Method for measuring the tear river
By instilling fluorescein onto the surface of the eye and utilizing image processing technology, the problem of measuring tear height has been solved, achieving accurate and rapid measurement results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- E SWIN DEV
- Filing Date
- 2021-02-18
- Publication Date
- 2026-06-12
Smart Images
Figure CN115397306B_ABST
Abstract
Description
[0001] This specification refers to application FR 19 10129 filed with INPI on September 13, 2019, in the name of the applicant, and patent application FR19 10131 filed with INPI on September 13, 2019, in the name of the applicant. Technical Field
[0002] This invention relates to the field of ophthalmic examination, and more specifically, to the control of eye lubrication, and even more specifically, to the measurement of the height of the tear duct. Background Technology
[0003] The tear river is the accumulation of tears at the interface between the cornea and the lower eyelid.
[0004] Observing the tear stream is one of the examinations that physicians usually perform in the differential diagnosis of dry eye syndrome.
[0005] Assessing the height of this tear river is particularly advantageous: it represents the total tear volume.
[0006] This assessment is routinely conducted using white light, employing slit lamps or image visualization / acquisition systems with cameras, lenses, and lighting, and, routinely, the tear river is interpreted visually.
[0007] The River of Tears is essentially watery and transparent.
[0008] When observed under white light, image formation involves the superposition of two effects:
[0009] Visualization of the structures existing behind the River of Tears
[0010] The air-tear refractive interface provides specular reflection of the light source, and by definition, the air-tear refractive interface resembles a mirror and forms a clear line at the tear river.
[0011] Technical issues
[0012] The problem is that the tear trough forms a concave surface between the cornea and the eyelid, meaning that not all light reflected from the surface of the tear trough near the eyelid is directed towards the lens aimed at the eye. This makes visualization of certain areas of the tear trough difficult. In fact, specular reflection is only possible if the tangent of the tear trough is oriented to reflect light into the lens's pupil. Obviously, this is only possible for a small portion of the tear trough. Therefore, the sharp line observed in this case does not indicate the size of the tear trough.
[0013] The areas of eye structure observed through the tear duct are difficult to explain.
[0014] Therefore, these known solutions do not allow for a direct measurement of the height of the River of Tears. Summary of the Invention
[0015] In view of the prior art, a method is proposed for measuring tear rivers using fluorescein concentrated in the tear river to make it fluoresce, the method comprising the following operations:
[0016] - Instill fluorescein onto the surface of the patient's eye to be examined.
[0017] - Illuminate the eye to be examined with blue light and capture an image of the eye, the image including a non-fluorescent blue region in the absence of fluorescein and a fluorescent green region in the presence of fluorescein.
[0018] And the image analysis includes:
[0019] - Identify the tear river and measure its height (h) in pixels.
[0020] - Identify the iris and measure its outer diameter (d) in pixels.
[0021] - Calculate the ratio of the physical outer diameter (D) of the iris, estimated in millimeters or otherwise measured, to its measurement in pixels (d) (R = D / d), and calculate the physical height of the tear river (H = Rxh) based on this ratio.
[0022] The advantage of this method is that it allows for the rapid and reliable measurement of the actual total height of the Tears River.
[0023] The estimated physical outer diameter of the iris can be a diameter based on an average value associated with the patient's eye type. For this purpose, a table of values associated with parameters such as eye color and type can be integrated into the software associated with the method. Alternatively, the physical outer diameter of the iris can be measured elsewhere and its value entered into the calculation software associated with the method.
[0024] Identifying tear rivers may include comparing the green to blue ratio of pixels in an image with a first determined threshold.
[0025] A threshold between 0.8 and 1.3 can be used, and more precisely, a threshold of approximately 0.95.
[0026] The method may include an operation to exclude dark areas, the operation being designed to eliminate those pixels whose green and / or blue levels are below a second determined threshold.
[0027] This makes it possible to eliminate potential anomalies in dark areas, such as green and blue levels approaching zero and the green / blue ratio becoming uncertain.
[0028] In one embodiment, the method includes segmenting an image of the identified fluorescent region, and more specifically, transforming the image into a binary image, assigning a first binary level to pixels that are predominantly blue and a second binary level to pixels that are predominantly green, or classifying the pixels of the image according to a threshold regarding the green / blue ratio.
[0029] Binary images allow for the identification of fluorescent regions and the detection of tear streams.
[0030] The method may include algorithms for searching connected components and eliminating small objects in the transformed image to further improve the detection of tear rivers.
[0031] The method may include an algorithm for applying a closure operator to the transformed image to eliminate small local defects without moving the contours of regions at a given binary level, which makes the next step of computing the regression polynomial easier.
[0032] A particularly advantageous embodiment is to use an algorithm for computing a regression polynomial, the algorithm comprising:
[0033] - Detect and store vertical segments in each column of the image that are perpendicular to the general direction of the tear river, the segments including a starting point generated by a transition from a first binary level to a second binary level, and including an ending point generated by a transition from the second level to the first level, the vertical segments representing the fluorescent tear river.
[0034] -Based on the transition of the upper part of the tear river, which is defined by the starting points of segments corresponding to the transition from the non-fluorescent to the fluorescent region, the first regression polynomial of the upper boundary line is calculated.
[0035] -Based on the transition of the lower part of the tear river, which is defined by the endpoints of segments corresponding to the transition from the fluorescent region to the non-fluorescent region, the second regression polynomial of the lower boundary line is calculated.
[0036] The least squares method can be used to calculate the regression polynomial.
[0037] The method may then include calculating the height (h) of the tear river in pixels by calculating the distance between regression polynomials calculated for each of the first and second lines by moving down along the columns of the image between the upper and lower lines.
[0038] According to one embodiment for simplifying measurements, the method may include selecting a region of interest around the lower eyelid of the patient's eye after imaging in order to reduce computation time and detection errors.
[0039] Depending on the chosen instillation method, the method may include a delay suitable for waiting for excess tear fluid containing the fluorescein to be reabsorbed via the lacrimal duct between the instillation and measurement of the fluorescein.
[0040] If fluorescein is instilled as drops, it is usually sufficient within one to several minutes for excess fluid introduced into the eye to be flushed out through the tear ducts.
[0041] Another subject is a computer program that includes instructions for implementing the above methods when the program is executed by a processor.
[0042] Additional subject is a computer-readable non-volatile storage medium on which a program is stored, the program being used to implement the above methods when the program is executed by a processor.
[0043] Another subject is an ophthalmic measurement device suitable for implementing the above methods and comprising: an ophthalmic measurement apparatus having a blue light source arranged around the lens of at least one camera; a computer having a user interface and programmed to drive the source and the at least one camera, and implementing the methods, wherein the computer and the interface are integrated into or external to the ophthalmic measurement apparatus and connected to the ophthalmic measurement apparatus. Attached Figure Description
[0044] Other features, details, and advantages of the invention will become apparent after reading the following detailed description and analyzing the accompanying drawings, in which:
[0045] [ Figure 1 [Demonstrate an apparatus suitable for implementing the method described;]
[0046] [ Figure 2 Show details of the steps in the method;
[0047] [ Figure 3A A photograph of the eye displayed in the context of the method described;
[0048] [ Figure 3B ]exhibit Figure 3A An illustration of the photograph;
[0049] [ Figure 4A Displayed after selection Figure 3A Details;
[0050] [ Figure 4B The display is based on Figure 4A binary image;
[0051] [ Figure 4C [Image showing the steps involved in generating the regression polynomial;]
[0052] [ Figure 5 [] is a schematic diagram representing the measured data;
[0053] [ Figure 6 [A schematic diagram illustrating the method described;]
[0054] [ Figure 7 This diagram illustrates a method for measuring the outer diameter of the iris applicable to the present invention. Detailed Implementation
[0055] The figures and descriptions below primarily contain elements with defined properties. Therefore, they are not only helpful for a better understanding of the invention, but also, where appropriate, for defining the invention.
[0056] Now for reference Figure 1 The figure illustrates an apparatus suitable for the method of the present invention, the apparatus including an ophthalmic measuring device 1, the ophthalmic measuring device having a lens 31 pointing to one or two cameras 30 positioned in front of the ophthalmic measuring device and having a blue light source 20, specifically at 470 nm.
[0057] As an example, the light source is four blue light-emitting diodes surrounding the lens of each camera, referred to below as blue LEDs.
[0058] The device further includes a computing device, such as a computer 3, which includes a screen 4, a keyboard 5 which may also be a touch-sensitive portion of the screen, a central processing unit having RAM, ROM and mass storage memory, as well as programs necessary for performing the method and an interface suitable for driving the light source and camera.
[0059] The computer can be external to or directly integrated into the ophthalmic measurement device, for example, behind the camera as described in patent application FR19 10131 filed with INPI on September 13, 2019.
[0060] Method based on Figure 2 Begin by instilling fluorescein into the eye or both eyes to be examined.
[0061] Fluorescein is routinely used to detect areas of damage to the bulbar conjunctiva or cornea where it temporarily binds. Its persistence is quite low, and the amount bound to the surface is also quite small.
[0062] Fluorescein is miscible with water. Therefore, tears are loaded with fluorescein, and this fluorescein eventually enters the tear duct, causing the tear duct to fluoresce.
[0063] Fluorescein can be instilled as droplets, and after a delay of several minutes, the additional fluid volume introduced by the fluorescein droplets is flushed out through the lacrimal duct. However, there is still enough fluorescein to label the tear volume, which then fluoresces under blue light.
[0064] Fluorescein can also be administered via any other means, such as by means of strip dripping, and in this case, there is no excess liquid beforehand, and the measurement can be performed without delay using the method presented above.
[0065] The image of the eye 10, visible under blue light, after the infusion was then captured by the corresponding camera. Figure 3A The visible image 100 includes fluorescent tear streams 12 that appear green. The image further includes an iris 11, an upper eyelid 13, a lower eyelid 14, and eyelashes 15, which backscatter incident light and therefore appear blue.
[0066] Based on the schematic display of image 100 Figure 3B The blue tint 101 is schematically represented by a grid pattern, and the fluorescent green 102 is schematically represented by dashed lines. Therefore, the tear river is clearly visible and well-defined. The color contrast between the fluorescent green area and the non-fluorescent blue area is high.
[0067] In the optional step, the area of interest 130 is drawn around the location of the tear duct. To do this, the physician roughly outlines the area of the tear duct on their screen. This avoids performing tear duct detection calculations on abnormal areas that might be selected by the software. For example, if there is a large amount of fluorescein on the eyelid.
[0068] Comparison with a threshold regarding the green / blue ratio makes it possible to at least partially identify the tear river.
[0069] exist Figure 3A In an image, the green / blue ratio is extremely important for a given pixel. In the absence of fluorescein, incident blue light is backscattered. The intensity of green is very low, even zero, and blue is dominant. In the presence of fluorescein, the green / blue ratio tends towards 0 because blue light is absorbed and re-emitted as green. Figure 4A As shown, in the bright and saturated tear region of fluorescent region 12a, the green / blue ratio is high, or even tends to infinity.
[0070] Figure 6 The computer program illustrating the method schematically then includes algorithm 220, which generates a binary image of the region to be considered, assigning binary value 221 to the non-fluorescent portion and binary value 222 to the fluorescent portion. Two types of thresholds can be used:
[0071] Sensitivity: This is a threshold related to the green / blue ratio.
[0072] Exclude dark areas: This threshold allows pixels to be eliminated when the maximum green and blue values are below the threshold: the green / blue ratio is not adequately defined in extremely dark images (low green and blue levels). Typically, on dark skin, anything may be detected on the skin, and therefore fluorescence may be detected where it is not present.
[0073] A threshold is suggested to the operator by default, and the operator can adjust the threshold, for example, based on the patient.
[0074] Therefore, what is obtained is Figure 4B The binary image, where the white parts are fluorescent areas.
[0075] In the next step 230, the method includes an algorithm for searching the connected components and eliminating small objects 231 outside the tear river in the transformed image. Small objects are pixels or groups of pixels whose shape makes it possible to determine that they are not part of the tear river and therefore should be excluded from subsequent image analysis. Specifically, fluorescent areas that are not part of the tear river may exist, for example on the bulbar conjunctiva or on the eyelid, and this algorithm makes it possible to exclude said fluorescent areas due to their shape (area in pixels, width / height aspect ratio, or other characteristics).
[0076] Next, the method may include the following step 240: applying a closure operator to the binary image, which makes it possible to fill the holes without moving the contours. For example, this step makes it possible to transform non-fluorescent areas 241 in significantly larger fluorescent areas into fluorescent areas. This operation is particularly useful if dust, bubbles, or any other contaminants are present in the fluorescent areas of the tear stream.
[0077] Step 250 includes calculating a regression polynomial for the upper and lower contours of the Tears River.
[0078] Therefore, based on the preceding binary image, a defined vertical segment will exist in each column of the image. By convention, the vertical direction is considered to be perpendicular to the generally horizontal direction of the tear duct, or the direction passing through the two eyes of the seated patient.
[0079] The binary image is viewed as a black-and-white image in which white defines the fluorescent region, and vertical segments are defined as follows:
[0080] - Its starting point, which is the transition from black to white at the starting point of the fluorescent region;
[0081] - Its endpoint, which is the transition from white to black at the end of the fluorescent region.
[0082] The complete set of fragment starting points gives the upper portion of the binary image, i.e., the transition line from non-fluorescent to fluorescent regions. The complete set of fragment ending points gives the lower portion of the binary image, i.e., the transition line from fluorescent to non-fluorescent regions.
[0083] For each of the upper and lower lines, the least squares method is used to compute the regression polynomial. This gives... Figure 4C Line 103 of the upper polynomial and line 104 of the lower polynomial. Polynomial regression allows for subpixel resolution and makes it possible to overcome transformation position quantization noise, as well as local anomalies in the image: such as noise, dust, or polyps on the eyelids. The distance between the top and bottom polynomials is, in principle, filtered by natural filtering.
[0084] A polynomial is advantageously a quartic polynomial so as to follow the curvature of the River of Tears.
[0085] Next, in step 260, the distance between two polynomials in a given column of the image is calculated to obtain the height of the tear river in pixels. Figure 5 In this procedure, the location used for measurement is chosen as a vertical position passing through the center of the iris, but the physician may choose other locations, possibly depending on their requirements.
[0086] It should be noted that if the image does not include objects smaller than the determined threshold, the steps of eliminating small objects 230 and applying the closure operator 240 may be omitted, so that the process can proceed directly from generating the binary image to calculating the regression polynomial, as follows. Figure 6 The text is described by the dashed line 270.
[0087] The second part of the method may include automatically detecting the outer diameter of the iris, measured in pixels.
[0088] In this case, the method includes the iris detection steps described in application FR 1910129 filed on September 13, 2019, in the name of the applicant. Figure 7 The iris detection process may include:
[0089] - The image undergoes a first transformation via an anisotropic bandpass filter 400 applied in the direction of the eye's width to produce a pair of transitions from dark to light and from light to dark along the horizontal axis of the eye.
[0090] - Segment the image 410 to identify rising and falling transition pairs that form segments that necessarily represent bright areas in the image;
[0091] - The image is filtered 420, which removes light fragments from the central area of the pattern as well as the top and bottom areas of the image;
[0092] - Considering the light fragments, other areas of the image are no longer considered in this analysis. The first operation 430 calculates the RMS circle of the periphery of the iris based on the right end of the light fragment on the left side of the image and the left end of the light fragment on the right side of the image.
[0093] - Step 440: Remove points that are too far from the RMS circle; and
[0094] - Regarding the remaining points, a new step involves calculating the RMS circle to follow the contour of the iris. Based on the RMS circle, the outer diameter of the iris at 450° is calculated. For example, calculating the maximum distance in pixels between the edges of the circle will give the outer diameter d of the iris in pixels. Knowing the actual outer diameter of the iris makes it possible to calculate the actual height of the tear river by calculating the ratio R = D / d between the physical diameter D of the iris estimated or measured elsewhere and its pixel-level measurement d, and then calculating the physical height H = Rxh of the tear river based on this ratio.
[0095] This invention is not limited to the examples described above, but any variations that can be conceived by those skilled in the art within the scope of the claims, for example by modifying the order of certain operations or by removing or adding certain operations, to achieve greater computational speed or accuracy.
Claims
1. A method for measuring tear rivers, the method using a fluorescein concentrated in the tear river to make it fluoresce, the method being based on the analysis of an image obtained by performing the following operations: - Instill fluorescein onto the surface of the patient's eye (10) to be examined. - Illuminate the eye to be examined with blue light (10). Its features include: - Capture (300) an image (100) of the eye to be examined, the image including a non-fluorescent blue area (101) in the absence of fluorescein and a fluorescent green area (102) in the presence of fluorescein. - Identify the tear river (12) and measure its height (h) in pixels. - Identify the iris (11) and measure its outer diameter (d) in pixels. - Calculate the ratio (R = D / d) of the physical outer diameter (D) of the iris, as estimated or otherwise measured in millimeters, to its measurement (d) in pixels, and calculate the physical height of the tear river (H = Rxh) based on this ratio.
2. The measurement method according to claim 1, characterized in that, The estimated physical outer diameter of the iris is based on an average value associated with the patient's eye type.
3. The method according to claim 1 or 2, characterized in that, Identifying the tear river (12) includes comparing the green to blue ratio of the pixels in the image with a first determined threshold.
4. The method of claim 3, further comprising the operation of excluding dark areas, the operation being designed to eliminate those pixels whose green and / or blue levels are below a second determined threshold.
5. The method of claim 3, comprising an algorithm (220) for transforming the image into a binary image, assigning a first binary level (221) to pixels whose color is primarily blue and assigning a second binary level (222) to pixels whose color is primarily green, or classifying the pixels of the image according to a threshold regarding the green / blue ratio.
6. The method of claim 5, further comprising an algorithm (230) for searching connected components and eliminating small objects (231) in the transformed image.
7. The method of claim 5, further comprising an algorithm (240) for referencing a closure operator to the transformed image to eliminate small local defects without moving the contours of regions at a given binary level.
8. The method of claim 5, further comprising an algorithm (250) for calculating a regression polynomial, the algorithm comprising: - Detect and store vertical segments in each column of the image that are perpendicular to the general direction of the tear river, the segments including a starting point generated by a transition from a first binary level to a second binary level, and including an ending point generated by a transition from the second binary level to the first binary level, the vertical segments representing fluorescent tear rivers. -Based on the transition of the upper portion of the tear river, which is defined by the starting point of the segment corresponding to the transition from the non-fluorescent blue region to the fluorescent green region, the first regression polynomial of the upper boundary line (103) is calculated. -Based on the transition of the lower portion of the tear river, which is defined by the endpoints of the segments corresponding to the transition from the fluorescent green region to the non-fluorescent blue region, the second regression polynomial of the lower boundary line (104) is calculated.
9. The method according to claim 8, characterized in that, The regression polynomial is calculated using the least squares method.
10. The method of claim 8, further comprising calculating (260) the height (h) of the tear river in pixels by calculating (260) the distance between the regression polynomials calculated for each of the upper and lower lines by calculating (260) downward along the column of the image between the upper and lower lines.
11. The method of claim 1, further comprising selecting a region of interest (130) around the lower eyelid of the patient's eye to reduce computation time and detection errors.
12. The method of claim 1, further comprising a delay (35) adapted to allow for the reabsorption of excess tear fluid containing the fluorescein via the lacrimal duct between the instillation of the fluorescein and the measurement.
13. A computer program comprising instructions for implementing the method according to any one of claims 1 to 12 when executed by a processor.
14. A computer-readable non-volatile storage medium having a program stored thereon for implementing the method according to any one of claims 1 to 12 when executed by a processor.
15. An ophthalmic measuring device for implementing the method according to any one of claims 1 to 12, characterized in that, The ophthalmic measurement device includes: an ophthalmic measurement apparatus (1) having a blue light source (20) arranged around a lens (31) of at least one camera (30); a computer having a user interface and programmed to drive the source and the at least one camera, and to implement the method, the computer and the interface being integrated into or external to the ophthalmic measurement device and connected to the ophthalmic measurement device.