A smart OCT system based on dual-color LEDs

Abstract

This invention relates to an intelligent OCT system based on dual-color LEDs, comprising: a three-pin common-anode dual-color LED for emitting yellow light and near-infrared light to the eyeball to be detected; an eyeball position recognition camera for receiving light signals emitted by the three-pin common-anode dual-color LED and reflected along the signal arm and reference arm respectively, converting the light signals into an image, and determining the eyeball position based on the imaging position of the image; an iris imaging optical path module for splitting the light beam emitted by the three-pin common-anode dual-color LED, converting the light signals emitted by the three-pin common-anode dual-color LED and reflected along the signal arm and reference arm respectively into optical images, and transmitting the optical images to an iris recognition CCD to form a layer to be processed; the iris recognition CCD for receiving the optical images transmitted by the iris imaging optical path module, converting the optical images into digital images, and transmitting them to the host computer.

Description

Technical Field

[0001] This invention relates to the field of medical testing, and more particularly to an intelligent OCT system based on dual-color LEDs. Background Technology

[0002] Current ophthalmic optical coherence tomography (OCT) imaging systems use near-infrared LEDs as the illumination source. Near-infrared light illuminates the face, and the reflected light is received by an eye position recognition camera to generate an image. The computer calculates the eye position based on the image and controls the device's movement to roughly align it with the eye. After illuminating the iris, the reflected light passes through an iris imaging optical path system and enters an iris position recognition CCD. The iris position recognition CCD generates an image, and the computer calculates the iris position based on this image and controls the device's movement to precisely align it with the eye.

[0003] Before near-infrared light reaches the eye position recognition camera, most of it is filtered by the built-in infrared cutoff plate. Only a small portion of the near-infrared light can reach the camera, resulting in a relatively dark image that cannot be used for eye position calculation and recognition. Furthermore, using near-infrared light as illumination can easily cause discomfort to the test subject and does not meet the requirements for indicator light colors in medical device product standards.

[0004] Chinese patent CN102824161B provides an ophthalmic OCT system, including: a light source, a coupler, a reference arm, a sample arm, a spectrometer, and a computer processing unit. The light emitted from the focusing lens of the sample arm is incident on a light path switching mirror and reflected by the light path switching mirror. The light path switching mirror controls the emission angle of its emitted light through different rotation angles, so that its emitted light can be incident on the two-dimensional mirror unit through different light paths. The different light paths include light emitted directly from the light path switching mirror to the two-dimensional mirror unit and light reflected by the mirror unit before being incident on the two-dimensional mirror unit, thereby realizing a large-area OCT scan of the fundus. However, the problem of sensory stimulation to the test subject caused by near-infrared light irradiation has not yet been solved. Summary of the Invention

[0005] To address this issue, the present invention provides an intelligent OCT system based on dual-color LEDs, which can solve the technical problem of not being able to use yellow light to cover near-infrared light and reduce the exposure time of near-infrared light to the human eye.

[0006] To achieve the above objectives, the present invention provides an intelligent OCT system based on dual-color LEDs, comprising:

[0007] A three-pin common anode dual-color LED is used to emit yellow light and near-infrared light toward the face;

[0008] An eye position recognition camera, containing an infrared filter, is used to receive light signals emitted by a three-pin common anode dual-color LED and reflected by a human face, and convert the light signals into an image. The infrared filter intercepts near-infrared light emitted by the three-pin common anode dual-color LED, while the eye position recognition camera receives yellow light emitted by the three-pin common anode dual-color LED and images the face illuminated by the yellow light. Simultaneously, the face image is transmitted to a host computer. The host computer extracts feature points of facial organs and contours from the face image to determine the eye position, and transmits the eye position parameters to the eye position recognition camera so that the camera can readjust its focus and focal length to acquire an eye image including the cornea, iris, and pupil.

[0009] The iris imaging optical path module is used to split the beam emitted by the three-pin common anode dual-color LED and convert the near-infrared light signal emitted by the three-pin common anode dual-color LED and reflected by the face into an optical image. The optical image includes various tissue layers inside the eyeball. The iris imaging optical path module transmits each of the tissue layers inside the eyeball to the iris recognition CCD to form a layer to be processed.

[0010] The iris recognition CCD is used to receive the optical images transmitted by the iris imaging optical path module, and extract a layer to be processed containing the cornea, iris and pupil from the optical images as a contrast contour layer, and extract the layers to be processed of each tissue in the posterior segment of the eye. The iris recognition CCD converts the contrast contour layer and the layers to be processed into digital images and transmits them to the host computer.

[0011] Furthermore, the intelligent OCT system based on dual-color LED also includes a host computer, which is connected to the eye position recognition camera and the iris imaging optical path module respectively. The host computer acquires feature points of facial organs and contours in the face image to obtain eye position parameters. The host computer readjusts the focus and focal length of the eye position camera according to the acquired eye position to obtain eye images and then obtain pupil state. The host computer also adjusts the yellow light intensity of the three-pin common anode dual-color LED according to the pupil change when the eye to be detected receives light signals. When the pupil change meets the standard, the host computer extracts the anterior segment tissue image from the eye image and sharpens the anterior segment tissue image to be detected according to the pixel grayscale distribution of the image transmitted by the eye position recognition camera. The sharpened anterior segment tissue image is then positioned and scaled to use the contours of the cornea, iris and pupil as reference contours for stitching together the tissues to be detected in the eye to be detected.

[0012] The host computer combines the layer to be processed transmitted by the iris recognition CCD with the image of the anterior segment tissue after image processing to form a tomographic image of the eyeball to be processed. The layers to be processed are combined in accordance with the structure of the human eyeball. The host computer determines whether there are artifacts in the image by analyzing the distribution of pixel values ​​in each region of the tomographic image of the eyeball to be processed, and determines whether the iris imaging optical path module needs to reacquire the layers of each tissue inside the eyeball when the reference arm moves based on the number of artifacts.

[0013] Furthermore, the host computer sets the yellow light intensity corresponding to the first standard value I11 of the yellow light control current to illuminate the eyeball to be tested. The host computer acquires the image information transmitted by the eyeball position recognition camera in real time. Based on the comparison between the pupil change Δx after the illumination of the light beam and the standard value Δx0 of pupil constriction of the eyeball to be tested, the host computer determines whether to adjust the yellow light control current I1 of the three-pin common anode dual-color LED.

[0014] When Δx≤Δx0, the host computer does not control the current I1 of the three-pin common anode dual-color LED yellow light, and maintains I1=I11;

[0015] When Δx > Δx0, the host computer adjusts the control current I1 of the three-pin common anode dual-color LED yellow light so that I1 = (1 - (Δx - Δx0)). 0.5 / ((△x+△x0) 0.5 )×I11;

[0016] The host computer presets the first standard value of the control current for the yellow light of the three-pin common anode dual-color LED to be identified as I11, and presets the standard value of the pupil constriction to be identified as △x0.

[0017] Furthermore, when the host computer adjusts the control current of the three-pin common anode dual-color LED yellow light, it acquires the image and position information transmitted by the eye position recognition camera in real time. It automatically extracts the anterior segment tissue, including the cornea, iris, and pupil, using a shortest path optimization algorithm based on dynamic programming. The host computer acquires the pixels with the lowest grayscale in the contour regions of the cornea, iris, and pupil, and fits each into a smooth curve, which is then fitted into a closed circle. The host computer extracts the closed circles fitted to each contour region and obtains the set of pixels Pi, i = 1, 2, 3, within the path formed by the extension line of the radius of the closed circle and a distance s0 from both sides of the closed circle. The host computer adjusts the grayscale of each pixel in the set Pi according to the acquired image sharpness to achieve image sharpening.

[0018] When Amax - Amin ≤ 32, the host computer performs soft thresholding on the grayscale of each pixel in the image. The host computer sets the first grayscale threshold of the pixel to A1, where A1 = [Amax + Amin] / 2. The host computer adjusts the pixel values ​​in the pixel set Pi based on the comparison between the grayscale values ​​of each pixel in the pixel set Pi and the first grayscale threshold A1.

[0019] When a < A1, the host computer adjusts the gray value a of the pixels in the pixel set Pi to a1, so that a1 = Amin;

[0020] When a≥A1, the host computer adjusts the gray value a of the pixels in the pixel set Pi to a2, so that a2=Amax;

[0021] When 32 < Amax - Amin ≤ 128, the host computer groups each pixel according to the gray value of each pixel in the image. The gray value gradient range of the group is 32. The host computer takes the median of the gray value of each pixel in each group and adjusts the gray value of all pixels in each group to the median of the gray value of each pixel in its corresponding group.

[0022] Wherein, the host computer sets Amax as the maximum grayscale value of each pixel and Amin as the minimum grayscale value of each pixel. P1 represents the set of pixels within the path range of the intersection point s0 on both sides of the extended radius line of the circle formed by fitting the corneal contour region and the corresponding circular contour line. P2 represents the set of pixels within the path range of the intersection point s0 on both sides of the intersection point s0 on the extended radius line of the circle formed by fitting the iris contour region and the corresponding circular contour line. P3 represents the set of pixels within the path range of the intersection point s0 on both sides of the intersection point s0 on the extended radius line of the circle formed by fitting the pupil contour region and the corresponding circular contour line. [Amax+Amin] represents rounding down "Amax+Amin".

[0023] Furthermore, when the host computer determines that the pupil change meets the standard, it sharpens the image, identifies the pupil center point, and establishes a two-dimensional rectangular coordinate system with the pupil center as the origin. The host computer presets a rectangular imaging area as R0, and the geometric center of R0 coincides with the coordinate origin set by the host computer. The host computer determines whether to adjust the size of the acquired image based on the comparison between the image outline position transmitted by the eye position recognition camera and its preset standard imaging area.

[0024] When R∈R0, the host computer determines whether to magnify the original image based on the distance between the intersection of the outer contour of the imaging area of ​​the image transmitted by the eyeball position recognition camera and the coordinate axis of the two-dimensional rectangular coordinate system and the intersection of the standard imaging area R0 and the coordinate axis.

[0025] when At that time, the host computer scales the image according to the original image ratio, so that the point (x, y) closest to the edge of the imaging region R0 in the scaled image can satisfy |x|=|x0| and |y|≤0.8×|y0|, or satisfy |y|=|y0| and |x|≤0.8×|x0|;

[0026] The host computer sets R as the imaging area of ​​the image transmitted by the eye position recognition camera, and the coordinates of the intersection points of the preset standard imaging area R0 and the coordinate axes of the established two-dimensional rectangular coordinate system are (x0, 0), (0, -y0), (-x0, 0) and (0, y0), respectively.

[0027] Furthermore, when the imaging area of ​​the image transmitted by the eye position recognition camera falls within the standard imaging area R0 of the host computer, the host computer determines whether to magnify the original image based on the distance between the intersection of the outer contour of the imaging area transmitted by the eye position recognition camera and the coordinate axis of the two-dimensional rectangular coordinate system and the intersection of the standard imaging area R0 and the coordinate axis.

[0028] When |x0-x|≤0.2×x0 and y=y0=0, or |y0-y|≤0.2×y0 and x=x0=0, the host computer determines that the original image should not be enlarged;

[0029] When |x0-x|>0.2×x0 and y=y0=0, or |y0-y|>0.2×y0 and x=x0=0, the host computer determines to enlarge the original image so that the point (x, y) closest to the edge of the imaging region R0 in the enlarged image can satisfy |x|=|x0|, |y|≤0.8×|y0|, or |y|=|y0|, |x|≤0.8×|x0|.

[0030] Furthermore, after the host computer completes the positioning and image processing of the contour lines of each tissue in the anterior segment of the eye, it adjusts the three-pin common-anode dual-color LED to emit near-infrared light while maintaining the emission of yellow light. By controlling the near-infrared light control current, the host computer ensures that the intensity of the yellow light is significantly greater than that of the near-infrared light, thus covering the near-infrared light. Specifically, when the yellow light current value is greater than 1 / 3 of the near-infrared light current value, the host computer determines that the yellow light intensity is significantly greater than the near-infrared light intensity, thus covering the near-infrared light. The host computer sets the first limit endpoint of the reference arm to N1 and the second limit endpoint to N2. The reference arm moves from the first limit endpoint to the second limit endpoint at a constant speed. The host computer determines whether it can acquire the layers of each tissue in the posterior segment of the eye obtained by the iris position recognition CCD during the movement of the reference arm, based on whether interference occurs between the light reflected from the signal arm and the reference arm.

[0031] When the iris imaging optical path module detects light interference, the host computer acquires the relevant layers at the time of light interference.

[0032] When the iris imaging optical path module does not detect light interference, the host computer determines that there is an optical path difference between the reference arm and the signal arm, does not acquire the relevant layers, and takes optical path compensation measures for the iris imaging optical path module.

[0033] Further, the host computer extracts the layer to be processed, placing it in the same two-dimensional Cartesian coordinate system as the processed anterior segment tissue image. The host computer sets the corneal edge contour line in the anterior segment tissue image as the edge red line of the layer to be processed, and performs image processing on the layer to be processed by translation, rotation, and scaling according to the structure of the human eye, so that the tomographic image to be processed and the anterior segment tissue image are combined in a manner consistent with the structure of the human eye to form the tomographic image of the eye to be processed. The host computer sets the origin of the two-dimensional Cartesian coordinate system as the first endpoint, any point on the corneal edge contour line as the second endpoint, and the line segment connecting the first and second endpoints as the scan line. The host computer sets the scan line to rotate around the first endpoint as the rotation center, sets α as the rotation angle, and sets a first threshold C1 for the pixel values ​​on the scan line based on the normal distribution of the pixel values ​​during rotation. The host computer acquires the pixel values ​​on the scan line and the corresponding coordinates of each pixel point during the rotation, and determines whether artifacts exist in the scan area of ​​the tomographic image of the eye to be processed based on the pixel values ​​C at different positions of the scan line during rotation.

[0034] When C≤C1, the host computer determines that there are no artifacts in the scanning area of ​​the ocular tomographic image to be processed;

[0035] When C > C1, the host computer determines that there are artifacts in the scanning area of ​​the ocular tomographic image to be processed.

[0036] Furthermore, when the host computer determines that artifacts exist in the scanning area of ​​the ocular tomographic image to be processed, the host computer sets a second threshold C2 for the pixel value on the scan line. The host computer determines the type of artifact present in the scanning area of ​​the ocular tomographic image to be processed based on the comparison between the pixel value C at different positions of the scan line during rotation and the second threshold C2 for the pixel value on the scan line.

[0037] When C1 < C ≤ C2, the host computer determines that there is a linear artifact in the scanned area;

[0038] When C > C2, the host computer determines that there is a ring artifact in the scanned area.

[0039] Furthermore, the host computer sets a threshold value of K for the number of linear artifacts in the scanning area and a threshold value of Q for the number of annular artifacts. The host computer determines whether to recapture and extract the tomographic image of the eyeball to be processed based on the comparison between the detected number of artifacts and their corresponding threshold values.

[0040] When k < K and q < Q, the host computer determines that the ocular tomographic image to be processed meets the detection standard and can be used for medical diagnosis and treatment;

[0041] When k≥K or q≥Q, the host computer determines that the to-be-processed ocular tomographic image does not meet the detection standard and needs to be recaptured and extracted.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention connects a current-limiting resistor in series in the near-infrared light pin of the dual-color LED, and through the circuit system, the yellow light of the dual-color LED is first turned on, which can obtain a bright face image, making it easy to extract facial organs and contour feature points, and is conducive to accurately obtaining the position parameters of the eyeballs, thereby adjusting the focus and focal length of the eyeball recognition camera to obtain a magnified and clear image of the frontal segment; when the near-infrared light is turned on and the yellow light and near-infrared light are turned on at the same time, the host computer controls the current to make the brightness of the yellow light much higher than that of the near-infrared light and completely cover the near-infrared light. At this time, the test subject can only observe the yellow light and cannot feel the presence of the near-infrared light, reducing the sensory stimulation of the near-infrared light on the test subject; at the same time, it can also meet the requirements of the medical device product standards regarding the color of the indicator light.

[0043] In particular, near-infrared light itself can generate a thermal effect, allowing the human eye to feel the heat generated by near-infrared light. Prolonged exposure to near-infrared light can also damage the human eye. Therefore, this invention chooses to first use relatively soft yellow light to irradiate the eyeball and obtain preliminary positional parameters of some structures of the eyeball based on the obtained positional relationship of the cornea, iris, and pupil contours. Since yellow light is visible light, it will cause changes in the pupil when irradiating the human eye. Because the light-sensing ability of the test subjects' eyes is not consistent, a more appropriate light source intensity is selected according to the changes in their pupils to ensure that the test subjects' eyes are in a more comfortable and relatively stable state during the test, making the test process smoother.

[0044] In particular, since the images acquired by the host computer after yellow light illumination are relatively soft, the contrast and brightness of the anterior segment tissues in the image cannot meet the positioning requirements. The host computer sharpens the anterior segment tissue contour lines by adjusting the grayscale of the pixels in the contour regions of each tissue. When the difference between the maximum and minimum grayscale of the pixels in the contour region is small, threshold soft processing can directly achieve grayscale polarization, realize grayscale jump, and thus improve the contrast of the contour region. When the difference between the maximum and minimum grayscale of the pixels in the contour region is large, grayscale grouping and the concentration of grayscale in each group can achieve grayscale jump while avoiding image distortion and loss of too much image detail information.

[0045] In particular, the grayscale difference that the human eye can generally recognize is 32. Therefore, when the grayscale difference of the contour area of ​​the anterior segment tissue is less than 32, by setting the median value between the maximum and minimum grayscale values ​​of the pixel as the first grayscale threshold of the pixel, the grayscale of the pixel can be polarized. This is beneficial for distinguishing the contour line of the anterior segment tissue, thereby achieving image sharpening. This makes the positional parameters of the anterior segment tissue more accurate, which is beneficial for making the planar positional parameters of each structure of the eye more accurate when the internal structure of the eyeball and the anterior segment tissue are combined into a whole.

[0046] In particular, this invention establishes a two-dimensional rectangular coordinate system in the anterior segment tissue image to represent the positional parameters of each point of the eye tissue in the form of coordinates, which is more intuitive and clear, and is conducive to detection and targeted examination. This invention sets a standard imaging area, which can ensure the integrity of the eye plane image and avoid the omission of some detailed information due to the small size of the acquired image. Setting a standard imaging area indirectly sets a standard for subsequent imaging of the internal structure of the eye.

[0047] In particular, this invention sets the origin of the two-dimensional rectangular coordinate system to coincide with the center point of the standard imaging area. The outer contour of the imaging area transmitted by the eye position recognition camera is the corneal contour line. The corneal contour line also serves as the outer contour line of the anterior segment tissue and internal structure of the eyeball in the planar image. By adjusting the size of the imaging area transmitted by the eye position recognition camera, the imaging size of the cornea, iris, and pupil can be adjusted to facilitate medical observation and help the testing personnel judge the state of various structures of the eyeball.

[0048] In particular, this invention utilizes the principle of light interference to make the signal arm and reference arm in the OCT system the same length. By changing the length of the reference arm, the interference phenomenon that occurs when the light reflected by the signal arm and reference arm has the same frequency when the optical path is equal is used to image the various layers of the internal structure of the eyeball. The host computer of this invention sets up optical path compensation measures to avoid the existence of optical path difference between the light beams reflected by the reference arm and the signal arm due to external objective factors, which would cause omission of eyeball structural layers, making the acquired detection image more complete and improving the reference value of the detection image.

[0049] In particular, this invention sets the scan line rotation angle to α. Since the structures of different regions of the internal tissue layer plane of the eyeball are different, the peak pixel values ​​of each region may vary greatly. Dividing different regions according to the structure of the internal tissue layer plane of the eyeball can avoid misjudging artifacts. According to the normal distribution of pixel values ​​on the scan line at different positions, the host computer of this invention can easily extract abnormal pixel peak values, thereby determining the presence of artifacts and realizing the filtering of effective information in the detected image.

[0050] In particular, the generation of artifacts is mostly unavoidable. The most common are linear artifacts and ring artifacts. Linear artifacts are caused by the beam hardening effect, which results in higher energy reflected beams, causing a relatively obvious fluctuation in the peak pixel value in that area. Ring artifacts are mostly caused by sudden changes in the motion state of the test subject, which leads to displacement of the test object and increases the amount of imaging data. Therefore, the peak pixel value in that area will fluctuate significantly. Distinguishing between ring artifacts and linear artifacts is helpful in determining the cause of the artifacts and in determining whether the acquired detection image is usable.

[0051] In particular, when the number of artifacts exceeds the detection standard, the image quality is easily degraded, or even impossible to analyze. It can also cover up lesions, causing missed diagnoses or false lesions, leading to misdiagnosis. This invention sets thresholds for the number of linear artifacts and ring artifacts, which can help analyze why the tomographic images of the eyeballs being processed do not meet the detection standard. When there are too many linear artifacts, the light beam can be processed by adding a filter. When there are too many ring artifacts, the eyeballs of the test subject are kept in a relatively static state, which can prevent the test subject from making misjudgments about the internal structure of the eyeball due to artifacts. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the intelligent OCT system based on dual-color LEDs according to an embodiment of the present invention.

[0053] Figure 2 This is a schematic diagram of the pixel points of the anterior segment tissue contour region in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0055] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0056] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0057] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0058] Please see Figure 1 As shown, it is a schematic diagram of the intelligent OCT system structure based on dual-color LEDs according to an embodiment of the present invention, including:

[0059] A three-pin common anode dual-color LED is used to emit yellow light and near-infrared light toward the face;

[0060] An eye position recognition camera includes an infrared filter to receive light signals emitted by a three-pin common anode dual-color LED and reflected by a human face, and converts the light signals into an image. The infrared filter intercepts the near-infrared light emitted by the three-pin common anode dual-color LED. The eye position recognition camera receives yellow light emitted by the three-pin common anode dual-color LED and images the face illuminated by the yellow light. The face image is then transmitted to a host computer. The host computer extracts feature points of facial organs and contours from the face image to determine the eye position and transmits the eye position parameters to the eye position recognition camera. This allows the eye position recognition camera to readjust its focus and focal length to acquire an eye image including the cornea, iris, and pupil.

[0061] The iris imaging optical path module is used to split the beam emitted by the three-pin common anode dual-color LED and convert the near-infrared light signal emitted by the three-pin common anode dual-color LED and reflected by the face into an optical image. The optical image includes various tissue layers inside the eyeball. The iris imaging optical path module transmits each of the tissue layers inside the eyeball to the iris recognition CCD to form a layer to be processed.

[0062] The iris recognition CCD is used to receive the optical images transmitted by the iris imaging optical path module, and extract a layer to be processed containing the cornea, iris and pupil from the optical images as a contrast contour layer, and extract the layers to be processed of each tissue in the posterior segment of the eye. The iris recognition CCD converts the contrast contour layer and the layers to be processed into digital images and transmits them to the host computer.

[0063] The intelligent OCT system based on dual-color LEDs also includes a host computer, which is connected to the eye position recognition camera and the iris imaging optical path module. The host computer acquires feature points of facial organs and contours in the face image to obtain eye position parameters. The host computer readjusts the focus and focal length of the eye position camera according to the acquired eye position to obtain an eye image and then obtain the pupil state. The host computer also adjusts the yellow light intensity of the three-pin common anode dual-color LED according to the pupil change when the eye receives light signals. When the pupil change meets the standard, the host computer extracts the anterior segment tissue image from the eye image and sharpens the anterior segment tissue image according to the pixel grayscale distribution of the image transmitted by the eye position recognition camera. The sharpened anterior segment tissue image is then positioned and scaled to use the contours of the cornea, iris, and pupil as reference contours for stitching together the tissue to be detected in the eye.

[0064] The host computer combines the layer to be processed transmitted by the iris recognition CCD with the image of the anterior segment tissue after image processing to form a tomographic image of the eyeball to be processed. The layers to be processed are combined in accordance with the structure of the human eyeball. The host computer determines whether there are artifacts in the image by analyzing the distribution of pixel values ​​in each region of the tomographic image of the eyeball to be processed, and determines whether the iris imaging optical path module needs to reacquire the layers of each tissue inside the eyeball when the reference arm moves based on the number of artifacts.

[0065] Specifically, this invention connects a current-limiting resistor in series with the near-infrared light pin of a dual-color LED. The circuit system first activates the yellow light in the dual-color LED, enabling the acquisition of a bright facial image. This facilitates the extraction of facial organs and contour features, and helps to accurately obtain eye position parameters, thereby adjusting the eye position recognition camera's focus and focal length to obtain a magnified and clear image of the anterior segment. When the near-infrared light is activated, simultaneously activating both the yellow and near-infrared lights, the host computer controls the current to ensure the yellow light's brightness is significantly higher than the near-infrared light, completely covering it. At this point, the test subject can only observe the yellow light and cannot perceive the near-infrared light, reducing sensory stimulation from the near-infrared light. This also meets the requirements for indicator light colors in medical device product standards.

[0066] The host computer sets the yellow light intensity corresponding to the first standard value I11 of the yellow light control current to illuminate the eyeball to be tested. The host computer acquires the image information transmitted by the eyeball position recognition camera in real time. Based on the comparison between the pupil change Δx after the illumination of the light beam and the standard value Δx0 of pupil constriction, the host computer determines whether to adjust the yellow light control current I1 of the three-pin common anode dual-color LED.

[0067] When Δx≤Δx0, the host computer does not control the current I1 of the three-pin common anode dual-color LED yellow light, and maintains I1=I11;

[0068] When Δx > Δx0, the host computer adjusts the control current I1 of the three-pin common anode dual-color LED yellow light so that I1 = (1 - (Δx - Δx0)). 0.5 / ((△x+△x0) 0.5 )×I11;

[0069] The host computer presets the first standard value of the control current for the yellow light of the three-pin common anode dual-color LED to be identified as I11, and presets the standard value of the pupil constriction to be identified as △x0.

[0070] Specifically, the present invention does not limit △x0. Under normal circumstances, the diameter of the human eye pupil is in the range of 2.5mm to 4mm. After being stimulated by external light, the range of pupil diameter change is 1.5 to 8mm, that is, the maximum value of the human eye pupil shrinkage can be 1mm. In the embodiment of the present invention, the host computer sets the standard value of pupil shrinkage to be identified as △x0 = 0.3mm.

[0071] Specifically, near-infrared light itself can generate a thermal effect, allowing the human eye to feel the heat generated by near-infrared light. Prolonged exposure to near-infrared light can also damage the human eye. Therefore, this invention chooses to first use relatively soft yellow light to illuminate the eyeball and obtain preliminary positional parameters of some structures of the eyeball based on the obtained positional relationship of the cornea, iris, and pupil contours. Since yellow light is visible light, it will cause changes in the pupil when it is irradiated. Because the light-sensing ability of the test subjects' eyes is not consistent, a more appropriate light source intensity is selected according to the changes in their pupils to ensure that the test subjects' eyes are in a more comfortable and relatively stable state during the test, making the test process smoother.

[0072] When the host computer adjusts the control current of the three-pin common anode dual-color LED yellow light, it acquires the image and position information transmitted by the eye position recognition camera in real time. It automatically extracts the anterior segment tissue, including the cornea, iris, and pupil, using a shortest path optimization algorithm based on dynamic programming. The host computer obtains the pixels with the lowest grayscale in the contour regions of the cornea, iris, and pupil, and fits each into a smooth curve, which is then fitted into a closed circle. The host computer extracts the closed circles fitted to each contour region and obtains the set of pixels Pi, i = 1, 2, 3, within the path formed by the extension line of the radius of the closed circle and a distance s0 from both sides of the closed circle. The host computer adjusts the grayscale of each pixel in the set Pi according to the acquired image sharpness to achieve image sharpening.

[0073] When Amax - Amin ≤ 32, the host computer performs soft thresholding on the grayscale of each pixel in the image. The host computer sets the first grayscale threshold of the pixel to A1, where A1 = [Amax + Amin] / 2. The host computer adjusts the pixel values ​​in the pixel set Pi based on the comparison between the grayscale values ​​of each pixel in the pixel set Pi and the first grayscale threshold A1.

[0074] When a < A1, the host computer adjusts the gray value a of the pixels in the pixel set Pi to a1, so that a1 = Amin;

[0075] When a≥A1, the host computer adjusts the gray value a of the pixels in the pixel set Pi to a2, so that a2=Amax;

[0076] When 32 < Amax - Amin ≤ 128, the host computer groups each pixel according to the gray value of each pixel in the image. The gray value gradient range of the group is 32. The host computer takes the median of the gray value of each pixel in each group and adjusts the gray value of all pixels in each group to the median of the gray value of each pixel in its corresponding group.

[0077] Wherein, the host computer sets Amax as the maximum grayscale value of each pixel and Amin as the minimum grayscale value of each pixel. P1 represents the set of pixels within the path range of the intersection point s0 on both sides of the extended radius line of the circle formed by fitting the corneal contour region and the corresponding circular contour line. P2 represents the set of pixels within the path range of the intersection point s0 on both sides of the intersection point s0 on the extended radius line of the circle formed by fitting the iris contour region and the corresponding circular contour line. P3 represents the set of pixels within the path range of the intersection point s0 on both sides of the intersection point s0 on the extended radius line of the circle formed by fitting the pupil contour region and the corresponding circular contour line. [Amax+Amin] represents rounding down "Amax+Amin".

[0078] Specifically, grayscale refers to the color depth of a point in a black and white image, typically ranging from 0 to 255, with white being 255 and black being 0.

[0079] Please see Figure 2 As shown, it is a schematic diagram of the pixel points of the anterior segment tissue contour region in an embodiment of the present invention, including a first edge line 11 of the corneal contour region, a corneal fitted circular contour line 12, a second edge line 13 of the corneal contour region, the intersection of the extended line 4 of the circular radius formed by fitting the corneal contour region and the first edge line of the corneal contour region, the straight-line distance between the intersection of the extended line 4 of the circular radius formed by fitting the corneal contour region and the corneal fitted circular contour line is s0, the intersection of the extended line 4 of the circular radius formed by fitting the corneal contour region and the second edge line of the corneal contour region, the straight-line distance between the intersection of the extended line 4 of the circular radius formed by fitting the corneal contour region and the corneal fitted circular contour line is s0;

[0080] It also includes a first edge line 21 of the iris contour region, a circular contour line 22 of the iris fitting, a second edge line 23 of the iris contour region, the intersection of the extended line of the radius of the circle formed by fitting the corneal contour region and the first edge line of the iris contour region, the straight-line distance between the intersection of the extended line of the radius of the circle formed by fitting the corneal contour region and the circular contour line of the iris fitting is s0, and the straight-line distance between the intersection of the extended line of the radius of the circle formed by fitting the corneal contour region and the second edge line of the iris contour region and the intersection of the extended line of the radius of the circle formed by fitting the corneal contour region and the circular contour line of the iris fitting is s0.

[0081] It also includes a first edge line 31 of the pupil contour region, a pupil fitted circular contour line 32, a second edge line 33 of the pupil contour region, the intersection of the extended line 4 of the circular radius formed by fitting the corneal contour region and the first edge line of the pupil contour region, the straight-line distance between the intersection of the extended line of the circular radius formed by fitting the corneal contour region and the pupil fitted circular contour line is s0, and the straight-line distance between the intersection of the extended line of the circular radius formed by fitting the corneal contour region and the second edge line of the pupil contour region and the intersection of the extended line of the circular radius formed by fitting the corneal contour region and the pupil fitted circular contour line is s0.

[0082] Specifically, since the image acquired by the host computer after yellow light illumination is relatively soft, the contrast and brightness of the anterior segment tissues in the image cannot meet the positioning requirements. The host computer sharpens the anterior segment tissue contour lines by adjusting the grayscale of the pixels in the contour regions of each tissue. When the difference between the maximum and minimum grayscale of the pixels in the contour region is small, threshold soft processing can directly achieve grayscale polarization, realize grayscale jump, and thus improve the contrast of the contour region. When the difference between the maximum and minimum grayscale of the pixels in the contour region is large, grayscale grouping and the concentration of grayscale in each group can achieve grayscale jump while avoiding image distortion and loss of too much image detail information.

[0083] When the host computer determines that the pupil change meets the standard, it sharpens the image, identifies the pupil center point, and establishes a two-dimensional rectangular coordinate system with the pupil center as the origin. The host computer presets a rectangular imaging area as R0, and the geometric center of R0 coincides with the coordinate origin set by the host computer. The host computer determines whether to adjust the size of the acquired image based on the comparison between the image outline position transmitted by the eye position recognition camera and its preset standard imaging area.

[0084] When R∈R0, the host computer determines whether to magnify the original image based on the distance between the intersection of the outer contour of the imaging area of ​​the image transmitted by the eyeball position recognition camera and the coordinate axis of the two-dimensional rectangular coordinate system and the intersection of the standard imaging area R0 and the coordinate axis.

[0085] when At that time, the host computer scales the image according to the original image ratio, so that the point (x, y) closest to the edge of the imaging region R0 in the scaled image can satisfy |x|=|x0| and |y|≤0.8×|y0|, or satisfy |y|=|y0| and |x|≤0.8×|x0|;

[0086] The host computer sets R as the imaging area of ​​the image transmitted by the eye position recognition camera, and the coordinates of the intersection points of the preset standard imaging area R0 and the coordinate axes of the established two-dimensional rectangular coordinate system are (x0, 0), (0, -y0), (-x0, 0) and (0, y0), respectively.

[0087] Specifically, the grayscale difference that the human eye can generally recognize is 32. Therefore, when the grayscale difference of the contour area of ​​the anterior segment tissue is less than 32, by setting the median value between the maximum and minimum grayscale values ​​of the pixel as the first grayscale threshold of the pixel, the grayscale of the pixel can be polarized. This is beneficial for distinguishing the contour line of the anterior segment tissue, thereby achieving image sharpening. This makes the positional parameters of the anterior segment tissue more accurate, which is beneficial for making the planar positional parameters of each structure of the eye more accurate when the internal structure of the eyeball and the anterior segment tissue are combined into a whole.

[0088] When the imaging area of ​​the image transmitted by the eye position recognition camera falls within the standard imaging area R0 of the host computer, the host computer determines whether to magnify the original image based on the distance between the intersection of the outer contour of the imaging area transmitted by the eye position recognition camera and the coordinate axis of the two-dimensional rectangular coordinate system and the intersection of the standard imaging area R0 and the coordinate axis.

[0089] When |x0-x|≤0.2×x0 and y=y0=0, or |y0-y|≤0.2×y0 and x=x0=0, the host computer determines that the original image should not be enlarged;

[0090] When |x0-x|>0.2×x0 and y=y0=0, or |y0-y|>0.2×y0 and x=x0=0, the host computer determines to enlarge the original image so that the point (x, y) closest to the edge of the imaging region R0 in the enlarged image can satisfy |x|=|x0|, |y|≤0.8×|y0|, or |y|=|y0|, |x|≤0.8×|x0|.

[0091] Specifically, the edge of the human cornea can be approximated as a circle. When the host computer makes the center of the pupil in the image transmitted by the eye position recognition camera, that is, the origin of the two-dimensional rectangular coordinate system, coincide with the geometric center of the preset imaging area R0, the shortest path of the outer contour lines of the two areas is located on the coordinate axis of the two-dimensional rectangular coordinate system, and the two endpoints of the shortest path are the intersection points of the outer contours of the two areas with the coordinate axis respectively.

[0092] Specifically, this invention establishes a two-dimensional rectangular coordinate system in the image of the anterior segment of the eye, representing the positional parameters of each point in the eyeball in coordinate form, which is more intuitive and clear, facilitating detection and targeted examination. The invention sets a standard imaging area, ensuring the integrity of the planar image of the eyeball and preventing the omission of detailed information due to an excessively small image size. Setting a standard imaging area indirectly establishes a standard for subsequent imaging of the internal structures of the eyeball. The invention sets the origin of the two-dimensional rectangular coordinate system to coincide with the center point of the standard imaging area. The outer contour of the imaging area transmitted by the eyeball position recognition camera is the corneal contour line, which also serves as the outer contour line of the anterior segment of the eye and the internal structures of the eyeball in the planar image. By adjusting the size of the imaging area transmitted by the eyeball position recognition camera, the imaging size of the cornea, iris, and pupil is adjusted, making it easier for medical observation and facilitating the assessment of the state of various structures of the eyeball by the examiners.

[0093] After the host computer completes the positioning and image processing of the contour lines of each tissue in the anterior segment of the eye, it adjusts the three-pin common-anode dual-color LED to emit near-infrared light while maintaining the emission of yellow light. By controlling the near-infrared light control current, the host computer ensures that the intensity of the yellow light is significantly greater than that of the near-infrared light, thus covering the near-infrared light. Specifically, when the yellow light current value is greater than 1 / 3 of the near-infrared light current value, the host computer determines that the yellow light intensity is significantly greater than the near-infrared light intensity, thus covering the near-infrared light. The host computer sets the first extreme endpoint of the reference arm to N1 and the second extreme endpoint to N2. The reference arm moves from the first extreme endpoint to the second extreme endpoint at a constant speed. The host computer determines whether it can acquire the layers of each tissue in the posterior segment of the eye obtained by the iris position recognition CCD during the movement of the reference arm based on whether interference occurs between the light reflected from the signal arm and the reference arm.

[0094] When the iris imaging optical path module detects light interference, the host computer acquires the relevant layers at the time of light interference.

[0095] When the iris imaging optical path module does not detect light interference, the host computer determines that there is an optical path difference between the reference arm and the signal arm, does not acquire the relevant layers, and takes optical path compensation measures for the iris imaging optical path module.

[0096] Specifically, the present invention does not limit the optical path compensation measures. The embodiments of the present invention provide a preferred optical path compensation measure, namely, adding a light-transmitting mirror on the optical axis of the signal arm to achieve optical path compensation.

[0097] Specifically, this invention utilizes the principle of light interference to make the signal arm and reference arm in the OCT system the same length. By changing the length of the reference arm, the interference phenomenon that occurs when the light reflected by the signal arm and reference arm has the same frequency when the optical path is equal is used to image the various layers of the internal structure of the eyeball. The host computer of this invention sets up optical path compensation measures to avoid optical path difference between the light beams reflected by the reference arm and the signal arm due to external objective factors, which could lead to the ocular structural layers being missed. This results in a more complete detection image and improves the reference value of the detection image.

[0098] The host computer extracts the layer to be processed, placing it in the same two-dimensional Cartesian coordinate system as the processed anterior segment tissue image. The host computer sets the corneal edge contour line in the anterior segment tissue image as the edge red line of the layer to be processed, and performs image processing on the layer to be processed by translation, rotation, and scaling according to the structure of the human eye, combining the tomographic image and the anterior segment tissue image in a manner consistent with the structure of the human eye to form the tomographic image of the eye to be processed. The host computer sets the origin of the two-dimensional Cartesian coordinate system as the first endpoint, any point on the corneal edge contour line as the second endpoint, and the line segment connecting the first and second endpoints as the scan line. The host computer sets the scan line to rotate around the first endpoint as the rotation center, sets α as the rotation angle, and sets a first threshold C1 for the pixel values ​​on the scan line based on the normal distribution of the pixel values ​​during rotation. The host computer acquires the pixel values ​​on the scan line and the corresponding coordinates of each pixel point during the rotation, and determines whether artifacts exist in the scan area of ​​the tomographic image of the eye to be processed based on the pixel values ​​C at different positions of the scan line during rotation.

[0099] When C≤C1, the host computer determines that there are no artifacts in the scanning area of ​​the ocular tomographic image to be processed;

[0100] When C > C1, the host computer determines that there are artifacts in the scanning area of ​​the ocular tomographic image to be processed.

[0101] Specifically, the present invention does not limit the rotation angle α of the scan line. α serves as the partitioning standard for the scan area, and partitioning is performed based on the structure of the layer to be processed in the eyeball itself.

[0102] Specifically, this invention sets the scan line rotation angle to α. Since the structures of different regions of the internal tissue layer plane of the eyeball are different, the peak pixel values ​​of each region may vary greatly. Dividing different regions according to the structure of the internal tissue layer plane of the eyeball can avoid misjudging artifacts. Based on the normal distribution of pixel values ​​on the scan line at different positions, the host computer of this invention can easily extract abnormal pixel peak values, thereby determining the presence of artifacts and achieving the filtering of effective information in the detected image.

[0103] When the host computer determines that artifacts exist in the scanning area of ​​the ocular tomographic image to be processed, it sets a second threshold C2 for the pixel values ​​on the scan line. The host computer then determines the type of artifact in the scanning area of ​​the ocular tomographic image to be processed based on the comparison between the pixel values ​​C at different positions on the scan line during rotation and the second threshold C2.

[0104] When C1 < C ≤ C2, the host computer determines that there is a linear artifact in the scanned area;

[0105] When C > C2, the host computer determines that there is a ring artifact in the scanned area.

[0106] Specifically, the generation of artifacts is mostly unavoidable. The most common are linear artifacts and ring artifacts. Linear artifacts are caused by the beam hardening effect, which results in higher energy reflected light beams, causing a relatively obvious fluctuation in the peak pixel value in that area. Ring artifacts are mostly caused by sudden changes in the motion state of the test subject, which leads to displacement of the test object and increases the amount of imaging data. Therefore, the peak pixel value in that area will fluctuate significantly. Distinguishing between ring artifacts and linear artifacts is helpful in determining the cause of the artifacts and in determining whether the acquired detection image is usable.

[0107] The host computer sets a threshold value of K for the number of linear artifacts and a threshold value of Q for the number of annular artifacts in the scanning area. The host computer determines whether to recapture and extract the ocular tomographic image to be processed based on a comparison between the detected number of artifacts and their corresponding threshold values.

[0108] When k < K and q < Q, the host computer determines that the ocular tomographic image to be processed meets the detection standard and can be used for medical diagnosis and treatment;

[0109] When k≥K or q≥Q, the host computer determines that the to-be-processed ocular tomographic image does not meet the detection standard and needs to be recaptured and extracted.

[0110] Specifically, the causes of artifacts in the detected image are concentrated in the sudden changes in the motion state of the eyeball being detected. Therefore, the way to reduce artifacts is to keep the eyeball in a relatively static state.

[0111] Specifically, when the number of artifacts exceeds the detection standard, it can easily lead to a decrease in image quality, or even make analysis impossible. It can also mask lesions, causing missed diagnoses or false lesions, leading to misdiagnosis. This invention sets thresholds for the number of linear artifacts and ring artifacts, which can help analyze why the tomographic images of the eyeball being processed do not meet the detection standard. When there are too many linear artifacts, a filter can be added to process the light beam. When there are too many ring artifacts, the eyeball of the test subject is kept in a relatively static state, which can avoid the test subject from making misjudgments about the internal structure of the eyeball due to artifacts.

[0112] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A smart OCT system based on dual-color LEDs, characterized in that, include: A three-pin common anode dual-color LED is used to emit yellow light and near-infrared light toward the face; An eye position recognition camera includes an infrared filter to receive light signals emitted by a three-pin common anode dual-color LED and reflected by a human face, and converts the light signals into an image. The infrared filter intercepts the near-infrared light emitted by the three-pin common anode dual-color LED. The eye position recognition camera receives yellow light emitted by the three-pin common anode dual-color LED and images the face illuminated by the yellow light. The face image is then transmitted to a host computer. The host computer extracts feature points of facial organs and contours from the face image to determine the eye position and transmits the eye position parameters to the eye position recognition camera. This allows the eye position recognition camera to readjust its focus and focal length to acquire an eye image including the cornea, iris, and pupil. The host computer adjusts the three-pin common anode dual-color LED to emit near-infrared light while maintaining the emission of yellow light. By controlling the near-infrared light control current, the intensity of the yellow light is made much greater than the intensity of the near-infrared light so that the yellow light covers the near-infrared light. When the yellow light current value is greater than 1 / 3 of the near-infrared light current value, the host computer determines that the intensity of the yellow light is much greater than the intensity of the near-infrared light so that the yellow light covers the near-infrared light. The iris imaging optical path module is used to split the beam emitted by the three-pin common anode dual-color LED and convert the near-infrared light signal emitted by the three-pin common anode dual-color LED and reflected by the face into an optical image. The optical image includes various tissue layers inside the eyeball. The iris imaging optical path module transmits each of the tissue layers inside the eyeball to the iris recognition CCD to form a layer to be processed. The iris recognition CCD is used to receive the optical images transmitted by the iris imaging optical path module, and extract a layer to be processed containing the cornea, iris and pupil from the optical images as a contrast contour layer, and extract the layers to be processed of each tissue in the posterior segment of the eye. The iris recognition CCD converts the contrast contour layer and the layers to be processed into digital images and transmits them to the host computer.

2. The intelligent OCT system based on dual-color LEDs according to claim 1, characterized in that, The intelligent OCT system based on dual-color LEDs also includes a host computer, which is connected to the eye position recognition camera and the iris imaging optical path module. The host computer acquires feature points of facial organs and contours in the face image to obtain eye position parameters. The host computer readjusts the focus and focal length of the eye position camera according to the acquired eye position to obtain an eye image and then obtain the pupil state. The host computer also adjusts the yellow light intensity of the three-pin common anode dual-color LED according to the pupil change when the eye receives light signals. When the pupil change meets the standard, the host computer extracts the anterior segment tissue image from the eye image and sharpens the anterior segment tissue image according to the pixel grayscale distribution of the image transmitted by the eye position recognition camera. The sharpened anterior segment tissue image is then positioned and scaled to use the contours of the cornea, iris, and pupil as reference contours for stitching together the tissue to be detected in the eye. The host computer combines the layer to be processed transmitted by the iris recognition CCD with the image of the anterior segment tissue after image processing to form a tomographic image of the eyeball to be processed. The layers to be processed are combined in accordance with the structure of the human eyeball. The host computer determines whether there are artifacts in the image by analyzing the distribution of pixel values ​​in each region of the tomographic image of the eyeball to be processed, and determines whether the iris imaging optical path module needs to reacquire the layers of each tissue inside the eyeball when the reference arm moves based on the number of artifacts.

3. The intelligent OCT system based on dual-color LEDs according to claim 2, characterized in that, The host computer sets the yellow light intensity corresponding to the first standard value I11 of the yellow light control current to illuminate the face. The host computer acquires eye image information transmitted by the eye position recognition camera in real time. Based on the comparison between the pupil change Δx after the light beam illumination and the standard value Δx0 of pupil constriction, the host computer determines whether to adjust the yellow light control current I1 of the three-pin common anode dual-color LED. When Δx≤Δx0, the host computer does not control the current I1 of the three-pin common anode dual-color LED yellow light, and maintains I1=I11; When Δx > Δx0, the host computer adjusts the control current I1 of the three-pin common anode dual-color LED yellow light so that I1 = (1 - (Δx - Δx0)). 0.5 / ((△x+△x0) 0.5 )×I11; The host computer presets the first standard value of the control current for the yellow light of the three-pin common anode dual-color LED to be identified as I11, and presets the standard value of the pupil constriction to be identified as △x0.

4. The intelligent OCT system based on dual-color LEDs according to claim 3, characterized in that, When the host computer adjusts the control current of the three-pin common anode dual-color LED yellow light, it acquires the image and position information transmitted by the eye position recognition camera in real time. It automatically extracts the anterior segment tissue, including the cornea, iris, and pupil, using a shortest path optimization algorithm based on dynamic programming. The host computer obtains the pixels with the lowest grayscale in the contour regions of the cornea, iris, and pupil, and fits each into a smooth curve, which is then fitted into a closed circle. The host computer extracts the closed circles fitted to each contour region and obtains the set of pixels Pi, i=1, 2, 3, within the path formed by the extension line of the radius of the closed circle and a distance s0 from both sides of the closed circle. The host computer adjusts the grayscale of each pixel in the set Pi according to the acquired image sharpness to achieve image sharpening. When Amax - Amin ≤ 32, the host computer performs soft thresholding on the grayscale of each pixel in the image. The host computer sets the first threshold for pixel grayscale as A1, where A1 = [Amax + Amin] / 2. The host computer adjusts the pixel values ​​in the pixel set Pi based on the comparison between the grayscale values ​​of each pixel in the pixel set Pi and the first threshold A1. When a < A1, the host computer adjusts the gray value a of the pixels in the pixel set Pi to a1, so that a1 = Amin; When a≥A1, the host computer adjusts the grayscale value a of the pixels in the pixel set Pi to a2, so that a2=Amax; When 32 < Amax - Amin ≤ 128, the host computer groups each pixel according to the gray value of each pixel in the image. The gray value gradient range of the group is 32. The host computer takes the median of the gray value of each pixel in each group and adjusts the gray value of all pixels in each group to the median of the gray value of each pixel in its corresponding group. Wherein, the host computer sets Amax as the maximum grayscale value of each pixel, sets Amin as the minimum grayscale value of each pixel, P1 represents the set of pixels within the path range s0 on both sides of the intersection point of the extended radius line of the circle formed by fitting the corneal contour region and the corresponding circular contour line, P2 represents the set of pixels within the path range s0 on both sides of the intersection point of the extended radius line of the circle formed by fitting the iris contour region and the corresponding circular contour line, P3 represents the set of pixels within the path range s0 on both sides of the intersection point of the extended radius line of the circle formed by fitting the pupil contour region and the corresponding circular contour line, and [Amax+Amin] represents rounding down "Amax+Amin".

5. The intelligent OCT system based on dual-color LEDs according to claim 4, characterized in that, When the host computer determines that the pupil change meets the standard, it sharpens the image, identifies the pupil center point, and establishes a two-dimensional rectangular coordinate system with the pupil center as the origin. The host computer presets a rectangular imaging area as R0, and the geometric center of R0 coincides with the coordinate origin set by the host computer. The host computer determines whether to adjust the size of the acquired image based on the comparison between the image outline position transmitted by the eye position recognition camera and its preset standard imaging area. When R∈R0, the host computer determines whether to magnify the original image based on the distance between the intersection of the outer contour of the imaging area of ​​the image transmitted by the eyeball position recognition camera and the coordinate axis of the two-dimensional rectangular coordinate system and the intersection of the standard imaging area R0 and the coordinate axis. When R ∉ R0, the host computer scales the image according to the original image ratio, so that the point (x, y) in the scaled image that is closest to the edge of the imaging region R0 can satisfy |x|=|x0| and |y|≤0.8×|y0|, or |satisfy|=|y0| and |x|≤0.8×|x0|. The host computer sets R as the imaging area of ​​the image transmitted by the eye position recognition camera, and the coordinates of the intersection points of the preset standard imaging area R0 and the coordinate axes of the established two-dimensional rectangular coordinate system are (x0, 0), (0, -y0), (-x0, 0) and (0, y0), respectively.

6. The intelligent OCT system based on dual-color LEDs according to claim 5, characterized in that, When the imaging area of ​​the image transmitted by the eye position recognition camera falls within the standard imaging area R0 of the host computer, the host computer determines whether to magnify the original image based on the distance between the intersection of the outer contour of the imaging area transmitted by the eye position recognition camera and the coordinate axis of the two-dimensional rectangular coordinate system and the intersection of the standard imaging area R0 and the coordinate axis. When |x0-x|≤0.2×x0 and y=y0=0, or |y0-y|≤0.2×y0 and x=x0=0, the host computer determines that the original image should not be enlarged. When |x0-x|>0.2×x0 and y=y0=0, or |y0-y|>0.2×y0 and x=x0=0, the host computer determines to enlarge the original image so that the point (x, y) closest to the edge of the imaging region R0 in the enlarged image can satisfy |x|=|x0|, |y|≤0.8×|y0|, or |y|=|y0|, |x|≤0.8×|x0|.

7. The intelligent OCT system based on dual-color LEDs according to claim 6, characterized in that, After the host computer completes the positioning and image processing of the contour lines of each tissue in the anterior segment of the eye, it sets the first extreme endpoint of the reference arm as N1 and the second extreme endpoint as N2. The reference arm moves from the first extreme endpoint to the second extreme endpoint at a constant speed. The host computer determines whether it can acquire the layers of each tissue in the posterior segment of the eye obtained by the iris position recognition CCD during the movement of the reference arm based on whether interference occurs between the light reflected from the signal arm and the reference arm. When the iris imaging optical path module detects light interference, the host computer acquires the relevant layers at the time of light interference. When the iris imaging optical path module does not detect light interference, the host computer determines that there is an optical path difference between the reference arm and the signal arm, does not acquire the relevant layers, and takes optical path compensation measures for the iris imaging optical path module.

8. The intelligent OCT system based on dual-color LEDs according to claim 7, characterized in that, The host computer extracts the layer to be processed, placing it in the same two-dimensional Cartesian coordinate system as the processed anterior segment tissue image. The host computer sets the corneal edge contour line in the anterior segment tissue image as the edge red line of the layer to be processed, and performs image processing on the layer to be processed by translation, rotation, and scaling according to the structure of the human eye, combining the tomographic image and the anterior segment tissue image in a manner consistent with the structure of the human eye to form the tomographic image of the eye to be processed. The host computer sets the origin of the two-dimensional Cartesian coordinate system as the first endpoint, any point on the corneal edge contour line as the second endpoint, and the line segment connecting the first and second endpoints as the scan line. The host computer sets the scan line to rotate around the first endpoint as the rotation center, sets α as the rotation angle, and sets a first threshold C1 for the pixel values ​​on the scan line based on the normal distribution of the pixel values ​​during rotation. The host computer acquires the pixel values ​​on the scan line and the corresponding coordinates of each pixel point during the rotation, and determines whether artifacts exist in the scan area of ​​the tomographic image of the eye to be processed based on the pixel values ​​C at different positions of the scan line during rotation. When C≤C1, the host computer determines that there are no artifacts in the scanning area of ​​the ocular tomographic image to be processed; When C > C1, the host computer determines that there are artifacts in the scanning area of ​​the ocular tomographic image to be processed.

9. The intelligent OCT system based on dual-color LEDs according to claim 8, characterized in that, When the host computer determines that artifacts exist in the scanning area of ​​the ocular tomographic image to be processed, it sets a second threshold C2 for the pixel values ​​on the scan line. The host computer then determines the type of artifact in the scanning area of ​​the ocular tomographic image to be processed based on the comparison between the pixel values ​​C at different positions on the scan line during rotation and the second threshold C2. When C1 < C ≤ C2, the host computer determines that there is a linear artifact in the scanned area; When C > C2, the host computer determines that there is a ring artifact in the scanned area.

Images