CN112237416A - Fundus multi-mode imaging system calibration method based on retinal surface blood vessel characteristics - Google Patents
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Abstract
The invention relates to a fundus multi-mode imaging system calibration method based on retinal surface blood vessel characteristics, and hardware comprises the following steps: the system comprises an OCT imaging optical path, a binocular fundus camera optical path, a near-infrared illumination optical path, a linear array camera, an area array camera, a near-infrared illumination light source, a near-infrared broadband light source, a computer, a human eye optical system and a retina; the calibration step of the fundus multi-mode imaging system comprises the following steps: focusing of an OCT optical imaging system and a binocular stereoscopic vision optical imaging system; OCT scanning imaging and binocular stereoscopic vision imaging; initializing the expressing sizes delta x, delta y and delta z of OCT unit pixels, and initializing the internal parameters of a binocular stereo vision camera, binocular stereo vision and the rotating and translating relations of an OCT system; OCT and binocular stereoscopic vision three-dimensional blood vessel imaging; extracting three-dimensional blood vessel characteristic points of OCT and binocular stereoscopic vision; matching OCT and binocular stereoscopic vision three-dimensional blood vessel characteristic points; calculating matching errors of OCT and binocular stereoscopic vision three-dimensional blood vessel characteristic points; and optimizing calibration parameters of the multi-modal imaging system.
Description
Technical Field
The invention relates to the technical field of a multi-modal imaging system calibration method, in particular to an eyeground multi-modal imaging system calibration method based on retinal surface blood vessel characteristics.
Background
The retina of the eye fundus of the human eye can acquire rich external information and is very sensitive to the health condition of people. Most of the eye diseases are caused by the pathological changes of the retina of the eye, such as glaucoma, macular degeneration, cataract and the like. Meanwhile, the blood vessels on the retina are the only blood vessels on the whole body of the human body which can be directly detected, and some systemic diseases, chronic diseases and the like can be screened according to pathological information on the blood vessels. Therefore, it is a popular matter of clinical medicine research to detect characteristic changes of blood vessels on the retina of the eye and to find some early symptoms of related diseases in time and to adopt effective treatment means.
In recent years, some imaging techniques have enriched our medical approaches. Optical Coherence Tomography (OCT) has been widely used in medical ophthalmic imaging because of its advantages such as non-contact and non-invasive properties, high resolution, and fast imaging. The monocular camera captures the fundus of the eye to obtain a two-dimensional image of the retinal surface structure, but there is some inconvenience in the diagnosis process, such as: when fundus macular degeneration is detected, information such as the spatial position of a pathological part, retinal blood vessel distribution and blood flow needs to be analyzed in many aspects, the information is difficult to observe only by a two-dimensional image, and three-dimensional reconstruction needs to be carried out on retinal surface information. The three-dimensional reconstruction of the monocular camera is greatly interfered by the eyeball motion. Compared with a monocular system, the binocular stereoscopic vision technology can simulate human eyes to obtain fusion of images, and real-time shooting of the same area from different positions is achieved. The binocular stereo vision technology is beneficial to realizing accurate three-dimensional reconstruction.
The OCT system is used for imaging optical signal processing, can obtain tomography in the depth direction, and is easy to interfere surface imaging by system reflection. The binocular stereo vision is to directly image visual information and can obtain clear three-dimensional images. The two imaging systems are used for detection, so that the interference caused by the movement of human eyes can be reduced, and the OCT images are corrected by using binocular vision images while the binocular stereoscopic vision tracks the movement of eyeballs, so that the multimode imaging of the fundus retinas is realized. Therefore, the multi-mode fundus retinal imaging system designed by using the binocular stereo vision technology and the OCT technology is particularly important for providing a reliable technology for medical diagnosis.
The calibration and fusion of images of the OCT system and the binocular stereoscopic vision system are realized, firstly, a coordinate conversion relation between the OCT system and the binocular stereoscopic vision system is required to be established, and the calibration of the multi-mode system is realized. Because the human eye optical component is a part of the optical imaging system, and the parameters of the human eye optical component of each person are generally different, the calibration of the multi-mode fundus retina imaging system cannot adopt an off-line pre-calibration mode, and needs in-vivo calibration. The provided fundus multi-mode imaging system calibration method based on the retinal surface blood vessel characteristics can realize the in-vivo calibration of the OCT and binocular stereo vision multi-mode system by acquiring the OCT three-dimensional scanning image and the binocular stereo vision system image in real time.
Disclosure of Invention
The invention aims to provide a fundus multi-mode imaging system calibration method based on retinal surface blood vessel characteristics.
In order to solve the above problems, the embodiment of the present invention provides a method for performing an overall optimization calibration on parameters to be calibrated (unit pixel representative sizes Δ x, Δ y, Δ z), parameters in a camera for binocular stereoscopic vision, and parameters outside the camera for binocular stereoscopic vision (a relationship between rotation and translation of the camera for binocular and the OCT coordinate system), in an OCT coordinate system, by setting a world coordinate system of a binocular stereoscopic vision system as an OCT coordinate system, and using a minimum matching error between three-dimensional blood vessel characteristic points obtained by OCT and three-dimensional blood vessel characteristic points obtained by binocular stereoscopic vision as an optimization objective function.
The hardware system comprises:
the system comprises an OCT imaging optical path, a binocular fundus camera optical path, a near-infrared illumination optical path, a linear array camera, an area array camera, a near-infrared illumination light source, a near-infrared broadband light source, a computer, a human eye optical system and a retina;
broadband near infrared light output by a near-infrared broadband light source is incident to a retina through an OCT imaging optical path and an eye optical system, signals scattered and reflected from different depths of the retina are subjected to the eye optical system and the OCT imaging optical path, interference spectrum signals are obtained on a linear array camera, one-dimensional A scanning signals of the OCT are obtained through operations such as interpolation, Fourier transform and the like, and three-dimensional C scanning signals of the OCT are obtained through two-dimensional scanning of light beams;
the infrared light output by the near-infrared illumination light source illuminates the retina through the near-infrared illumination light path and the human eye optical system, and the image on the surface of the retina is imaged on the two area-array cameras after passing through the human eye optical system and the optical paths of the binocular fundus camera.
The method comprises the following calibration steps:
step a, focusing of an OCT optical imaging system and a binocular stereoscopic vision optical imaging system;
step b, OCT scanning imaging and binocular stereoscopic vision imaging;
step c, initializing representative sizes delta x, delta y and delta z of OCT unit pixels, and initializing internal parameters of a binocular stereo vision camera, binocular stereo vision and the rotation and translation relations of an OCT system;
step d, OCT and binocular stereoscopic vision three-dimensional blood vessel imaging;
step e, extracting three-dimensional blood vessel bifurcation characteristic points of OCT and binocular stereo vision;
step f, matching the three-dimensional blood vessel characteristic points of the OCT and binocular stereo vision and calculating matching errors;
and g, judging whether the optimization is finished or not according to the error or the iteration times, if not, optimizing the delta x, the delta y and the delta z, the binocular stereoscopic vision camera internal parameters, the binocular stereoscopic vision and OCT system rotation and translation relation parameters, and returning to the step d.
Wherein, step a specifically includes:
step a1, focusing of a common objective lens of the OCT optical imaging system and the binocular stereovision optical imaging system;
step a2, focusing of the OCT linear array camera;
step a3, focusing of the area array camera of the binocular stereoscopic vision optical imaging system.
Wherein, step b specifically includes:
b1, the OCT system carries out operations such as interpolation, Fourier transform and the like on the interference spectrum acquired by the linear array camera to obtain a one-dimensional A scanning signal of the OCT, and then generates a three-dimensional C scanning signal of the OCT through the scanning process of the two-dimensional scanning galvanometer to form a three-dimensional image;
b2, the binocular stereo vision system obtains the two-dimensional images of the left camera and the right camera of the retina through the shooting of the left area-array camera and the right area-array camera;
and b3, repeating the b1 and b2 to obtain multiple sets of three-dimensional original data of the OCT and binocular stereovision.
Wherein, step c specifically includes:
c1, initializing the actual sizes delta x, delta y and delta z expressed by the unit pixels in the three directions of the OCT image X, Y, Z;
and c2, initializing coordinate rotation and translation relations of the internal parameters of the binocular stereoscopic vision camera, the binocular stereoscopic vision system and the OCT system.
Wherein, step d specifically includes:
step d1, determining the actual imaging range of the OCT according to the initialized values or optimized values of the actual sizes delta x, delta y and delta z expressed by the unit pixels in the three directions of the OCT image X, Y, Z;
d2, segmenting the three-dimensional OCT image based on the intensity characteristics of the OCT signal, and extracting a three-dimensional retinal blood vessel image;
d3, extracting retinal blood vessels from the two-dimensional images of the binocular stereoscopic vision left and right cameras;
d4, matching retinal blood vessels in the two-dimensional images of the left camera and the right camera with binocular stereo vision;
and d5, obtaining the binocular stereoscopic vision retina three-dimensional blood vessel image under the coordinates of the OCT system by utilizing the internal parameters of the binocular stereoscopic vision system, the coordinate rotation and translation relations of the binocular stereoscopic vision system and the OCT system.
Wherein, step e specifically includes:
step e1, extracting each blood vessel bifurcation characteristic point of the OCT retina three-dimensional blood vessel image;
and e2, extracting each blood vessel bifurcation characteristic point of the binocular stereoscopic vision retina three-dimensional blood vessel image.
Wherein, step f specifically includes:
step f1, matching the OCT and binocular stereo vision three-dimensional blood vessel characteristic points;
and f2, calculating matching errors of the three-dimensional blood vessel characteristic points of the OCT and binocular stereo vision.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a hardware composition and a functional block diagram of a fundus multi-modality imaging system based on retinal surface blood vessel characteristics according to an embodiment of the present invention;
FIG. 2 is a calibration procedure of a fundus multi-modality imaging system based on retinal surface blood vessel characteristics according to an embodiment of the present invention;
fig. 3 is a multi-modality imaging system coordinate system established in an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The embodiment of the invention provides a calibration method of an eyeground multi-mode imaging system based on retinal surface blood vessel characteristics, the basic composition and principle of the calibration method are shown in figure 1, and a hardware system comprises: an OCT imaging optical path 6, a binocular fundus camera optical path 8, a near infrared illumination optical path 7, a linear array camera 3, an area array camera 5, a near infrared illumination light source 4, a near infrared broadband light source 2, a computer 1, a human eye optical system 9 and a retina 10; broadband near infrared light output by the near-infrared broadband light source 2 is incident to a retina 10 through an OCT imaging optical path 6 and an eye optical system 9, signals scattered and reflected from different depths of the retina 10 pass through the eye optical system 9 and the OCT imaging optical path 6, interference spectrum signals are obtained on the linear array camera 3, one-dimensional A scanning signals of OCT are obtained through operations such as interpolation, Fourier transform and the like, and three-dimensional C scanning signals of OCT are obtained through two-dimensional scanning of light beams; the infrared light output by the near-infrared illumination light source 4 illuminates the retina 10 through the near-infrared illumination light path 7 and the human eye optical system 9, and the surface image of the retina 10 is imaged on the two area array cameras 5 after passing through the human eye optical system 9 and the binocular fundus camera light path 8.
The embodiment of the invention provides a fundus multi-mode imaging system calibration method based on retinal surface blood vessel characteristics, which comprises the following steps as shown in figure 2:
step a (S1 and S2), focusing of the OCT optical imaging system and the binocular stereoscopic optical imaging system;
step b (S3 and S4), OCT scanning imaging and binocular stereovision imaging;
c (S5 and S6), initializing representative sizes delta x, delta y and delta z of OCT unit pixels, and initializing internal parameters of a binocular stereo vision camera, binocular stereo vision and rotation and translation relations of an OCT system;
step d (S7 and S8), OCT and binocular stereoscopic three-dimensional blood vessel imaging;
step e (S9 and S10), extracting three-dimensional blood vessel bifurcation characteristic points of OCT and binocular stereo vision;
f (S11 and S12), OCT and binocular stereo vision three-dimensional blood vessel feature point matching and matching error calculation;
and g (S13, S14 and S15), judging whether the optimization is finished or not according to the error magnitude or the iteration times, if the optimization is not finished, optimizing the delta x, the delta y and the delta z, the binocular stereoscopic vision camera internal parameters, the binocular stereoscopic vision and OCT system rotation and translation relation parameters, returning to the step d, and if the optimization is finished, obtaining the optimal calibration result.
Wherein, step a specifically includes:
step a1, focusing of a common objective lens of the OCT optical imaging system and the binocular stereovision optical imaging system;
step a2(S1), focusing of the OCT line camera; (ii) a
Step a3(S2), and focusing of the area array camera of the binocular stereoscopic vision optical imaging system.
Wherein, step b specifically includes:
b1(S3), the OCT system carries out operations such as interpolation, Fourier transform and the like on interference spectra collected by the linear array camera to obtain a one-dimensional A scanning signal of the OCT, and then generates a three-dimensional C scanning signal of the OCT through the scanning process of the two-dimensional scanning galvanometer to form a three-dimensional image;
step b2(S4), the binocular stereo vision system obtains the left and right camera two-dimensional images of the retina through the shooting of the left and right area-array cameras;
and b3, repeating the b1 and b2 to obtain multiple sets of three-dimensional original data of the OCT and binocular stereovision.
Wherein, step c specifically includes:
c1(S5), initializing the actual sizes Δ x, Δ y, Δ z of the unit pixels in the three directions of the OCT image X, Y, Z, if the number of pixels in the three directions of X, Y, Z is Nx、Ny、NzThen the actual imaging range of OCT is Δ x Nx、Δy*Ny、Δz*Nz；
Step c2(S6), initialization of intrinsic parameters and extrinsic parameters of the binocular camera, wherein the intrinsic parameters include fx(focal length of camera in X-direction), fy(focal length of camera in Y-direction), u0、v0(coordinates of the principal point in the image pixel coordinate system), radial distortion k1, k2, k3 and tangential distortion p1, p2, the extrinsic parameters include rotation R (including three variables Rx, Ry, Rz) and translation relation T (including three variables Tx, Ty, Tz) between the camera coordinate system and the OCT coordinate system, and thus each camera includes 18 parameters:
Para＝{Δx、Δy、Δz、fx、fy、u0、v0、k1、k2、k3、p1、p2、Rx、Ry、 Rz、Tx、Ty、Tz}；
radial distortion correction formula:
xcorrected＝x(1+k1r2+k2r4+k3r6)
ycorrected＝y(1+k1r2+k2r4+k3r6)
tangential distortion correction formula:
xcorrected＝x+[2p1xy+p2(r2+2x2)]
ycorrected＝y+[p1(r2+2y2)+2p2xy]
the correlation between the image pixel coordinate system (u, v) and the image physical coordinate system (x, y) can be represented by the following formula:
image pixel coordinate system (u, v) and world coordinate system (O)WXWYWZW) The correlation between them can be expressed by the following formula:
the world coordinate system is the OCT coordinate system O shown in figure 3OCT。
Wherein, step d specifically includes:
step d1, determining the actual imaging range of the OCT according to the initialized values or optimized values of the actual sizes delta x, delta y and delta z expressed by the unit pixels in the three directions of the OCT image X, Y, Z;
d2, segmenting the three-dimensional OCT image based on the intensity characteristics of the OCT signal, and extracting a three-dimensional retinal blood vessel image;
step d3, extracting retinal blood vessels in the two-dimensional images of the left camera and the right camera of the binocular stereoscopic vision based on a NiBlack local threshold method;
d4, matching retinal blood vessels in the two-dimensional images of the left camera and the right camera with binocular stereo vision;
and d5, obtaining the binocular stereoscopic vision retina three-dimensional blood vessel image under the coordinates of the OCT system by utilizing the internal parameters of the binocular stereoscopic vision system, the coordinate rotation and translation relations of the binocular stereoscopic vision system and the OCT system.
Wherein, step e specifically includes:
step e1, extracting each blood vessel bifurcation characteristic point of the OCT retina three-dimensional blood vessel image based on the deep learning method;
and e2, extracting the blood vessel bifurcation characteristic points of the binocular stereoscopic vision retina three-dimensional blood vessel image based on the depth learning method.
Wherein, step f specifically includes:
f1, matching the three-dimensional blood vessel characteristic points based on the nearest neighbor principle OCT and the binocular stereo vision;
and f2, calculating matching errors of the three-dimensional blood vessel characteristic points of the OCT and binocular stereo vision.
Wherein, step g specifically comprises:
step g1, determining an optimization objective function as:
n in the formula is the number of the feature points of all three-dimensional blood vessels after matching,
is the nth OCT three-dimensional blood vessel characteristic point coordinate,
coordinates of the nth binocular stereo vision three-dimensional blood vessel feature points are obtained;
step g2, performing Para of the left camera and the right camera based on methods such as genetic algorithm and the likeLeft side ofAnd ParaRight sideOptimizing and completing minimum value solving, wherein the steps are as follows:
in the genetic algorithm, specific input parameters are as follows: size num of the initial population; boundary conditions bounds (value ranges of each parameter); probability of mutation Pm(ii) a Cross probability Pc(ii) a The maximum iteration number N; an objective function f. The output parameters are: optimal solution vector (i.e. corresponding Para when the objective function is minimal)Left side ofAnd ParaRight side) (ii) a The specific settings for the genetic algorithm are as follows:
step 1: input ParaLeft side ofAnd ParaRight sideParameters, and giving boundary conditions bounds to the input parameters;
step 2: initializing an initial population randomly;
and 3, step 3: calculating objective function values corresponding to individuals in the population, wherein the smaller the objective function value is, the higher the fitness value of the individual is, arranging the individuals in the population from high fitness value to low fitness value, and selecting the individual with high fitness value for copying;
and 4, step 4: and selecting individuals needing cross operation from the population based on the tournament selection strategy, and then performing cross operation on the selected individuals by using an arithmetic cross operator to generate new filial generations.
And 5, step 5: randomly mutating individuals needing mutation operation in a population within a boundary range to generate new filial generations, if the parameter value of the individuals in the population exceeds a boundary upper limit, giving the boundary upper limit to the parameter, if the parameter value of the individuals in the population is lower than a boundary lower limit, giving a corresponding boundary lower limit to the parameter, and after copying, crossing and mutating operations are carried out on an initial population, generating a new population;
and 6, step 6: and solving objective function values corresponding to individuals in the new population, performing ascending arrangement on the objective function values, outputting the individual with the highest fitness value as the optimal solution vector in each iteration, and taking the corresponding objective function value as the minimum objective function value of the current iteration.
And 7, step 7: and judging whether the termination condition is met, if not, returning to the step 3 to continue iteration.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. A fundus multi-mode imaging system calibration method based on retinal surface blood vessel characteristics is characterized by comprising the following steps:
the system comprises an OCT imaging optical path, a binocular fundus camera optical path, a near-infrared illumination optical path, a linear array camera, an area array camera, a near-infrared illumination light source, a near-infrared broadband light source, a computer, a human eye optical system and a retina;
broadband near infrared light output by the near-infrared broadband light source is incident to a retina through an OCT imaging optical path and an eye optical system, and signals scattered and reflected from different depths of the retina are subjected to the eye optical system and the OCT imaging optical path to obtain interference spectrum signals on the line array camera;
the infrared light output by the near-infrared illumination light source illuminates the retina through the near-infrared illumination light path and the human eye optical system, and the image on the surface of the retina is imaged on the two area-array cameras after passing through the human eye optical system and the optical paths of the binocular fundus camera.
2. A fundus multi-mode imaging system calibration method based on retinal surface blood vessel characteristics is characterized by comprising the following calibration steps:
step a, focusing of an OCT optical imaging system and a binocular stereoscopic vision optical imaging system;
step b, OCT scanning imaging and binocular stereoscopic vision imaging;
step c, initializing representative sizes delta x, delta y and delta z of OCT unit pixels, and initializing internal parameters of a binocular stereo vision camera, binocular stereo vision and the rotation and translation relations of an OCT system;
step d, OCT and binocular stereoscopic vision three-dimensional blood vessel imaging;
step e, extracting three-dimensional blood vessel bifurcation characteristic points of OCT and binocular stereo vision;
step f, matching the three-dimensional blood vessel characteristic points of the OCT and binocular stereo vision and calculating matching errors;
and g, judging whether the optimization is finished or not according to the error or the iteration times, if not, optimizing the delta x, the delta y and the delta z, the binocular stereoscopic vision camera internal parameters, the binocular stereoscopic vision and OCT system rotation and translation relation parameters, and returning to the step d.
3. A fundus multi-modal imaging system calibration method based on retinal surface blood vessel characteristics according to claim 2, characterized in that the step a specifically comprises:
step a1, focusing of a common objective lens of the OCT optical imaging system and the binocular stereovision optical imaging system;
step a2, focusing of the OCT linear array camera;
step a3, focusing of the area array camera of the binocular stereoscopic vision optical imaging system.
4. A fundus multi-modal imaging system calibration method based on retinal surface vascular characteristics according to claim 2, wherein the step b specifically comprises:
b1, the OCT system carries out operations such as interpolation, Fourier transform and the like on the interference spectrum acquired by the linear array camera to obtain a one-dimensional A scanning signal of the OCT, and then generates a three-dimensional C scanning signal of the OCT through the scanning process of the two-dimensional scanning galvanometer to form a three-dimensional image;
b2, the binocular stereo vision system obtains the two-dimensional images of the left camera and the right camera of the retina through the shooting of the left area-array camera and the right area-array camera;
and b3, repeating the b1 and b2 to obtain multiple sets of three-dimensional original data of the OCT and binocular stereovision.
5. A fundus multi-modal imaging system calibration method based on retinal surface vascular characteristics according to claim 2, wherein the step c specifically comprises:
c1, initializing the actual sizes delta x, delta y and delta z expressed by the unit pixels in the three directions of the OCT image X, Y, Z;
and c2, initializing coordinate rotation and translation relations of the internal parameters of the binocular stereoscopic vision camera, the binocular stereoscopic vision system and the OCT system.
6. A fundus multi-modal imaging system calibration method based on retinal surface vascular characteristics according to claim 2, wherein the step d specifically comprises:
step d1, determining the actual imaging range of the OCT according to the initialized values or optimized values of the actual sizes delta x, delta y and delta z expressed by the unit pixels in the three directions of the OCT image X, Y, Z;
d2, segmenting the three-dimensional OCT image based on the intensity characteristics of the OCT signal, and extracting a three-dimensional retinal blood vessel image;
d3, extracting retinal blood vessels from the two-dimensional images of the binocular stereoscopic vision left and right cameras;
d4, matching retinal blood vessels in the two-dimensional images of the left camera and the right camera with binocular stereo vision;
and d5, obtaining the binocular stereoscopic vision retina three-dimensional blood vessel image under the coordinates of the OCT system by utilizing the internal parameters of the binocular stereoscopic vision system, the coordinate rotation and translation relations of the binocular stereoscopic vision system and the OCT system.
7. A fundus multi-modal imaging system calibration method based on retinal surface vascular characteristics according to claim 2, wherein the step e specifically comprises:
step e1, extracting each blood vessel bifurcation characteristic point of the OCT retina three-dimensional blood vessel image;
and e2, extracting each blood vessel bifurcation characteristic point of the binocular stereoscopic vision retina three-dimensional blood vessel image.
8. A fundus multi-modal imaging system calibration method based on retinal surface vascular characteristics according to claim 2, wherein step f specifically comprises:
step f1, matching the OCT and binocular stereo vision three-dimensional blood vessel characteristic points;
and f2, calculating matching errors of the three-dimensional blood vessel characteristic points of the OCT and binocular stereo vision.
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| 党彦辉,刘国忠: "一种基于双目立体视觉追踪眼球运动的方法" * |
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