CN115120178A - Method for acquiring curvature of front surface of eyeball - Google Patents
Method for acquiring curvature of front surface of eyeball Download PDFInfo
- Publication number
- CN115120178A CN115120178A CN202210775579.3A CN202210775579A CN115120178A CN 115120178 A CN115120178 A CN 115120178A CN 202210775579 A CN202210775579 A CN 202210775579A CN 115120178 A CN115120178 A CN 115120178A
- Authority
- CN
- China
- Prior art keywords
- curvature
- sclera
- degrees
- cornea
- blood vessel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 210000005252 bulbus oculi Anatomy 0.000 title claims abstract description 24
- 210000003786 sclera Anatomy 0.000 claims abstract description 57
- 210000004087 cornea Anatomy 0.000 claims abstract description 39
- 238000010586 diagram Methods 0.000 claims abstract description 12
- 210000004204 blood vessel Anatomy 0.000 claims description 29
- 210000001232 limbus corneae Anatomy 0.000 claims description 17
- 230000009466 transformation Effects 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 9
- 230000004927 fusion Effects 0.000 claims description 8
- 238000012216 screening Methods 0.000 claims description 5
- 239000013598 vector Substances 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 4
- 230000002457 bidirectional effect Effects 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 210000001508 eye Anatomy 0.000 abstract description 10
- 230000007547 defect Effects 0.000 abstract description 4
- 238000012876 topography Methods 0.000 abstract 1
- 210000000795 conjunctiva Anatomy 0.000 description 9
- 230000006870 function Effects 0.000 description 4
- 210000004379 membrane Anatomy 0.000 description 4
- 239000012528 membrane Substances 0.000 description 4
- 210000000744 eyelid Anatomy 0.000 description 3
- 230000004075 alteration Effects 0.000 description 2
- 201000009310 astigmatism Diseases 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000035807 sensation Effects 0.000 description 2
- 210000001760 tenon capsule Anatomy 0.000 description 2
- 238000002834 transmittance Methods 0.000 description 2
- 102000008186 Collagen Human genes 0.000 description 1
- 108010035532 Collagen Proteins 0.000 description 1
- 201000002287 Keratoconus Diseases 0.000 description 1
- 241001469893 Oxyzygonectes dovii Species 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 229920001436 collagen Polymers 0.000 description 1
- 208000021921 corneal disease Diseases 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 210000004177 elastic tissue Anatomy 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000004400 mucous membrane Anatomy 0.000 description 1
- 210000001328 optic nerve Anatomy 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000028327 secretion Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
- 238000002054 transplantation Methods 0.000 description 1
- 230000008733 trauma Effects 0.000 description 1
- 208000005494 xerophthalmia Diseases 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
- A61B3/107—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for determining the shape or measuring the curvature of the cornea
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/0016—Operational features thereof
- A61B3/0025—Operational features thereof characterised by electronic signal processing, e.g. eye models
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
- A61B3/14—Arrangements specially adapted for eye photography
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4038—Image mosaicing, e.g. composing plane images from plane sub-images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/7715—Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/32—Indexing scheme for image data processing or generation, in general involving image mosaicing
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Medical Informatics (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Heart & Thoracic Surgery (AREA)
- Biophysics (AREA)
- Veterinary Medicine (AREA)
- Public Health (AREA)
- Animal Behavior & Ethology (AREA)
- Surgery (AREA)
- Molecular Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biomedical Technology (AREA)
- Ophthalmology & Optometry (AREA)
- Multimedia (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Signal Processing (AREA)
- Geometry (AREA)
- Computer Graphics (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- Eye Examination Apparatus (AREA)
Abstract
The invention relates to a method for acquiring eyeball front surface curvature, which adopts a Placido module to shoot corneal curvature, and a structured light module to acquire sclera curvature, and comprises the following steps: s1: starting shooting; s2: a camera Placido module disc unit acquires a cornea Placido diagram; s3: gazing on a plane perpendicular to the eye axis: the structured light module units respectively acquire sclera images in the directions of angles of 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees and 315 degrees; s4: calculating a cornea front surface curvature map according to the cornea Placido map; s5: and calculating a sclera curvature map according to the sclera point cloud data of the eight maps to obtain the corneal curvature. The invention overcomes the defect that the corneal topography instrument can only measure the curvature of the front surface of the cornea, and the full-width eyeball front surface curvature graph after picture arrangement accurately reflects the condition of the corneosclera and can assist the fitting of the scleral lens.
Description
Technical Field
The invention belongs to the technical field of ophthalmologic examination, and particularly relates to a method for acquiring the curvature of the front surface of an eyeball.
Background
As shown in fig. 1, Sclera (Sclera), which is commonly called white eye in human eyes, is a milky opaque fibrous membrane in the eyeball and belongs to the fibrous membrane of the eyeball. It is composed mainly of collagen and some elastic fibers. The sclera is covered by a tenon's capsule (tenon's capsule), the anterior segment is covered by a bulbar conjunctiva, the posterior pole is covered by a sclera sieve plate, and the optic nerve fiber bundle passes through the eyeball. The sclera is approximately 5/6 of the fibrous membrane of the eyeball and serves to protect the contents of the eyeball and maintain the morphology of the eyeball.
The conjunctiva, sclera, and cornea are all anatomical names of the eye, and are located at the following positions:
1. cornea, sclera: the eyeball is a spherical object with the outermost fibrous membranes, the sclera and the cornea, typically 5/6 for the sclera, and the white portion of the eye for the sclera, the middle black or brown area for the cornea, which is transparent but may mirror the color of the posterior iris. If the iris of Chinese is brown, the brown color can be seen, while the iris of western people is often gray, blue or gray blue.
2. And (3) forming a conjunctiva: the conjunctiva is a translucent mucous membrane that covers primarily the sclera, as well as the tarsal surfaces of the eyelids. Covering the white of the eye, i.e. the white eyeball, is called the bulbar conjunctiva, and covering the inner side of the eyelid is called the palpebral conjunctiva. The interface between the white of the eye and the inner surface of the eyelid is called the fornix conjunctiva.
In general, the conjunctiva, sclera, and cornea are all ocular surface tissues and are susceptible to external factors. Especially, the conjunctiva and the cornea are in close contact with the outside, and are more easily affected by external physical and chemical factors and trauma.
The average diameter of a common human cornea is 11.8mm, the diameter of a common hard contact lens is about 9.2mm, the diameter of the common hard contact lens is about 75 to 80 percent of the diameter of the cornea, and the large-diameter contact lens can completely cover the surface of the cornea; the hard contact lenses commonly used are designed to be worn on the cornea, which is one of the most sensitive parts of the human body, and when the patient ages or the tear secretion capacity of the patient is reduced to a certain degree, the friction between the contact lens and the cornea is increased, and foreign body sensation or maladaptive symptoms gradually appear.
This phenomenon is more pronounced in people with corneal disease, and therefore, scleral lenses, which are hard, highly oxygen permeable contact lenses with large diameter contact points on the sclera, have been developed to improve vision. By increasing the diameter of the lens, the contact point between part or all of the lens and the surface of the eye is changed from the contact point of the cornea to the relatively insensitive sclera, so that the existence of foreign body sensation can be effectively reduced; the lens is applied to keratoconus, cornea transplantation, xerophthalmia, laser vision correction, higher astigmatism degree, more special astigmatism axis degree, people who can wear improperly or feel uncomfortable when the soft contact lens is worn, and the vision can be effectively improved through the large-diameter hard contact lens.
The curvature height and other numerical values of the front surface of the eyeball need to be known in the process of fitting the scleral lens; currently, the curvature of the anterior surface of the eyeball is detected by a special device, and the following problems exist:
1: a Placido disc based corneal topographer system can only measure the curvature of the corneal portion;
2: the measurement of structured light for transparent black parts is not accurate;
3: the scleral mirror fitting requires all curvatures of the corneal and scleral sites;
4: the three-dimensional anterior ocular segment and OCT systems based on scheimpflug, while capable of measuring scleral curvature, suffer from a limited range of diameters and are expensive to measure.
Disclosure of Invention
In order to solve the above problems, the primary objective of the present invention is to provide a method for obtaining the curvature of the anterior surface of the eyeball, which overcomes the defect that the corneal topographer can only measure the curvature of the anterior surface of the cornea, and makes up the problem that the structured light has strong light transmittance to the corneal part and cannot be measured.
In order to achieve the above object, the technical scheme of the invention is as follows.
A method for acquiring the curvature of the front surface of an eyeball adopts a Placido module to shoot the curvature of a cornea, and a structured light module to acquire the curvature of a sclera, and comprises the following steps:
s1: starting shooting;
s2: a camera Placido module disc unit acquires a cornea Placido diagram;
s3: gaze is at least: the structured light module units respectively acquire sclera images in the directions of angles of 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees and 315 degrees;
s4: calculating a cornea front surface curvature map according to the cornea Placido map;
s5: and calculating a sclera curvature map according to the sclera point cloud data of the eight maps to obtain the corneal curvature.
The calculation process of the point cloud data curvature is as follows:
a data point Pi is taken from a scattered point cloud, and then n points are uniformly taken from the point cloud by taking the Pi as a center, wherein the n points cover the whole point cloud as much as possible.
The n nearest neighbors within a region are fitted according to a local paraboloid fit formula:
Z(x,y)＝ax^2+by^2+cxy+dx+ey+f，
the coefficients above can be solved by a least square method, i.e. the coefficients of the space surface function can be solved, and after the coefficients are solved, the local average curvature of the point cloud of the data points is calculated according to the property of the space surface curve.
According to the formula:
the local mean curvature of the point cloud can be calculated.
Further, the plurality of scleral curvature maps are spliced to generate an accurate scleral curvature map, and the splicing method comprises the following steps:
s110: taking the front-view image as a central image;
s120: placing the eight pictures in eight directions of the front-view image respectively;
s130: extracting the corneal limbus from each picture, and fusing and splicing the eight pictures;
s140: extracting the capillary vessels of each picture, and accurately adjusting and splicing the fused image according to the direction and the diameter of the capillary vessels;
wherein, the adjustment splicing process is as follows:
1) local SIFT algorithm for scleral vessel pair, limbal pair regions; extracting SIFT characteristic points of the scleral blood vessel pair and the corneal limbus region by adopting a local SIFT algorithm to obtain the positions, the dimensions and the directions of the scleral blood vessel pair and the corneal limbus;
2) performing sclera blood vessel pair and corneal limbus characteristic point rough matching by using a rapid nearest neighbor algorithm, and performing primary screening by using a threshold setting and bidirectional cross inspection method;
the fast nearest neighbor algorithm feature matching algorithm implementation steps are as follows: storing sclera blood vessel pairs and limbus feature description vectors in order by using a K-D Tree (KDimmusional Tree) data structure, and then finding out the nearest sclera blood vessel pair, limbus feature adjacent points and next-nearest adjacent points by using a fast approximate K nearest neighbor (FLANN) algorithm.
3) After finding out the sclera blood vessel pair and the limbus feature point, the feature point matching is limited by adopting the distance ratio of the nearest neighbor point to the next nearest neighbor point because the matched sclera blood vessel pair and the limbus feature point not only require the feature description vector to be nearest, but also can be distinguished from other feature points. And calculating the ratio of the distance between the nearest neighbor point and the distance between the next neighbor points, if the ratio is less than the threshold value, retaining, otherwise, rejecting.
4) Using RANSAC algorithm to carry out sclera blood vessel pair and limbus fine matching.
The main process of the RANSAC algorithm (RANdom SAmple Consensus) in SIFT feature screening is as follows:
(1) randomly choosing a RANSAC sample from a sample set, namely 4 matching point pairs;
(2) calculating a transformation matrix M according to the 4 matching point pairs;
(3) calculating consistent set consensus meeting the current transformation matrix according to the sample set, the transformation matrix M and the error measurement function, and returning the number of elements in the consistent set;
(4) judging whether the optimal (maximum) consistent set exists according to the number of elements in the current consistent set, and if so, updating the current optimal consistent set;
(5) updating the current error probability p, if p is larger than the allowed minimum error probability, repeating the steps (1) to (4) to continue iteration until the current error probability p is smaller than the minimum error probability;
5) image transformation, mapping different images to the same coordinate system;
6) image fusion.
Direct stitching can cause problems such as ghosting and chromatic aberration, and therefore a specific method is required to be used for image fusion. Reserving pixel points with large pixel gray values in the sclera blood vessel pair and the corneal limbus overlapping region, and reserving information with maximum contrast in the image; and carrying out image fusion by using the pixel points.
S150: and calculating and displaying the curvature value.
The invention has the beneficial effects that:
1: the defect that the cornea topographer can only measure the curvature of the front surface of the cornea is overcome;
2: the problem that the light transmission of the structured light to the cornea part is strong and cannot be measured is solved;
3: the full-width eyeball front surface curvature graph after picture arrangement accurately reflects the vision condition, and can assist the fitting of the scleral lens.
Drawings
Fig. 1 is a schematic perspective view of an anterior surface of an eyeball.
Fig. 2 is a schematic diagram of a system implemented by the invention.
Fig. 3 is a flow chart of a method implemented by the present invention.
Fig. 4 is a schematic view of the shooting orientation of the present invention.
FIG. 5 is a schematic diagram of a rough mosaic of different aspect images implemented by the present invention.
FIG. 6 is a flow chart of an image stitching process implemented by the present invention.
Figure 7 is a diagram of human eye corneal scleral capillaries.
Fig. 8 is a diagram of a capillary vessel distribution after preliminary image processing implemented by the present invention.
Fig. 9 is a diagram of capillary pair splicing for segmentation achieved by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 2-5, in order to implement the method for obtaining the curvature of the anterior surface of the eyeball according to the present invention, a Placido module is used to photograph the curvature of the cornea, and a structured light module is used to obtain the curvature of the sclera, as shown in fig. 2, the Placido module is embodied as a Placido plate unit, and the structured light module is embodied as a structured light unit, both of which are disposed in a camera.
The method comprises the following steps:
the method comprises the following steps:
s1: starting shooting;
s2: a camera Placido disc unit acquires a cornea Placido diagram;
s3: fixation: the structured light units respectively acquire sclera images in the directions of angles of 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees and 315 degrees;
s4: calculating a cornea front surface curvature map according to the cornea Placido map;
s5: calculating a sclera curvature map according to the sclera point cloud data of the eight orthographic and oblique views;
the point cloud data calculation curvature calculation process comprises the following steps:
taking a data point Pi in the scattered point cloud, and then uniformly taking n points in the point cloud by taking Pi as a center, wherein the n points are required to cover the whole point cloud as much as possible.
The n nearest neighbors within a region are fitted according to a local paraboloid fit formula:
Z(x,y)＝ax^2+by^2+cxy+dx+ey+f，
the coefficients above can be obtained by a least square method, i.e. the coefficients of the space surface function can be obtained, and after the coefficients are obtained, the local average curvature of the point cloud of the data points is calculated according to the properties of the space surface curve.
According to the formula:
the local mean curvature of the point cloud can be calculated.
Then, the eight scleral curvature maps are stitched to generate an accurate scleral curvature map, as shown in fig. 6, the stitching method is:
s110: taking the front-view image as a central image;
s120: the eight images are respectively arranged in eight directions of the front-view image, and the eight images can cover 360 degrees of angles, so that the generated curvature is more accurate; in practice, 6 drawings, or more, for example 16 drawings, may also be used.
S130: extracting the corneal edge of each picture, roughly fusing and splicing the eight pictures; fig. 7 is a diagram of the capillaries of the sclera of the cornea obtained and processed to give fig. 8, showing the capillary distribution of the eye.
S140: extracting the capillary vessels of each picture, finely adjusting and splicing the fused images according to the directions and diameters of the capillary vessels, and specifically comprising the following steps of:
1) local SIFT algorithm for scleral vessel pair, limbal pair regions; performing SIFT feature point extraction on the sclera blood vessel pair and the corneal limbus region by adopting a local SIFT algorithm to obtain the positions, the dimensions and the directions of the sclera blood vessel pair and the corneal limbus;
2) performing sclera blood vessel pair and corneal limbus feature point rough matching by using a rapid nearest neighbor algorithm, and performing primary screening by using a threshold setting and bidirectional cross inspection method;
the fast nearest neighbor algorithm feature matching algorithm implementation steps are as follows: sequentially storing sclera blood vessel pairs and limbus feature description vectors by using a K-D Tree (KDimensional Tree) data structure, and finding out nearest sclera blood vessel pairs, limbus feature adjacent points and secondary adjacent points by using a fast approximate K nearest neighbor (FLANN) algorithm.
3) After finding out the sclera blood vessel pair and the limbus feature point, the feature point matching is limited by adopting the distance ratio of the nearest neighbor point to the next nearest neighbor point because the matched sclera blood vessel pair and the limbus feature point not only require the feature description vector to be nearest, but also can be distinguished from other feature points. And calculating the ratio of the distance between the nearest neighbor point and the distance between the next neighbor points, if the ratio is less than the threshold value, retaining, otherwise, rejecting.
4) Using RANSAC algorithm to perform sclera blood vessel pair and limbus fine matching.
The main process of the RANSAC algorithm (RANdom SAmple Consensus) in SIFT feature screening is as follows:
(1) randomly choosing a RANSAC sample from a sample set, namely 4 matching point pairs;
(2) calculating a transformation matrix M according to the 4 matching point pairs;
(3) calculating consistent set consensus meeting the current transformation matrix according to the sample set, the transformation matrix M and the error measurement function, and returning the number of elements in the consistent set;
(4) judging whether an optimal (maximum) consistent set exists or not according to the number of elements in the current consistent set, and if so, updating the current optimal consistent set;
(5) updating the current error probability p, if p is larger than the allowed minimum error probability, repeating the steps (1) to (4) to continue iteration until the current error probability p is smaller than the minimum error probability;
5) image transformation, mapping different images to the same coordinate system;
6) image fusion.
The direct stitching can cause problems of ghost, chromatic aberration and the like, so that image fusion is required. Reserving pixel points with large pixel gray values in the sclera blood vessel pair and the corneal limbus overlapping region, and reserving information with maximum contrast in the image; and performing image fusion by using the pixel points.
S150: and calculating and displaying a curvature value.
Fig. 7 is a diagram of the capillaries of the sclera of the cornea obtained and processed to give fig. 8, showing the capillary distribution of the eye. Then, the capillary vessel pair splicing diagram is obtained by segmentation.
S150: and calculating and displaying the curvature value. The curvature values are typically displayed on a full anterior eyeball surface map after the puzzle is assembled.
Wherein, Placido module, structured light module then can have different connected modes, and Placido module can be built-in inside structured light module, and structured light module also can be built-in Placido module.
Placido module, structured light module, may be provided in one integrated device or in two separate devices.
Therefore, by the method, the curvature values of the front surface of the eyeball, including the cornea and the sclera, can be accurately obtained, the defect that the corneal topographer can only measure the curvature of the front surface of the cornea is overcome, meanwhile, the problem that the light transmittance of the structured light to the cornea part is strong and cannot be measured is solved, and the fitting of the scleral mirror can be effectively assisted.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A method for acquiring the curvature of the front surface of an eyeball is characterized in that a Placido module is used for shooting the curvature of a cornea, a structured light module is used for acquiring the curvature of a sclera, and the method comprises the following steps:
s1: starting shooting;
s2: a camera Placido module disc unit acquires a cornea Placido diagram;
s3: gaze at least: the structured light module units respectively acquire sclera images in the directions of angles of 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees and 315 degrees;
s4: calculating a cornea front surface curvature map according to the cornea Placido map;
s5: calculating a plurality of sclera curvature maps according to the sclera point cloud data of eight orthographic and oblique views to obtain the corneal curvature,
the calculation process of the point cloud data curvature is as follows:
a data point Pi is taken from a scattered point cloud, and then n points are uniformly taken from the point cloud by taking the Pi as a center, wherein the n points cover the whole point cloud as much as possible.
The n nearest neighbors within a region are fitted according to a local paraboloid fit formula:
Z(x,y)＝ax^2+by^2+cxy+dx+ey+f，
the coefficients above can be solved by a least square method, i.e. the coefficients of the space surface function can be solved, and after the coefficients are solved, the local average curvature of the point cloud of the data points is calculated according to the property of the space surface curve.
According to the formula:
the local mean curvature of the point cloud can be calculated.
2. The method of claim 1, wherein the plurality of scleral curvature maps are assembled to generate a more comprehensive and accurate scleral curvature map, the assembling method comprising:
s110: taking the front-view image as a central image;
s120: placing the eight pictures in eight directions of the front-view image respectively;
s130: extracting the corneal limbus from each picture, and fusing and splicing the eight pictures;
s140: extracting the sclera blood vessel pair and the limbus of each picture, accurately adjusting and splicing the fused image according to the sclera blood vessel pair, the limbus direction and the diameter, and extracting the positions of the sclera blood vessel pair and the limbus;
s150: and calculating and displaying a curvature value.
3. The method for obtaining the curvature of the anterior surface of the eyeball as set forth in claim 2, wherein the step S140 specifically comprises:
1) local SIFT algorithm for scleral vessel pair, limbal pair regions; extracting SIFT characteristic points of the scleral blood vessel pair and the corneal limbus region by adopting a local SIFT algorithm to obtain the position, the scale and the direction of the scleral blood vessel pair and the corneal limbus;
2) performing sclera blood vessel pair and corneal limbus feature point rough matching by using a rapid nearest neighbor algorithm, and performing primary screening by using a threshold setting and bidirectional cross inspection method;
the fast nearest neighbor algorithm feature matching algorithm implementation steps are as follows: sequentially storing sclera blood vessel pairs and corneal limbus feature description vectors by using a K-D tree data structure, and finding out nearest sclera blood vessel pairs, corneal limbus feature adjacent points and next adjacent points by using a fast approximate K nearest neighbor algorithm;
3) after scleral blood vessel pairs and corneal limbus feature points are found out, limiting feature point matching by adopting the distance ratio of nearest neighbor points to next nearest neighbor points; calculating the ratio of the distance between the nearest neighbor point and the distance between the next neighbor points, if the ratio is less than a threshold value, retaining, otherwise, removing;
4) using RANSAC algorithm to perform sclera blood vessel pair and limbus cornea fine matching, the process is as follows:
(1) randomly choosing a RANSAC sample from the sample set, namely 4 matching point pairs;
(2) a transformation matrix M is calculated from these 4 matching point pairs:
(3) according to the sample set, the transformation matrix M and the error measurement function, calculating a consistent set consensus meeting the current transformation matrix, and returning the number of elements in the consistent set:
(4) judging whether the current consistency set is an optimal consistency set according to the number of elements in the current consistency set, and if so, updating the current optimal consistency set;
(5) updating the current error probability p, if the p is larger than the allowed minimum error probability, repeating the steps (1) to (4) to continue iteration until the current error probability p is smaller than the minimum error probability;
5) image transformation, mapping different images to the same coordinate system;
6) image fusion;
reserving pixel points with large pixel gray values in the sclera blood vessel pair and the corneal limbus overlapping region, and reserving information with maximum contrast in the image; and carrying out image fusion by using the pixel points.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210775579.3A CN115120178A (en) | 2022-07-03 | 2022-07-03 | Method for acquiring curvature of front surface of eyeball |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210775579.3A CN115120178A (en) | 2022-07-03 | 2022-07-03 | Method for acquiring curvature of front surface of eyeball |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115120178A true CN115120178A (en) | 2022-09-30 |
Family
ID=83381767
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210775579.3A Pending CN115120178A (en) | 2022-07-03 | 2022-07-03 | Method for acquiring curvature of front surface of eyeball |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115120178A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117611585A (en) * | 2024-01-22 | 2024-02-27 | 天津医科大学眼科医院 | Eyeball wall deformation analysis method and system based on image processing |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130093998A1 (en) * | 2011-10-17 | 2013-04-18 | Eyedeal Scanning, Llc. | Method and apparatus for determining eye topology |
| DE102014220410A1 (en) * | 2014-10-08 | 2015-11-05 | Carl Zeiss Meditec Ag | Identify a patient eye card |
| US20190307326A1 (en) * | 2018-04-06 | 2019-10-10 | Amo Wavefront Sciences, Llc | Methods and systems for corneal topography with in-focus scleral imaging |
| US20190357767A1 (en) * | 2018-05-23 | 2019-11-28 | Alcon Inc. | Measuring a posterior corneal surface of an eye |
| CN113662506A (en) * | 2021-09-26 | 2021-11-19 | 温州医科大学 | Corneal surface morphology measuring method, device, medium and electronic equipment |
-
2022
- 2022-07-03 CN CN202210775579.3A patent/CN115120178A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130093998A1 (en) * | 2011-10-17 | 2013-04-18 | Eyedeal Scanning, Llc. | Method and apparatus for determining eye topology |
| DE102014220410A1 (en) * | 2014-10-08 | 2015-11-05 | Carl Zeiss Meditec Ag | Identify a patient eye card |
| US20190307326A1 (en) * | 2018-04-06 | 2019-10-10 | Amo Wavefront Sciences, Llc | Methods and systems for corneal topography with in-focus scleral imaging |
| US20190357767A1 (en) * | 2018-05-23 | 2019-11-28 | Alcon Inc. | Measuring a posterior corneal surface of an eye |
| CN113662506A (en) * | 2021-09-26 | 2021-11-19 | 温州医科大学 | Corneal surface morphology measuring method, device, medium and electronic equipment |
Non-Patent Citations (1)
| Title |
|---|
| 黄明吉主编: "《数字化成形与数字化成形与先进制造技术》", 31 August 2020, 北京：机械工业出版社, pages: 28 - 36 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117611585A (en) * | 2024-01-22 | 2024-02-27 | 天津医科大学眼科医院 | Eyeball wall deformation analysis method and system based on image processing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6845804B2 (en) | How to determine spectacle lenses with unwanted astigmatism | |
| JP2023071644A (en) | Digital therapeutic corrective spectacles | |
| Walsh | The effect of mydriasis on the pupillary centration of the human eye | |
| Dubbelman et al. | The shape of the aging human lens: curvature, equivalent refractive index and the lens paradox | |
| TWI466654B (en) | Methods and apparatuses for enhancing peripheral vision | |
| CN113678055B (en) | Determination of at least one optical parameter of an ophthalmic lens | |
| KR20060021331A (en) | Contact lens with shaped periphery | |
| EP3649577B1 (en) | Application to determine reading/working distance | |
| ES2354153T3 (en) | METHOD FOR EVALUATING THE PERFORMANCE OF AN OPTICAL SYSTEM AND METHOD FOR ITS DESIGN. | |
| Waring | Making sense of keratospeak II: proposed conventional terminology for corneal topography | |
| ES2932157T3 (en) | Determination of a refractive error of an eye | |
| US20220007929A1 (en) | Holographic Real Space Refractive System | |
| EP3746839B1 (en) | Method for determining refractory power of eye using immersive system and electronic device thereof | |
| TWI835923B (en) | Method for determining an ophthalmic lens adapted for correcting an abnormal refraction of an eye of a wearer | |
| CN115120178A (en) | Method for acquiring curvature of front surface of eyeball | |
| JP2020525842A (en) | Ophthalmic lens design based on topology | |
| JP7305325B2 (en) | Methods and means for evaluating rotational stability of toric contact lenses | |
| CN114779497B (en) | Scleral contact lens based on phase modulation technology | |
| JP6785857B2 (en) | A refractive power determination device configured to determine the refractive power of an optical lens and a method for determining the refractive power of an optical lens. | |
| Timberlake et al. | The direct ophthalmoscope how it works and how to use it | |
| Sahu | Corneal topography: sirius | |
| CN113662706A (en) | Method of designing and manufacturing intraocular lenses | |
| Maheshwari et al. | Learning optics using vision | |
| CN115644796B (en) | Strabismus-based AC/A detection method and device | |
| Hasan et al. | An algorithm to differentiate astigmatism from Keratoconus in Axial Topgraphic images |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |