METHOD FOR MEASURING A TEAR MENISCUS
Patent Information
- Authority / Receiving Office
- MX · MX
- Patent Type
- Patents
- Current Assignee / Owner
- E SWIN DEV
- Filing Date
- 2022-08-19
- Publication Date
- 2026-06-12
Smart Images
Figure MX435379B0
Abstract
Description
METHOD FOR MEASURING A TEAR MENISCUS The description refers to application FR 19 10129 filed on September 13, 2019 in the name of the applicant with the INPI, and to patent application FR 19 10131 filed on September 13, 2019 in the name of the applicant and with the INPI. TECHNICAL FIELD OF THE INVENTION The invention relates to the field of ophthalmological examinations and, specifically, to the control of ocular lubrication and, more specifically, to the measurement of the height of the tear meniscus. BACKGROUND OF THE INVENTION The tear meniscus is an accumulation of tear fluid at the interface between the cornea and the lower eyelid. Tear meniscus examination is one of the most commonly performed examinations by healthcare professionals for the differential diagnosis of dry eye syndrome. It is particularly advantageous to evaluate the height of this meniscus: which represents the total volume of tears. This evaluation is typically performed in white light, using a slit lamp or an imaging display / acquisition system with a camera, lens, and illumination, and the meniscus is usually interpreted visually. The meniscus is essentially watery and transparent. Under white light observation, image formation involves the superposition of two effects: Visualization of the structure present behind the tear meniscus, specular reflection of the light source by the dioptric interface of air and tear which, by definition, behaves like a mirror and forms a transparent line in the meniscus. Technical problem The problem is that the tear meniscus forms a concave, curved surface between the cornea and the eyelid, meaning that light rays reflected from the surface of the meniscus next to the eyelid do not travel toward a lens positioned and oriented toward the eye, making visualization of certain regions of the meniscus difficult. In fact, the specular reflection is only possible when the tangent of the meniscus is oriented to reflect light toward the pupil of the lens. Clearly, this will only be possible for a portion of the meniscus. Therefore, the transparent line observed in this case does not represent the size of the meniscus. The regions where the structure of the eye is observed through the meniscus are difficult to interpret. Therefore, these known solutions do not allow the height of the tear meniscus to be measured directly. BRIEF DESCRIPTION OF THE INVENTION By virtue of the above technique, a method is proposed for measuring a tear meniscus by using fluorescein concentrated in the tear meniscus to make it fluorescent, the method comprises the steps of: - infuse fluorescein into the surface of the patient's eye to be examined, - illuminating the eye to be examined with blue light and capturing an image of the eye to be examined, said image comprising non-fluorescent blue regions in the absence of fluorescein and fluorescent green regions in the presence of fluorescein, and wherein the image analysis comprises: - identify the tear meniscus and measure its height in pixels (h), - identify the iris and measure its outer diameter in pixels (d), - calculate a ratio (R=D / d) of an outer physical diameter (D) of the iris as calculated or measured in millimeters to its measurement in pixels (d) and calculate the physical height (H=Rxh) of the tear meniscus based on this ratio. This method is advantageous because it allows for rapid and reliable measurement of the actual total height of the tear meniscus. The calculated physical outer diameter of the iris can be a diameter based on the average value related to the patient's eye type. To do this, tables of values related to parameters such as color, eye type, etc., can be integrated into the method-related software. The physical outer diameter of the iris can also be measured elsewhere, and its value entered into the method-related calculation software. Identifying the tear meniscus can involve comparing the green to blue ratio of the image pixels with a first determined threshold. A threshold between 0.8 and 1.3 can be used, and specifically, in the order of 0.95. The method may comprise a dark region exclusion operation that is designed to eliminate those pixels for which the green and / or blue level is less than one second. MA / E / ZUZZ / U ZUO I / determined threshold. This makes it possible to eliminate possible abnormal detections in dark regions such as the green and blue levels that tend to zero and the ratio of green and blue becomes undefined. In one embodiment, the method comprises segmenting the image identifying the fluorescent regions and, more specifically, transforming the image into a binary image that assigns a first binary level to those pixels that are predominantly blue, and a second binary level to those pixels that are predominantly green, or classifying the pixels of the image according to a threshold with respect to a green / blue ratio. Binary imaging allows fluorescent regions to be identified, and allows the tear meniscus to be detected. The method may comprise an algorithm for searching for connected components and removing small objects in the transformed image to further improve meniscus detection. The method may comprise an algorithm for applying a closure operator to the transformed image to remove small local artifacts without moving the boundaries of regions of a given binary level, which facilitates the next step of calculating the regression polynomial. A particularly advantageous embodiment is the use of an algorithm for calculating regression polynomials, which comprises: - detecting and storing vertical segments perpendicular to the general direction of the tear meniscus in each column of the image, said segments comprising a start produced by a transformation from the first binary level to the second binary level, and comprising an end produced by a transition from said second level to said first level, said vertical segments representing the fluorescent tear meniscus, - based on the transitions that delimit the upper portion of the tear meniscus formed by the beginnings of segments that correspond to the transition from the non-fluorescent region to the fluorescent region, calculate a first regression polynomial that delimits an upper line, - based on the transitions that delimit the lower portion of the tear meniscus formed by the ends of segments that correspond to the transition from the fluorescent region to the non-fluorescent region, calculate a second regression polynomial that delimits a lower line. You can calculate the regression polynomial using the method of least squares. The method may then comprise calculating the height (h) of the tear meniscus in pixels by calculating the distance between the regression polynomials calculated for each of said first line and second line below the columns of the image between the top line and the bottom line. ML / E / ZUZZ / U ZUO I / According to an embodiment for simplifying measurement, the method may comprise, after obtaining the images, selecting a region of interest around the lower eyelid of said eye of the patient to reduce calculation times and reduce detection errors. Depending on the instillation method selected, the method may comprise a suitable delay to wait for reabsorption through the tear ducts of excess tear fluid comprising said fluorescein between the instillation of the fluorescein and the measurement. If fluorescein is infused as a drop, one or a few minutes are usually necessary for the excess fluid introduced into the eye to be expelled through the tear ducts. Another issue is a computer program comprising instructions for implementing the above method when this program is executed by a processor. An additional matter is a non-volatile computer-readable storage medium on which a program is stored to implement the above method when this program is executed by a processor. A further aspect is an ophthalmic measuring device suitable for implementing the above method and comprising an ophthalmic measuring apparatus provided with blue light sources arranged around a lens of at least one camera, a computer provided with a user interface and programmed to drive said sources and said at least one camera and implement the method, said computer and said interface being integrated into the ophthalmic measuring device or being external thereto and connected to the ophthalmic measuring device. BRIEF DESCRIPTION OF THE DRAWINGS Other features, details and advantages of the invention will become apparent upon reading the following detailed description, and upon examining the accompanying drawings in which: Figure 1 is a device suitable for implementing the method according to the description; Figure 2 shows a detail of a step in the method; Figure 3A shows a photo of an eye in the context of the method according to the description; Figure 3B shows a schematic representation of the photo in Figure 3A; Figure 4A shows a detail of Figure 3A after selection; Figure 4B shows a binary image based on Figure 4A; MA / E / ZUZZ / U ZUO I / Figure 4C shows an image of a step for generating regression polynomials; Figure 5 is a schematic view with the measured data being plotted; Figure 6 shows a schematic view of a method according to the description; Figure 7 shows a schematic view of a method for measuring the outer diameter of the iris, which can be applied to the invention. DETAILED DESCRIPTION OF THE INVENTION For the most part, the drawings and the following description contain elements of a specific nature. Therefore, they can serve not only to better understand the present invention, but also contribute to its definition, as appropriate. Reference is now made to Figure 1 which shows a device suitable for the method of the invention comprising an ophthalmic measuring apparatus 1 provided with one or two cameras whose lenses 31 point towards the position of the eyes of the patient placed in front of the ophthalmic measuring apparatus and provided with two sources 20 of blue light 470 nm, in particular. According to the example, the light sources are four light-emitting diodes, hereafter blue LEDs, around the lenses of each camera. The device further comprises a computing device, such as a computer 3 comprising a display 4, a keyboard 5, which may also be a touch-sensitive part of the display, a central processor unit with ROM, RAM and mass storage memory, as well as the programs necessary to carry out the method and the appropriate interface to operate the light sources of the cameras. The computer may be external or directly integrated into the ophthalmic measuring device, for example, behind the cameras as described in patent application FR19 10131 filed on September 13, 2019, with the INPI. The method begins according to Figure 2 with the instillation of fluorescein into the eye or both eyes to be examined. Fluorescein is typically used to detect damaged areas of the bulbar conjunctiva or cornea, to which it temporarily binds. Its persistence is somewhat low, and the amount bound to the surface is also too low. Fluorescein can mix with water. Therefore, tears are loaded with fluorescein, and it eventually ends up in the tear meniscus, causing it to become fluorescent. Fluorescein can be instilled as a drop, and after a delay of a few minutes, the additional fluid volume introduced by the fluorescein drop is expelled through the tear ducts. However, enough fluorescein remains to mark the tear volume, which fluoresces when illuminated with blue light. Fluorescein can also be instilled by other means, for example, by means of strips, and in this case, there is no excess fluid a priori and the measurement of the method presented above can be carried out without any delay. The corresponding camera then captures an image of the eye 10 under blue light after instillation. The image 100 observed in Figure 3A includes the fluorescent tear meniscus 12, which is observed in green. It also includes the iris 11, the upper eyelid 13, the lower eyelid 14, and the eyelashes 15, which backscatter the incident light and are thus observed in blue. According to Figure 3B, which schematically shows an image 100, the blue color 101 is schematically represented by a lattice pattern, and the fluorescent green color 102 by a dashed line. In this way, the tear meniscus is clearly visible and well-defined. The color contrast between the fluorescent green regions and the non-fluorescent blue regions is high. In an optimal step, a region of interest 130 is drawn around the meniscus position. To do this, the medical specialist roughly circles the tear meniscus region on their screen. This avoids performing tear meniscus detection calculations in an abnormal region that the software might select. For example, if there is a lot of fluorescein on the eyelid. A comparison with the threshold with respect to the green / blue ratio makes it possible to identify, at least partially, the tear meniscus. In the image in Figure 3A, for a given pixel, the green / blue ratio is very significant. In the absence of fluorescein, incident blue light is backscattered. The green intensity is very low, even zero, and blue is dominant. The green / blue ratio tends to 0, and in the presence of fluorescein, as blue light is absorbed, it is re-emitted as green. The green / blue ratio is high, or even approaches infinity in the tear meniscus region, as shown in Figure 4A, for which the fluorescent region 12a is bright and saturated. The software for the method shown schematically in Figure 6 then comprises an algorithm 220 that will produce a binary image of the region to be considered, and assign a binary value 221 to the non-fluorescent portions and a binary value 222 to the fluorescent portions. Two types of thresholds can be used: Sensitivity: This is the threshold with respect to the green / blue ratio. Dark Region Exclusion: This threshold allows pixels to be removed when the maximum green and blue is less than a threshold: the green / blue ratio is poorly defined in M / E / ZUZZ / U ZUO I / a very dark image (which is a low level of green and blue). Normally, on dark skin, anything can be detected on the skin, and therefore, fluorescence can be detected in places where it is not present. The threshold values are proposed by default to the operator who can, for example, adapt them to the patient. The binary image in Figure 4B is obtained in this way, where the white parts are the fluorescent regions. In the next step 230, the method comprises an algorithm for searching for connected components and removing small objects 231 external to the meniscus in the transformed image. Small objects are pixels or groups of pixels whose morphology makes it possible to determine that they are not part of the tear meniscus and, therefore, that they should be excluded from subsequent image analysis. Specifically, there may be fluorescent regions that are not part of the tear meniscus, for example, in the bulbar conjunctiva or in the eyelids, and this algorithm makes it possible to discard them due to their morphology (area in pixels, aspect ratio in width / height or other characteristics). The method may then comprise a step 240 of applying a closure operator to the binary image, which makes it possible to fill in small holes without moving the contours. For example, this step makes it possible to transform non-fluorescent regions 241 into a fluorescent region of significantly larger size in fluorescent regions. This operation is particularly useful in the presence of dust, a bubble, or any other mishap in the fluorescent region of the tear meniscus. Step 250 comprises calculating the regression polynomials on the upper and lower contours of the tear meniscus. To do this, based on the binary image above, vertical segments will be defined in each column of the image. Normally, the vertical direction is considered to be the direction perpendicular to a generally horizontal direction of the tear meniscus, or the direction passing through both eyes of the patient sitting in the chair. Considering the binary image as a black and white image where white defines the fluorescent region, a vertical segment is defined by: - its beginning, which is the transition from black to white at the beginning of the fluorescent region; - its end, which is the transition from white to black at the end of the fluorescent region; The set of all segment starts provides the top of the binary image, specifically the transition line from the non-fluorescent region to the fluorescent region. The set of all segment ends provides the bottom of the binary image, specifically the transition line from the fluorescent region to the non-fluorescent region. ML / E / ZUZZ / U ZUO I t In each of the upper and lower lines, a regression polynomial is calculated using the least squares method. This provides line 103 for the upper polynomial and line 104 for the lower polynomial in Figure 4C. Polynomial regression enables sub-pixel resolution and makes it possible to overcome transition position quantization noise, as well as image artifacts—noise, dust, or eyelid growth, for example. The distance between the upper and lower polynomials is source filtered, by principle. The polynomials are advantageously four-degree polynomials to follow the curvature of the meniscus. Next, in step 260, the distance between the two polynomials is calculated for a given column of the image to provide the tear meniscus height in pixels. In Figure 5, the measurement position has been selected as the vertical line passing through the center of the iris, but the specialist may select other locations, possibly at his or her request. It is worth mentioning that in the case where the image does not include objects whose size is less than a predetermined threshold, the steps of removing small objects 230 and applying the closure operator 240 can be omitted to go directly from generating the binary image to the step of calculating the regression polynomials, as described in Figure 6 by means of the dashed line 270. A second part of the method may comprise automatically detecting the outer diameter of the iris measured in pixels. In this case, the method comprises the iris detection steps as described in application FR19 10129 filed on September 13, 2019, on behalf of the applicant. According to Figure 7, the iris detection steps may comprise: - a first transformation of the image through the application of an anisotropic band pass filter 400 which is applied in the direction of the width of the eye to produce pairs of rising, dark to light, and falling, light to dark, transitions along the horizontal axis of the eye; - segment 410 the image to find the pairs of rising and falling transitions, which form segments that are necessarily representatives of bright regions of the image; - filter 420 the image, which removes light segments from the central region comprising the pattern and upper and lower regions of the image; - taking into account the light segments, the other regions of the image are no longer considered in the analysis, a first operation 430 of calculating an RMA circle of the iris perimeter based on the right ends of the light segments on the left side of the image and the left ends of the light segments on the right side of the image; ΜΛ / Ε / ΖυΖΖ / υ / υΰΊ / - a step 440 of removing points that are too far from the RMS circle; and - with respect to the remaining points, a new step of calculating an RMS circle to follow the contour of the iris. Based on the RMS circle, calculating the outer diameter of the iris 450, for example, calculating the largest distance in pixels between the edges of the circle will yield the outer diameter d of the iris in pixels which, knowing the actual outer diameter of the iris, makes it possible to calculate the actual height of the tear meniscus by calculating the ratio R=D / d between the physical diameter D of the iris calculated or measured elsewhere and its measurement in pixels d and calculating the physical height H=Rxh of the tear meniscus from this ratio. 0 The invention is not limited to the examples described above, but covers any variant that those skilled in the art may contemplate, for example, by modifying the order of certain operations or by removing or adding certain operations for greater speed or precision of calculation, within the scope of the protection claimed.
Claims
1. A method for measuring a tear meniscus by using fluorescein concentrated in the tear meniscus to make it phosphorescent, based on an analysis of an image obtained by implementing the operations of: - infusing fluorescein into the surface of the patient's eye (10) to be examined, - illuminating the eye (10) to be examined with blue light (20), characterized in that it comprises: - capturing (30) an image (100) of the eye to be examined, said image comprising non-fluorescent blue regions (101) in the absence of fluorescein and fluorescent green regions (102) in the presence of fluorescein, - identifying the tear meniscus (12) and measuring its height in pixels (h), - identifying the iris (11) and measuring its outer diameter in pixels (d),- Calculate a ratio (R=D / d) of a physical outer diameter (D) of the iris as calculated or measured in millimeters for its measurement in pixels (d) and calculate the physical height (H=Rxh) of the tear meniscus based on this ratio.
2. The measurement method according to claim 1, further characterized in that the calculated physical outer diameter of the iris is based on the average value related to the patient's eye type.
3. The measurement method according to claim 1 or 2, further characterized in that identifying the tear meniscus (12) may comprise comparing the green to blue ratio of the image pixels with a predetermined first threshold.
4. The measurement method according to claim 3, further characterized in that it may comprise an operation that excludes dark regions which is designed to eliminate those pixels for which the green and / or blue level is less than a second determined threshold.
5. The measurement method according to any of the preceding claims, further characterized in that it transforms (220) the image into a binary image assigning a first binary level (221) to those pixels that are predominantly blue, and a second binary level (222) to those pixels that are predominantly green, or classifies the image pixels according to a threshold with respect to a green / blue ratio.
6. The measurement method according to claim 5, further characterized in that it comprises an algorithm (230) for searching for connected components and removing small objects (231) in the transformed image.
7. The measurement method according to claim 5 or 6, further characterized in that it comprises an algorithm (240) for applying a closure operator to the transformed image to remove small local defects without moving the margins of regions at a given binary level.
8. The measurement method according to claim 5, 6, or 7, further characterized in that it additionally comprises an algorithm (250) for calculating regression polynomials, comprising: - detecting and storing vertical segments perpendicular to the general direction of the tear meniscus in each column of the image, said segments comprising a start produced by a transformation from the first binary level to the second binary level, and comprising an end produced by a transition from said second level to said first level, said vertical segments representing the fluorescent tear meniscus, - based on the transitions that delimit the upper portion of the tear meniscus formed by the segment starts that correspond to the transition from the non-fluorescent region to the fluorescent region, calculating a first regression polynomial that delimits an upper line (103),- Based on the transitions that delimit the lower portion of the tear meniscus formed from the ends of segments that correspond to the transition from the fluorescent region to the non-fluorescent region, calculate a second regression polynomial that delimits a lower line (104)., 9. The measurement method according to claim 8, further characterized in that calculating the regression polynomial uses the least squares method.
10. The method according to claim 8 or 9, further characterized in that it comprises calculating (260) the height (h) of the tear meniscus in pixels by calculating the distance between the regression polynomials calculated for each of said first line and second line below the columns of the image between the upper line and the lower line.
11. The measurement method according to any of the preceding claims, further characterized in that it comprises selecting a region of interest (130) around the lower eyelid of said patient's eye to reduce calculation times and reduce detection errors.
12. The method in accordance with any of the preceding claims, further characterized in that it comprises a delay (35) suitable to wait for the reabsorption through the tear ducts of excess tear fluid comprising said fluorescein between the instillation of the fluorescein and the measurement.
13. A computer program comprising instructions for implementing the method as claimed in any of claims 1 to 12 when the program is executed by a processor.
14. A computer-readable, non-volatile storage medium in which a program for implementing the method of any of claims 1 to 12 is stored when this program is executed by a processor. ML / E / ZUZZ / U ZUO I / 15. An ophthalmic measuring device for implementing the method as claimed in any of claims 1 to 12, characterized in that it comprises an ophthalmic measuring apparatus (1) provided with blue light sources (20) arranged around a lens (31) of at least one camera (30), a computer provided with a user interface (4, 5) and programmed 5 to actuate said sources and said at least one camera and implement the method, said computer and said interface being integrated into the ophthalmic measuring device or being external thereto and connected to the ophthalmic measuring device.