578323890000041
where N is the number of pixels of the a scan, and α can be adjusted according to the tissue, the premise that the above equation (3) holds is that it is assumed that most of the beam energy is attenuated within the imaging depth range, and the attenuation outside the imaging depth range is negligible.
Performing exponentiation operation on the original signal subjected to attenuation correction compensation to enhance image contrast, wherein the signal intensity of each pixel is as follows:
Figure BDA0002578323890000042
s in formula (5)ac(z) is the attenuation corrected signal.
The invention has the beneficial effects that: the invention provides a choroid OCT image enhancement method and device based on signal reverse compensation.
Drawings
FIG. 1 is a flow chart of the technical solution of the present invention.
Fig. 2 is OCT fundus image acquisition and pre-processing.
FIG. 3 is a schematic diagram of choroidal signal attenuation correction and image contrast de-enhancement; the left image is the original image, and the right image is the image after attenuation correction and artifact removal.
FIG. 4 is a comparison of the inner and outer boundaries of a conventional dynamic programming algorithm and a deep learning automatic segmentation choroid; wherein the left image is the result of the traditional algorithm, and the right image is the result of the deep learning algorithm.
FIG. 5 is a diagram of adaptive threshold separation of choroidal vessels from non-vessels; wherein the upper image is high myopia, and the lower image is emmetropia.
Fig. 6 is a diagram of choroidal vessels after three-dimensional reconstruction.
Detailed Description
The invention will be better explained with reference to the drawings and some specific embodiments.
(1) Data acquisition and preprocessing:
the fundus image is acquired using current commercial instruments or self-constructed OCT, and the image is pre-processed, including appropriate cropping, leaving an OCT choroidal intensity map, as shown in figure 2.
(2) Reverse attenuation compensates for choroidal signals:
the signal received by the OCT detector is a backscattered and reflected signal. Due to the fact that loss occurs in light scattering of a certain wavelength caused by the influence of RPE and choroid self-pigment on light absorption, by extracting the attenuation principle and rule of choroid scattered light, a signal compensation and enhancement algorithm is constructed, and visualization and contrast of a choroid image can be improved.
The attenuation correction processing algorithm of the OCT signal comprises two steps of attenuation compensation and image enhancement contrast of light respectively. In an OCT system, the photoelectric signal of the interference between the reference arm and the sample arm can be represented by the following formula:
Figure BDA0002578323890000051
where k is the wave number, the collected signal is divided into m wave numbers with equal interval, rho is the photoelectric conversion efficiency of OCT detector, and S [ k ]m]Refers to the radiation energy of the light source in the corresponding wave band, and Δ x is the optical path difference between the reference arm and the sample arm, R RAnd RSThe reflectivity of the reference arm and the sample arm, respectively. Interaction term H [ k ] between reference arm and sample armm]Can be obtained by the formula (1):
Figure BDA0002578323890000052
the reflectance profile function s (z) in the depth direction can be obtained by performing inverse discrete fourier transform on equation (2):
Figure BDA0002578323890000053
while equations (1) - (3) hold the assumption that no energy attenuation occurs in the beam during propagation. However, the energy attenuation of the light beam occurs during the propagation process, so we need to improve the above formula. When the light beam penetrates the tissue sample, a small portion is converted to heat and the remainder is scattered, where the light beam is attenuated in the form of absorption. Assuming that the local attenuation of the beam is only related to the scattering of light and is backscattered with a fixed proportion, it is possible to calculate that the local attenuation of the beam at depth Z is proportional to the reflectivity R and the backscatter constant α at that location.
In practical situation, after the direct data processing of OCT, the attenuated signal D is obtainedAInstead of the original signal s (z). This is a main cause of occurrence of a dark shadow in strongly attenuated tissues such as blood vessels and pigments, i.e., a cause of occurrence of an artifact. To correct forThe signal is attenuated and the attenuation term needs to be eliminated, while the artifacts are also removed. Discretizing the formula, and performing attenuation correction compensation on the OCT signal of each pixel point:
Figure BDA0002578323890000061
Where N is the number of pixels of the A-scan, and α can be adjusted according to the organization. The above equation holds if it is assumed that most of the beam energy is attenuated within the imaging depth range, and the attenuation outside the imaging depth range is negligible.
In the later stage, the invention performs exponentiation operation on the original signal after attenuation correction compensation to enhance the image contrast, and the signal intensity of each pixel is as follows:
Figure BDA0002578323890000062
in the formula Sac(z) is the attenuation corrected signal, as shown in FIG. 3.
(3) Deep learning intelligent segmentation:
accurate identification of the inner and outer choroidal boundaries is an important step in accurately quantifying three-dimensional choroidal vascular indicators.
The suprachoroidal boundary is defined as the boundary between Bruch's membrane and the Retinal Pigment Epithelium (RPE), and the infrachoroidal boundary is defined as the boundary between the choroid and the sclera. Because the RPE layer is represented as a high signal band in the OCT image, the automatic division of the suprachoroidal boundary can be well realized based on the traditional shortest path graph theory algorithm; however, the contrast difference of the boundary between the choroid and the sclera in the OCT image makes it difficult to automatically segment the lower boundary by the shortest path graph theory algorithm. Deep learning is the most important breakthrough in the field of artificial intelligence, and makes a great breakthrough in the field of computer vision, and the efficiency of the algorithm based on deep learning is obviously better than that of the traditional algorithm. The invention establishes a deep learning segmentation model to realize automatic segmentation of the upper and lower choroidal boundaries, and comprises the following specific steps:
Semi-automatically labeling the inner and outer boundaries of the choroid of thousands of OCT images based on a traditional shortest path graph theory algorithm, and accurately describing the upper and lower boundaries of the choroid in an image; the labeled choroidal image set was as 8: 2, randomly dividing the training set and the testing set.
Inputting the training image into an open-source deep learning neural network model, setting a training optimization algorithm as a random gradient descent method (SGD), setting an algorithm learning ratio to be 1.0e-5, setting an iteration momentum to be 0.9, setting an iteration cost function to be a Dice coefficient (dicecoefficient), setting an iteration pass number (epoch) to be 150, and setting a batch sample size (batch size) to be 8. In addition, necessary enhancement processing (such as translation, rotation, and inversion) is performed on the input image, and the robustness of the model is improved. Wherein, the Dice coefficient calculation formula is as follows:
Figure BDA0002578323890000071
x denotes a choroidal boundary prediction set, Y denotes a choroidal boundary labeling set, | X | + | Y | denotes an intersection or overlap between the two sets, | X | + | Y | denotes the total amount of the two. The larger the Dice coefficient is, the higher the similarity of the two sets is, and the more accurate the model is; when the prediction set and the annotation set are completely the same, the Dice coefficient is 1; when the prediction set is not correlated with the annotation set, the Dice coefficient is 0. In the model training process, a Dice coefficient greater than 0.95 is set as a target function.
Secondly, inputting the images of the test set into the deep learning neural network model, and calculating a Dice coefficient and a boundary error between the output result of the choroid boundary and the labeling set to evaluate the segmentation performance of the deep learning neural network model. The results are shown in FIG. 4 and Table 1.
TABLE 1 efficiency of deep learning and segmentation of OCT image choroid boundary
Figure BDA0002578323890000072
(4) Adaptive thresholding to separate choroidal vessels from non-vessels: choroidal vessels and stroma exhibit different characteristics in OCT images, with vessels dominated by low-intensity signals and stroma dominated by high-intensity signals. Since the brightness of the choroidal signal in the OCT image is affected by the RPE layer, the type of instrument, the focusing condition during operation, and other relevant factors, the separation of choroidal vessels and stroma based on the fixed threshold method is theoretically limited and has poor universality. In contrast, interference caused by overall shift of the brightness of the choroidal signal can be better avoided based on the adaptive threshold, and automatic separation of choroidal vessels and stroma can be better realized, and the flow of the method is as follows:
in the OCT image, a square frame having a length and a width of 2 × w +1 pixel blocks is used as a local window. The coordinate (x, y) is the geometric center of the square frame, the luminance information of all pixel blocks in the frame is counted, and the mean value m (x, y) and the variance s (x, y) are obtained. According to the parameter k, an in-frame threshold value T (x, y) can be obtained, and the calculation formula is as follows:
T(x,y)＝m(x,y)+k*s(x,y) (7)
Secondly, according to the threshold value T (x, y) in the frame, all pixel blocks in the frame can be subjected to binarization processing, as follows:
Figure BDA0002578323890000081
where i and j are relative coordinates characterizing the relative geometric center (x, y) of the pixel block, Ax+i,y+jRepresenting the luminance of a block of pixels with coordinates (x + i, y + j), Ax+i,y+jBinarized data representing a pixel block of coordinates (x + i, y + j).
And thirdly, sequentially moving the local windows to realize binarization processing of the brightness matrix A of all pixel blocks in the OCT image so as to obtain a binarization matrix B. The results are shown in FIG. 5.
(5) Three-dimensional overall and each region quantification index:
only characteristic information on a certain cross section of the choroid can be acquired based on a single OCT image, but the disease progression of the disease is difficult to accurately evaluate by a single OCT image analysis mode due to the uneven spatial distribution of certain choroid lesions. The invention images the eyeground in a radial scanning mode to obtain choroid three-dimensional space data, and performs registration and reconstruction on the image to realize choroid three-dimensional space reconstruction. In the OCT image, the fovea centralis is taken as a reference point, the position of the fovea centralis is horizontally translated to ensure that the horizontal coordinates of the fovea centralis in the space coordinate in all the OCT images are the same, the three-dimensional reconstruction of the image is realized, and the overall quantitative index and each area quantitative index which can represent choroidal ischemia are calculated according to the distribution and the proportion of blood vessels in the three-dimensional space in the image. The method comprises the following specific steps:
Acquisition of an image: imaging is carried out by taking the fovea of the fundus macula as a center in a radial scanning mode, and m pieces of choroid two-dimensional cross section data are obtained.
Marking characteristic points: since all OCT images contain the feature of the fovea maculata, the present invention marks the fovea maculata as a feature point of the OCT images for subsequent image registration.
Image registration: the fovea centralis in all OCT images has the same abscissa in space coordinates by horizontally translating the position of the fovea centralis, so that the images are registered.
Reconstructing a choroid three-dimensional space: extracting a choroid region template M (x, y, z) in the registered image based on the upper and lower choroid boundaries obtained by the deep learning neural network model, and automatically separating blood vessels and matrixes by combining an adaptive threshold method to obtain a binarized three-dimensional space choroid structure matrix V (x, y, z).
Establishing and testing indexes: indexes such as choroidal vessel volume CVV, choroidal non-vessel volume SV, choroidal vessel index CVI and choroidal ischemia index CII are established for quantification. The calculation formula of each index is as follows:
Figure BDA0002578323890000091
Figure BDA0002578323890000092
Figure BDA0002578323890000093
Figure BDA0002578323890000101
wherein P isx，Py，PzRespectively, characterize the physical geometry of a voxel in three-dimensional space along the x, y and z axes. n, m and k represent the number of pixels along the x, y and z axes, respectively, in the three-dimensional spatial matrix.
The invention tests the repeatability of the index by repeatedly carrying out OCT imaging on the same subject for 2 times by the same operator, and compares the repeatability with the two-dimensional corresponding index, and the test result is shown in the following table.
0-6mm Left side 3-6mm Left side 1-3mm 0-1mm Right side 1-3mm The right side is 3-6mm
CVV Is perpendicular to 0.995 0.989 0.987 0.985 0.963 0.964
Level of 0.994 0.981 0.98 0.99 0.96 0.989
Three-dimensional 0.998 0.997 0.998 0.997 0.998 0.999
CVI Is perpendicular to 0.883 0.736 0.815 0.814 0.714 0.731
Level of 0.865 0.664 0.798 0.864 0.786 0.541
Three-dimensional 0.991 0.986 0.965 0.975 0.983 0.982
CII Is perpendicular to 0.883 0.736 0.815 0.814 0.714 0.731
Level of 0.865 0.664 0.798 0.864 0.786 0.541
Three-dimensional 0.991 0.986 0.965 0.975 0.983 0.982
SV Is perpendicular to 0.968 0.86 0.962 0.871 0.927 0.934
Level of 0.935 0.694 0.964 0.939 0.91 0.768
Three-dimensional 0.994 0.977 0.989 0.990 0.994 0.992
Therefore, the repeatability of the three-dimensional index is obviously superior to that of the two-dimensional index. In the two-dimensional indexes, the repeatability of the vertical two-dimensional index is superior to that of the horizontal two-dimensional index.
After preprocessing a choroid image signal acquired by a system, establishing a reverse signal attenuation compensation model based on the idea of reverse compensation of an OCT (optical coherence tomography) backscattering signal, reversely compensating the choroid signal, enhancing the signal-to-noise ratio between blood vessels and non-blood vessel tissues, and further performing intelligent segmentation on the inner and outer boundaries of the choroid by adopting a segmentation method based on deep learning; on the basis of boundary segmentation, an improved self-adaptive threshold method is further adopted to automatically separate out three-dimensional choroidal vessels and non-vascular tissues, and the overall quantitative index and each region quantitative index capable of representing choroidal ischemia are calculated according to the distribution and the proportion of the vessels in the three-dimensional space in an image. The method is suitable for all OCT systems which have high penetration on tissues and can acquire choroid and images thereof, and has the advantages of automation, strong universality and high-precision reflection of choroid three-dimensional blood vessel abnormality.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.

Claims (4)

1. A choroidal OCT image enhancement method based on signal inverse compensation is characterized by comprising the following steps:
(1) and (3) acquiring data: acquiring a fundus image by using OCT, preprocessing the image including appropriate cutting, and reserving an OCT choroid intensity map;
(2) reverse attenuation compensates for choroidal signals: by extracting the attenuation principle and rule of the scattered light of the choroid, a signal compensation and enhancement algorithm is constructed, and the visualization and the contrast of the choroid image are improved.
2. The method for enhancing the choroidal OCT image based on inverse signal compensation according to claim 1, wherein said step (2) of inversely attenuating the compensated choroidal signal comprises the steps of: by extracting the attenuation principle and the attenuation rule of the choroidal scattered light, the attenuation correction processing algorithm of the OCT signals improves the visualization and the contrast of a choroidal image, the attenuation correction processing algorithm of the OCT signals comprises two steps of attenuation compensation and image contrast enhancement respectively, and in the OCT system, photoelectric signals interfered between a reference arm and a sample arm can be represented by the following formula:
Figure FDA0002578323880000011
Where k is the wave number, the collected signal is divided into m wave numbers with equal interval, rho is the photoelectric conversion efficiency of OCT detector, and S [ k ]m]Refers to the radiation energy of the light source in the corresponding wave band, and Δ x is the optical path difference between the reference arm and the sample arm, RRAnd RSReflectance of the reference arm and sample arm, respectively;
interaction term H [ k ] between reference arm and sample armm]Obtained from the above formula (1):
Figure FDA0002578323880000012
obtaining a reflectance profile function s (z) in the depth direction by performing inverse discrete fourier transform on the above expression (2):
Figure FDA0002578323880000013
discretizing the formula, and performing attenuation correction compensation on the OCT signal of each pixel:
Figure FDA0002578323880000014
where N is the number of pixels of the a scan, and α can be adjusted according to the tissue, the premise that the above equation (3) holds is that it is assumed that most of the beam energy is attenuated within the imaging depth range, and the attenuation outside the imaging depth range is negligible.
Performing exponentiation operation on the original signal subjected to attenuation correction compensation to enhance image contrast, wherein the signal intensity of each pixel is as follows:
Figure FDA0002578323880000021
s in formula (5)ac(z) is the attenuation corrected signal.
3. A choroidal OCT image enhancement device based on signal inverse compensation is characterized by comprising the following modules,
an input module: for inputting an OCT choroidal intensity map;
An image processing module: constructing a signal compensation and enhancement algorithm by extracting a choroid scattered light attenuation principle and rule for improving visualization and contrast of a choroid image;
an output module: for outputting the image after removing the artifact by the attenuation correction.
4. The apparatus as claimed in claim 1, wherein the image processing module comprises the following algorithm models: by extracting the attenuation principle and the attenuation rule of the choroidal scattered light, the attenuation correction processing algorithm of the OCT signals improves the visualization and the contrast of a choroidal image, the attenuation correction processing algorithm of the OCT signals comprises two steps of attenuation compensation and image contrast enhancement respectively, and in the OCT system, photoelectric signals interfered between a reference arm and a sample arm can be represented by the following formula:
Figure FDA0002578323880000022
where k is the wave number and the acquired signal is divided into m equally spaced wavesNumber, rho is the photoelectric conversion efficiency of the OCT probe, Skm]Refers to the radiation energy of the light source in the corresponding wave band, and Δ x is the optical path difference between the reference arm and the sample arm, RRAnd RSReflectance of the reference arm and sample arm, respectively;
interaction term H [ k ] between reference arm and sample arm m]Obtained from the above formula (1):
Figure FDA0002578323880000023
obtaining a reflectance profile function s (z) in the depth direction by performing inverse discrete fourier transform on the above expression (2):
Figure FDA0002578323880000031
discretizing the formula, and performing attenuation correction compensation on the OCT signal of each pixel:
Figure FDA0002578323880000032
where N is the number of pixels of the a scan, and α can be adjusted according to the tissue, the premise that the above equation (3) holds is that it is assumed that most of the beam energy is attenuated within the imaging depth range, and the attenuation outside the imaging depth range is negligible.
Performing exponentiation operation on the original signal subjected to attenuation correction compensation to enhance image contrast, wherein the signal intensity of each pixel is as follows:
Figure FDA0002578323880000033
s in formula (5)ac(z) is the attenuation corrected signal.
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