阅读说明：本技术 一种眼角膜厚度的测量方法、装置、介质及电子设备 (Method, device, medium and electronic equipment for measuring thickness of cornea ) 是由 郑钦象 于 2021-08-10 设计创作，主要内容包括：本申请公开了一种眼角膜厚度的测量方法、装置、计算机可读存储介质及电子设备,通过分别获取拍摄角度不同且均包含眼角膜的第一眼部图像和第二眼部图像并分别识别以得到第一眼角膜图像和第二眼角膜图像；然而根据第一眼角膜图像和第二眼角膜图像构建眼角膜的三维图像,识别三维图像中的眼角膜前表面区域和眼角膜后表面区域,最后计算得到眼角膜厚度；即通过两幅错位的二维图像构建得到三维图像,然后识别该三维图像中的眼角膜前表面区域和后表面区域,计算得到眼角膜厚度,即可以通过拍摄图像以测量眼角膜厚度,从而降低了测量的难度,并且利用图像识别可以较为准确的获取眼角膜前表面区域和后表面区域位置,从而准确计算得到眼角膜厚度。(The application discloses a method and a device for measuring corneal thickness, a computer readable storage medium and an electronic device, wherein a first eye image and a second eye image which have different shooting angles and respectively comprise a cornea are respectively obtained and respectively identified to obtain the first cornea image and the second cornea image; then, constructing a three-dimensional image of the cornea according to the first cornea image and the second cornea image, identifying an anterior surface area and a posterior surface area of the cornea in the three-dimensional image, and finally calculating to obtain the thickness of the cornea; the method comprises the steps of constructing two staggered two-dimensional images to obtain a three-dimensional image, identifying an anterior surface area and a posterior surface area of the cornea in the three-dimensional image, and calculating to obtain the thickness of the cornea, namely, the thickness of the cornea can be measured by shooting the image, so that the measurement difficulty is reduced, and the positions of the anterior surface area and the posterior surface area of the cornea can be accurately obtained by utilizing image identification, so that the thickness of the cornea can be accurately calculated.)

1. A method of measuring corneal thickness of an eye, comprising:

respectively acquiring a first eye image and a second eye image which have different shooting angles and respectively comprise eye corneas;

identifying a first eye cornea image and a second eye cornea image of the first eye image and the second eye image, respectively;

obtaining a three-dimensional image containing the cornea of the eye according to the first cornea image and the second cornea image;

identifying an anterior corneal surface region and a posterior corneal surface region in the three-dimensional image; and

and calculating the thickness of the cornea according to the front surface area and the back surface area of the cornea.

2. The method according to claim 1, wherein the acquiring the first eye image and the second eye image, which have different imaging angles and each include a cornea, respectively comprises:

and respectively acquiring the first eye image and the second eye image by using a binocular camera.

3. The method for measuring the thickness of an ocular cornea according to claim 1, wherein the identifying the first and second corneal images of the first and second ocular images, respectively, comprises:

and respectively inputting the first eye image and the second eye image into a first neural network model to obtain the first eye cornea image and the second eye cornea image.

4. The method for measuring the thickness of the cornea of an eye according to claim 1, further comprising, before said obtaining a three-dimensional image including the cornea from the first cornea image and the second cornea image:

acquiring a first distance and a second distance between two shooting lenses of the binocular camera and eyes of a patient; and

acquiring a third distance between two shooting lenses of the binocular camera;

the obtaining a three-dimensional image containing the cornea of the eye according to the first cornea image and the second cornea image comprises:

constructing the three-dimensional image comprising the cornea of the eye from the first image of the cornea, the second image of the cornea, the first distance, the second distance, and the third distance.

5. The method of measuring corneal thickness according to claim 1, wherein the identifying the anterior corneal surface region and the posterior corneal surface region in the three-dimensional image comprises:

and inputting the three-dimensional image into a second neural network model to obtain the anterior corneal surface area and the posterior corneal surface area of the eye.

6. The method of measuring corneal thickness according to claim 1, wherein the identifying the anterior corneal surface region and the posterior corneal surface region in the three-dimensional image comprises:

acquiring two side areas of the front surface of the cornea and two side areas of the back surface of the cornea in the three-dimensional image;

fitting according to the two side areas of the front surface of the cornea to obtain the front surface area of the cornea; and

and fitting according to the two side areas of the cornea rear surface to obtain the cornea rear surface area.

7. The method for measuring the thickness of the cornea of claim 1, wherein the calculating the thickness of the cornea from the anterior corneal surface area and the posterior corneal surface area comprises:

and calculating the distance between the front surface area of the cornea and the back surface area of the cornea along the thickness direction of the cornea to obtain the thickness of the cornea.

8. An apparatus for measuring thickness of a cornea of an eye, comprising:

the image acquisition module is used for respectively acquiring a first eye image and a second eye image which have different shooting angles and respectively comprise eye corneas;

a first identification module, configured to identify a first eye cornea image and a second eye cornea image of the first eye image and the second eye image, respectively;

the image construction module is used for obtaining a three-dimensional image containing the cornea according to the first cornea image and the second cornea image;

a second identification module for identifying an anterior corneal surface region and a posterior corneal surface region in the three-dimensional image; and

and the thickness calculating module is used for calculating the thickness of the cornea according to the front surface area of the cornea and the back surface area of the cornea.

9. A computer-readable storage medium, in which a computer program is stored, the computer program being adapted to perform the method of measuring the corneal thickness of an eye according to any one of claims 1 to 7.

10. An electronic device, the electronic device comprising:

a processor; and

a memory for storing the processor-executable instructions;

the processor is configured to perform the method of measuring corneal thickness of an eye of any one of claims 1 to 7.

Technical Field

The present application relates to the field of image processing technologies, and in particular, to a method and an apparatus for measuring corneal thickness, a computer-readable storage medium, and an electronic device.

Background

The cornea is the most anterior, transparent portion of the eye, covering the iris, pupil and anterior chamber, and providing the eye with 70% of its refractive power, and small changes in the cornea can cause large changes in refractive state, and thus, much effort has been made to study the shape and optical characteristics of the cornea. The accurate measurement of the corneal curvature and the Corneal Central Thickness (CCT) provides important basis for early diagnosis of corneal diseases, preoperative screening and postoperative follow-up of corneal refractive surgery, intraocular lens degree calculation and the like, and has important significance for intraocular pressure correction, glaucoma investigation and the like.

The existing methods for measuring the thickness of the corneal layer are mostly calculated based on refraction or reflection of light, however, the propagation of light is easily interfered by external media, and the final measurement result has large deviation due to small influence. Moreover, the measurement by light propagation requires a patient to be well-matched, and the measurement result has a great influence on patients with poor or even non-matched matching.

Disclosure of Invention

The present application is proposed to solve the above-mentioned technical problems. Embodiments of the present application provide a method and an apparatus for measuring a corneal thickness of an eye, a computer-readable storage medium, and an electronic device, which solve the above problem of low accuracy in measuring a corneal thickness of an eye.

According to one aspect of the present application, there is provided a method for measuring corneal thickness of an eye, comprising: respectively acquiring a first eye image and a second eye image which have different shooting angles and respectively comprise eye corneas; identifying a first eye cornea image and a second eye cornea image of the first eye image and the second eye image, respectively; obtaining a three-dimensional image containing the cornea of the eye according to the first cornea image and the second cornea image; identifying an anterior corneal surface region and a posterior corneal surface region in the three-dimensional image; and calculating the thickness of the cornea according to the front surface area of the cornea and the back surface area of the cornea.

In an embodiment, the respectively acquiring the first eye image and the second eye image which have different shooting angles and each include a cornea of an eye includes: and respectively acquiring the first eye image and the second eye image by using a binocular camera.

In an embodiment, the identifying the first and second corneal images of the first and second eye images, respectively, comprises: and respectively inputting the first eye image and the second eye image into a first neural network model to obtain the first eye cornea image and the second eye cornea image.

In an embodiment, before the obtaining the three-dimensional image including the cornea according to the first cornea image and the second cornea image, the method for measuring the thickness of the cornea further includes: acquiring a first distance and a second distance between two shooting lenses of the binocular camera and eyes of a patient; acquiring a third distance between two shooting lenses of the binocular camera; the obtaining a three-dimensional image containing the cornea of the eye according to the first cornea image and the second cornea image comprises: constructing the three-dimensional image comprising the cornea of the eye from the first image of the cornea, the second image of the cornea, the first distance, the second distance, and the third distance.

In one embodiment, the identifying the anterior corneal surface region and the posterior corneal surface region in the three-dimensional image comprises: and inputting the three-dimensional image into a second neural network model to obtain the anterior corneal surface area and the posterior corneal surface area of the eye.

In one embodiment, the identifying the anterior corneal surface region and the posterior corneal surface region in the three-dimensional image comprises: acquiring two side areas of the front surface of the cornea and two side areas of the back surface of the cornea in the three-dimensional image; fitting according to the two side areas of the front surface of the cornea to obtain the front surface area of the cornea; and fitting according to the two side areas of the cornea rear surface to obtain the cornea rear surface area.

In one embodiment, said calculating said corneal thickness of the eye from said anterior corneal surface area and said posterior corneal surface area comprises: and calculating the distance between the front surface area of the cornea and the back surface area of the cornea along the thickness direction of the cornea to obtain the thickness of the cornea.

According to another aspect of the present application, there is provided an apparatus for measuring a thickness of a cornea of an eye, comprising: the image acquisition module is used for respectively acquiring a first eye image and a second eye image which have different shooting angles and respectively comprise eye corneas; a first identification module, configured to identify a first eye cornea image and a second eye cornea image of the first eye image and the second eye image, respectively; the image construction module is used for obtaining a three-dimensional image containing the cornea according to the first cornea image and the second cornea image; a second identification module for identifying an anterior corneal surface region and a posterior corneal surface region in the three-dimensional image; and the thickness calculation module is used for calculating the thickness of the cornea according to the front surface area of the cornea and the back surface area of the cornea.

According to another aspect of the present application, there is provided a computer-readable storage medium having stored thereon a computer program for performing any of the methods of measuring corneal thickness of an eye described above.

According to another aspect of the present application, there is provided an electronic apparatus including: a processor; and a memory for storing the processor-executable instructions; the processor is used for executing any one of the methods for measuring the thickness of the cornea.

According to the method, the device, the computer-readable storage medium and the electronic equipment for measuring the thickness of the cornea, the first eye image and the second eye image which have different shooting angles and respectively comprise the cornea are respectively obtained, and the first eye cornea image and the second eye cornea image in the first eye image and the second eye image are respectively identified; obtaining a three-dimensional image containing the cornea of the eye according to the first cornea image and the second cornea image, identifying an anterior surface area of the cornea and a posterior surface area of the cornea in the three-dimensional image, and finally calculating to obtain the thickness of the cornea of the eye according to the anterior surface area of the cornea and the posterior surface area of the cornea of the eye; the method comprises the steps of constructing two staggered two-dimensional images to obtain a three-dimensional image, identifying a front surface area and a back surface area of the cornea in the three-dimensional image, calculating the thickness of the cornea according to the front surface area and the back surface area of the cornea, namely measuring the thickness of the cornea by shooting the image, thereby reducing the measurement difficulty, and accurately obtaining the positions of the front surface area and the back surface area of the cornea by utilizing the image identification, thereby accurately calculating the thickness of the cornea.

Drawings

The above and other objects, features and advantages of the present application will become more apparent by describing in more detail embodiments of the present application with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings, like reference numbers generally represent like parts or steps.

Fig. 1 is a schematic flow chart of a method for measuring corneal thickness of an eye according to an exemplary embodiment of the present disclosure.

Fig. 2 is a schematic flow chart of a method for measuring corneal thickness of an eye according to another exemplary embodiment of the present application.

Fig. 3 is a flowchart illustrating a method for identifying an anterior corneal surface area and a posterior corneal surface area according to an exemplary embodiment of the present application.

Fig. 4 is a schematic structural diagram of an apparatus for measuring corneal thickness according to an exemplary embodiment of the present application.

Fig. 5 is a schematic structural diagram of an apparatus for measuring corneal thickness of an eye according to another exemplary embodiment of the present application.

Fig. 6 is a block diagram of an electronic device provided in an exemplary embodiment of the present application.

Detailed Description

Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein.

Fig. 1 is a schematic flow chart of a method for measuring corneal thickness of an eye according to an exemplary embodiment of the present disclosure. As shown in fig. 1, the method for measuring the thickness of the corneal layer includes:

step 110: a first eye image and a second eye image which have different shooting angles and respectively comprise eye corneas are respectively obtained.

Specifically, the implementation manner of step 110 may be: the method comprises the steps of respectively obtaining a first eye image and a second eye image by using a binocular camera, or obtaining the eye images by using two cameras at different positions or shooting angles, so as to obtain the first eye image and the second eye image, namely obtaining two staggered eye images. The acquired first eye image and the second eye image may be one or more frames of images captured from video stream data. Because the patient may not be matched when the cornea image is collected, especially for children or babies, the method can be used for recording video stream data for a period of time and selecting one or more frames of images from the video stream data as the first eye image and the second eye image. Specifically, the method for selecting the eye image may be manually completed, or may be automatically selected, that is, the video stream is converted into multi-frame image data, and then the multi-frame image data is selected, specifically, an image with the maximum opening degree of opening of the eye (that is, an image with the maximum distance between the upper eyelid and the lower eyelid) in the multi-frame image data is selected, so that the screened image can completely display the cornea region image.

Step 120: a first eye cornea image and a second eye cornea image of the first eye image and the second eye image are identified, respectively.

Specifically, the implementation manner of step 120 may be: and respectively inputting the first eye image and the second eye image into the first neural network model to obtain a first eye cornea image and a second eye cornea image. And respectively inputting the first eye image and the second eye image into the first neural network model by utilizing the trained first neural network model to obtain a first eye cornea image and a second eye cornea image. Wherein the training process of the first neural network model may be training using a standard eye image and a corresponding cornea image.

Step 130: and obtaining a three-dimensional image containing the cornea of the eye according to the first cornea image and the second cornea image.

After the first cornea image and the second cornea image which are staggered with each other are obtained, a three-dimensional image containing the cornea can be constructed according to the first cornea image and the second cornea image, and the three-dimensional image of the cornea can be obtained.

Step 140: an anterior corneal surface region and a posterior corneal surface region in the three-dimensional image are identified.

After the three-dimensional image containing the cornea is obtained, the three-dimensional image is identified to obtain an anterior surface area of the cornea and a posterior surface area of the cornea, namely an area image of the cornea. Specifically, the implementation manner of step 140 may be: and inputting the three-dimensional image into the second neural network model to obtain an anterior corneal surface region and a posterior corneal surface region of the eye. Namely, the trained second neural network model is utilized to input the three-dimensional image into the second neural network model, and the image of the anterior surface area of the cornea and the image of the posterior surface area of the cornea are obtained. Wherein the training process of the second neural network model may be training using the standard three-dimensional image of the eye and the corresponding images of the anterior surface area of the cornea and the posterior surface area of the cornea.

Step 150: and calculating the thickness of the cornea according to the front surface area and the back surface area of the cornea.

After identifying the anterior corneal surface area and the posterior corneal surface area, the distance between the anterior corneal surface area and the posterior corneal surface area may be calculated to obtain the corneal thickness of the eye. Specifically, the implementation manner of step 150 may be: and calculating the distance between the front surface area of the cornea and the back surface area of the cornea along the thickness direction of the cornea to obtain the thickness of the cornea. For example, all distance values of the anterior surface area and the posterior surface area of the cornea along the thickness direction of the cornea can be calculated, and the maximum value can be taken as the thickness of the cornea, or the thicknesses of the cornea at a plurality of different positions can be directly obtained, so as to provide more reference data for diagnosis of doctors. Wherein, the thickness direction of the cornea of the eye can be determined by the following modes: the method comprises the steps of obtaining an upper eyelid area image and a lower eyelid area image in a three-dimensional image through image recognition, obtaining a reference line through fitting according to the upper eyelid area image and the lower eyelid area image (a straight line can be fitted in a specific mode, the sum of distances from all points on the upper eyelid area image and the lower eyelid area image to the straight line is the minimum), obtaining a tangent plane (reference plane) of an eye cornea image by taking the intersection point of the reference line and the eye cornea image as a tangent point, and obtaining the vertical direction of the reference plane as the thickness direction of the eye cornea.

According to the method for measuring the thickness of the cornea, a first eye image and a second eye image which have different shooting angles and respectively comprise the cornea are respectively obtained, and the first eye cornea image and the second eye cornea image in the first eye image and the second eye image are respectively identified; obtaining a three-dimensional image containing the cornea of the eye according to the first cornea image and the second cornea image, identifying an anterior surface area of the cornea and a posterior surface area of the cornea in the three-dimensional image, and finally calculating to obtain the thickness of the cornea of the eye according to the anterior surface area of the cornea and the posterior surface area of the cornea of the eye; the method comprises the steps of constructing two staggered two-dimensional images to obtain a three-dimensional image, identifying a front surface area and a back surface area of the cornea in the three-dimensional image, calculating the thickness of the cornea according to the front surface area and the back surface area of the cornea, namely measuring the thickness of the cornea by shooting the image, thereby reducing the measurement difficulty, and accurately obtaining the positions of the front surface area and the back surface area of the cornea by utilizing the image identification, thereby accurately calculating the thickness of the cornea.

Fig. 2 is a schematic flow chart of a method for measuring corneal thickness of an eye according to another exemplary embodiment of the present application. As shown in fig. 2, before step 130, the method for measuring the thickness of the corneal layer may further include:

step 160: a first distance and a second distance between two photographing lenses of a binocular camera and eyes of a patient are acquired.

Step 170: and acquiring a third distance between the two shooting lenses of the binocular camera.

By acquiring the first distance and the second distance between the two shooting lenses of the binocular camera and the eyes of the patient and the third distance between the two shooting lenses of the binocular camera, the dislocation distance between the first eye image and the second eye image which are obtained by shooting through the two shooting lenses of the binocular camera can be calculated by utilizing the optical principle, so that the three-dimensional image of the cornea can be accurately constructed.

Correspondingly, step 130 is adjusted to: and constructing a three-dimensional image containing the cornea of the eye according to the first cornea image, the second cornea image, the first distance, the second distance and the third distance.

Fig. 3 is a flowchart illustrating a method for identifying an anterior corneal surface area and a posterior corneal surface area according to an exemplary embodiment of the present application. As shown in fig. 3, step 140 may include:

step 141: two side regions of the anterior surface of the cornea and two side regions of the posterior surface of the cornea in the three-dimensional image are acquired.

Since the cornea is transparent, interference between the layers results in a difficult image recognition, especially in the middle. Because the differentiation of both sides region is relatively obvious and because there is certain declination when the camera shoots, consequently, this application is through obtaining the both sides region of the anterior surface of eye cornea and the both sides region of the posterior surface of eye cornea in the three-dimensional image to the accuracy of the both sides region of guaranteeing to obtain.

Step 142: and fitting according to the two side areas of the front surface of the cornea to obtain the front surface area of the cornea.

Step 143: and fitting according to the two side areas of the cornea rear surface to obtain the cornea rear surface area of the eye.

After the two side areas of the front surface of the cornea and the two side areas of the back surface of the cornea are obtained, the complete areas of the front surface of the cornea and the back surface of the cornea are obtained through fitting according to the two side areas of the front surface of the cornea and the two side areas of the back surface of the cornea respectively. Because the anterior surface of the cornea and the posterior surface of the cornea are generally smooth circular arcs, the whole anterior surface of the cornea and the whole posterior surface of the cornea can be obtained by relatively accurate fitting according to the two side areas and the radians thereof.

Fig. 4 is a schematic structural diagram of an apparatus for measuring corneal thickness according to an exemplary embodiment of the present application. As shown in fig. 4, the device 40 for measuring the thickness of the corneal layer includes: an image obtaining module 41, configured to obtain a first eye image and a second eye image which have different shooting angles and both include a cornea of an eye; a first identification module 42, configured to identify a first eye cornea image and a second eye cornea image in the first eye image and the second eye image, respectively; an image construction module 43, configured to obtain a three-dimensional image including a cornea of an eye according to the first cornea image and the second cornea image; a second recognition module 44 for recognizing an anterior corneal surface region and a posterior corneal surface region in the three-dimensional image; and a thickness calculation module 45 for calculating the thickness of the cornea of the eye according to the anterior surface area of the cornea and the posterior surface area of the cornea of the eye.

According to the device for measuring the thickness of the cornea, the image acquisition module 41 is used for respectively acquiring the first eye image and the second eye image which have different shooting angles and respectively comprise the cornea, and the first identification module 42 is used for respectively identifying the first eye cornea image and the second eye cornea image in the first eye image and the second eye image; however, the image construction module 43 obtains a three-dimensional image including the cornea of the eye according to the first cornea image and the second cornea image, the second recognition module 44 recognizes the anterior surface area of the cornea and the posterior surface area of the cornea in the three-dimensional image, and the thickness calculation module 45 calculates the thickness of the cornea of the eye according to the anterior surface area of the cornea and the posterior surface area of the cornea of the eye; the method comprises the steps of constructing two staggered two-dimensional images to obtain a three-dimensional image, identifying a front surface area and a back surface area of the cornea in the three-dimensional image, calculating the thickness of the cornea according to the front surface area and the back surface area of the cornea, namely measuring the thickness of the cornea by shooting the image, thereby reducing the measurement difficulty, and accurately obtaining the positions of the front surface area and the back surface area of the cornea by utilizing the image identification, thereby accurately calculating the thickness of the cornea.

In an embodiment, the image acquisition module 41 may be further configured to: the method comprises the steps of respectively obtaining a first eye image and a second eye image by using a binocular camera, or obtaining the eye images by using two cameras at different positions or shooting angles, so as to obtain the first eye image and the second eye image, namely obtaining two staggered eye images.

In an embodiment, the first identification module 42 may be further configured to: and respectively inputting the first eye image and the second eye image into the first neural network model to obtain a first eye cornea image and a second eye cornea image.

In an embodiment, the second identification module 44 may be further configured to: and inputting the three-dimensional image into the second neural network model to obtain an anterior corneal surface region and a posterior corneal surface region of the eye.

In an embodiment, the thickness calculation module 45 may be further configured to: and calculating the distance between the front surface area of the cornea and the back surface area of the cornea along the thickness direction of the cornea to obtain the thickness of the cornea.

Fig. 5 is a schematic structural diagram of an apparatus for measuring corneal thickness of an eye according to another exemplary embodiment of the present application. As shown in fig. 5, the device 40 for measuring the thickness of the cornea of the eye may further include: a lens interval acquisition module 46 for acquiring a first distance and a second distance between two photographing lenses of the binocular camera and the eyes of the patient; and a shooting distance acquiring module 47, configured to acquire a third distance between two shooting lenses of the binocular camera. Correspondingly, the image construction module 43 is configured to: and constructing a three-dimensional image containing the cornea of the eye according to the first cornea image, the second cornea image, the first distance, the second distance and the third distance.

In one embodiment, as shown in fig. 5, the second identification module 44 may include: a two-side region acquisition unit 441 for acquiring two-side regions of the anterior surface of the cornea and two-side regions of the posterior surface of the cornea in the three-dimensional image; a front surface obtaining unit 442, configured to obtain a cornea front surface region according to fitting of two side regions of the cornea front surface; a back surface obtaining unit 443 for obtaining a back surface area of the cornea of the eye by fitting two side areas of the back surface of the cornea of the eye.

Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 6. The electronic device may be either or both of the first device and the second device, or a stand-alone device separate from them, which stand-alone device may communicate with the first device and the second device to receive the acquired input signals therefrom.

FIG. 6 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.

As shown in fig. 6, the electronic device 10 includes one or more processors 11 and memory 12.

The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

Memory 12 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by processor 11 to implement the methods of corneal thickness measurement of various embodiments of the present application described above and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.

In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other form of connection mechanism (not shown).

When the electronic device is a stand-alone device, the input means 13 may be a communication network connector for receiving the acquired input signals from the first device and the second device.

The input device 13 may also include, for example, a keyboard, a mouse, and the like.

The output device 14 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 14 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.

Of course, for simplicity, only some of the components of the electronic device 10 relevant to the present application are shown in fig. 6, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.

In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods of measuring corneal thickness according to various embodiments of the present application described in the "exemplary methods" section of this specification, supra.

The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.

Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods of measuring corneal thickness according to various embodiments of the present application described in the "exemplary methods" section above in this specification.

The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.

The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".

It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.

The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.