PS-OCT Visual Recognition Improvement Method and System Based on Polarization Multi-parameter Fusion

Abstract

The present invention discloses a method and system for improving the visibility of PS-OCT based on polarization multi-parameter fusion. The method includes obtaining an original PS-OCT image and performing preprocessing to obtain the contour of the sample to be measured, filtering a QUV three-dimensional array constructed based on the Stokes matrix using the contour of the sample to be measured to obtain a polarization state image, calculating a local optical axis image and a local phase retardation image using a Poincaré sphere based on the polarization state obtained by calculation, performing average gradient fusion or weighted fusion on the obtained multiple types of images, and obtaining a final PS-OCT image after fusion. This method and system do not require the input of polarized light that interferes with each other, only require a single input polarization state, and have low requirements for the complexity of the system. It has a wide range of applications and may be used in endoscope-based PS-OCT or galvano-meter-based planar scanning PS-OCT.

Description

Technical Field

[0001] The present invention relates to the field of optical coherence tomography technology, and in particular to a method and system for improving the visibility of PS-OCT based on polarization multi-parameter fusion.

Background Art

[0002] Optical coherence tomography (OCT) is the imaging method with the highest recognized resolution in the current field of intravascular imaging. However, the conventional OCT system that images based on tissue intensity is insufficient in terms of tissue characteristic analysis. As a result, it is difficult for humans or AI to distinguish different biological tissues such as different membrane structures and plaques when analyzing images. The intensity-based OCT currently used in clinics has a blurred image representation result when in use. However, in addition to the intensity information carried by light, light further contains other additional characteristics. By using the additional characteristics carried by light, analysis or quantitative measurement can be performed on different tissues to improve the visibility. The principle of polarization-sensitive OCT (PS-OCT) is based on this. Multiple types of biological tissues or samples can realize the polarization modulation of the input light, change the polarization state of the input light, and obtain a method for additionally representing the visibility of the biological tissue or sample, and obtain characteristic information other than intensity.

[0003] The PS-OCT technology changes the polarization state of the input polarization light by propagating and reflecting the polarized light incident on the tissue of the sample within the medium of the sample, and demodulates the polarization state of the back-reflected light, thereby obtaining the polarization characteristic information of the sample and realizing depth-resolved imaging by the birefringence of the tissue. This special property is particularly important in samples or biological tissues. Proteins or biopolymer substances with isotropic tissue structures in blood vessels, such as collagen and actin, can change the polarization state of the incident light. It induces the occurrence of form birefringence and generates measurable optical signals. By measuring the polarization state of the back-reflected light or back-scattered light from the sample or biological tissue, polarization information of the sample in terms of depth resolution, such as phase retardation and the direction of the optical axis, can be obtained.

[0004] Currently, the conventional techniques for calculating PS-OCT polarization information are mostly phase retardation algorithms and polarization degree algorithms based on the Jones matrix or Mueller matrix. However, due to the obvious diattenuation effect and depolarization effect in the system, the polarization visibility calculated by the current algorithms is low. Therefore, a method based on multi-parameter fusion analysis is proposed to calculate polarization characteristic information and improve the visibility of polarization images.

Summary of the Invention

Problems to be Solved by the Invention

[0005] Therefore, the present invention aims to provide a method and system for improving the visibility of PS-OCT based on polarization multi-parameter fusion to realize color images of different tissue structures in a sample, more intuitively assist doctors in diagnosis, and reduce the learning difficulty of conventional intravascular imaging products.

Means for Solving the Problems

[0006] To achieve the above object, the method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to the present invention is Obtain the original PS-OCT image, perform preprocessing on the original PS-OCT image, and obtain the contour of the sample waiting to be measured, S1, Use the contour of the sample waiting to be measured to filter the QUV three-dimensional array constructed based on the Stokes matrix to obtain a polarization state image, S2, Based on the calculated polarization state, use the Poincaré sphere to calculate the local optical axis image and the local phase retardation image, S3, Include step S4 of performing average gradient fusion or weighted fusion on the multiple types of images obtained in S2 and S3, and obtaining the final PS-OCT image after fusion.

[0007] More preferably, in S1, the preprocessing is Multiply the H-channel data and V-channel data of the two polarization states in the original PS-OCT image by a cosine taper window for shaping, S101, Perform Fourier transform on the shaped data to obtain the Fourier domain matrices of the H-channel and V-channel, S102, After taking the average value of the Fourier domain matrices of the H-channel and V-channel, fuse the original images of the H-channel and V-channel as the original images of the H-channel and V-channel, S103, Include step S104 of filtering and removing noise from the fused image according to a set threshold to obtain the contour of the sample waiting to be measured.

[0008] More preferably, in S103, when fusing the original images of the H-channel and V-channel, the fusion formula is as follows:

Equation

[0009] More preferably, in S2, the polarization state image After normalizing the Stokes matrix, a QUV three-dimensional array is formed, and using the contour of the sample to be measured, the QUV three-dimensional array is filtered, the pixel points outside the contour are set to 0, and the QUV three-dimensional array is drawn in the RGB format to obtain a polarization state color image of the contour of the sample to be measured. This method is adopted

[0010] More preferably, in S3, when calculating the local optical axis image Using the Poincare sphere, the spatial normal vector B of the plane of the PS-OCT image n is extracted, the second dimension and the third dimension of B n are exchanged to obtain an x*y*3 matrix, which is filtered through the contour of the sample to be measured to obtain an optical axis image, further including that x and y represent the number of rows and columns of the pixels of the image

[0011] More preferably, the local phase retardation is calculated by adopting the following formula

Equation

[0012] More preferably, the local optical axis image is calculated by adopting the following formula

Equation

[0013] More preferably, in S4, performing average gradient fusion on multiple types of images includes a method of normalizing three types of images, namely the polarization state image, the local optical axis image, and the local phase retardation image, and fusing the calculation results of the three types of images using gradient features and adjustable fusion weight coefficients.

[0014] More preferably, in S4, performing weighted fusion on multiple types of images S401 of converting three types of images, namely the polarization state image, the local optical axis image, and the local phase retardation image, into grayscale images includes S402 of respectively performing grayscale feature fusion, shape feature fusion, and texture feature fusion on the three grayscale images, then fusing them again to obtain a final fused image, and obtaining a final PS-OCT image according to the following fusion formula [Equation] Among them, d i is a fusion coefficient, d1 = 0.4, d2 = 0.2, d3 = 0.4, and Fusimage i represents the image after grayscale feature fusion, shape feature fusion, and texture feature fusion.

[0015] More preferably, in S402, the grayscale feature fusion includes extracting grayscale feature values including the average value, variance, energy, slope, and kurtosis for the grayscale image adopting a weighted fusion method, fusing the original three types of images into a figure based on the five grayscale feature values respectively calculating the fused images of the five grayscale feature values and finally includes the process of fusing the fused images of the five grayscale feature values again so as to form one final grayscale fused image.

[0016] More preferably, in S402, the shape feature fusion includes performing extraction of shape features on three types of grayscale images, normalizing the central moment, obtaining shape features of seven invariant moments, using the seven shape features as a shape feature vector, forming one shape feature matrix, performing shape fusion using the shape feature matrix, and obtaining a shape fusion image.

[0017] More preferably, in S402, the texture feature fusion includes performing extraction of texture features including energy, entropy, contrast, and correlation on three types of grayscale images, constructing a texture feature vector using the texture features, fusing the three types of grayscale images according to the constructed four types of texture feature vectors, forming four types of texture feature images, and then fusing the four types of texture feature images according to equal weights to form a texture fusion image.

[0018] The present invention is used to implement the above-described PS-OCT visibility improvement method based on polarization multi-parameter fusion. An original PS-OCT image is acquired, preprocessing is performed on the original PS-OCT image, and an image acquisition module for acquiring the contour of a sample to be measured. Using the contour of the sample to be measured, filtering a QUV three-dimensional array constructed based on the Stokes matrix to obtain a polarization state image, and calculating a local optical axis image and a local phase retardation image using a Poincare sphere based on the polarization state obtained by calculation. An image processing module for performing the above operations. An image fusion module for performing average gradient fusion or weighted fusion on the above-obtained multiple types of images to obtain a final PS-OCT image after fusion is further provided. A PS-OCT visibility improvement system based on polarization multi-parameter fusion is provided.

Advantages of the Invention

[0019] The PS-OCT visibility improvement method and system based on polarization multi-parameter fusion disclosed in the present application have at least the following advantages compared with the prior art.

[0020] 1. The PS-OCT visibility improvement method and system based on polarization multi-parameter fusion according to the present application do not require the input of interfering polarizations during the acquisition of PS-OCT, only require a single input polarization state, and have low requirements for the complexity of the system. It has a wide range of applications and may be used in endoscope-based PS-OCT or in galvanometer-based planar scanning PS-OCT.

[0021] 2. The PS-OCT visibility improvement method and system based on polarization multi-parameter fusion according to the present application adopt multiple methods to calculate and fuse polarization information, and have higher visibility compared to the current existing technologies.

Brief Description of the Drawings

[0022]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Modes for Carrying Out the Invention

[0023] Hereinafter, the present invention will be described in more detail with reference to the drawings and specific embodiments.

[0024] As shown in FIG. 1, the PS-OCT visibility improvement method based on polarization multi-parameter fusion according to an embodiment of an aspect of the present invention is Obtain the original PS-OCT image, perform preprocessing on the original PS-OCT image, and acquire the contour of the sample waiting for measurement, S1, Filter the QUV three-dimensional array constructed based on the Stokes matrix by using the contour of the sample waiting for measurement to obtain the polarization state image, S2, Calculate the local optical axis image and the local phase retardation image by using the Poincaré sphere based on the polarization state obtained by calculation, S3, Include step S4 of performing average gradient fusion or weighted fusion on multiple types of images obtained in S2 and S3, and obtaining the final PS-OCT image after fusion.

[0025] In one embodiment of the present application, the process of preprocessing is Multiply the H-channel data and V-channel data of the two polarization states in the original PS-OCT image by a cosine taper window for shaping, S101, Perform Fourier transform on the shaped data to obtain the Fourier domain matrices of the H-channel and V-channel, S102, After taking the average value of the Fourier domain matrices of the H-channel and V-channel, fuse the original images of the H-channel and V-channel as the original images of the H-channel and V-channel, S103, Include step S104 of filtering and removing noise from the fused image according to a set threshold to obtain the contour of the sample waiting for measurement.

[0026] In S103, when fusing the original images of the H-channel and V-channel, the fusion formula is as follows:

Equation

[0027] In the process of specific implementation, the system acquires the A-scan data H and V in two orthogonal polarization states, draws a reference plane for them, applies a cosine taper window for shaping, adds dispersion compensation, and then performs FFT to obtain the Fourier domain IMG_H and IMG_V of the H and V channels. Since they are complex matrices of x*y*4, when displaying as an image, the absolute value is taken, and the average is taken along the third dimension to obtain the original images of the H and V channels. Due to the birefringence effect of polarization, phase delay occurs. As shown in Figure 2, there are two upper and lower images in both the H and V channels, resulting in a total of four images. By fusing these four images, the structural diagram of the sample to be measured can be obtained, and the fusion formula is as follows:

Equation

[0028] In S2, the polarization state image forms a QUV three-dimensional array after normalizing the Stokes matrix, filters the QUV three-dimensional array using the contour of the sample to be measured, sets the pixel points outside the contour to 0, and depicts the QUV three-dimensional array in the RGB format to obtain the polarization state color image of the contour of the sample to be measured.

[0029] The polarization state is S0 = e 2x + e 2y S1 = e 2x - e 2y S2 = 2e x e y cosθ, S3 = 2e x e yIt is represented by the Stokes parameter sinθ. Of course, S0 2 = S1 2 + S2 2 + S3 2 Therefore, in the formula, only three variables are independent. In the ideal case (i.e., when transmitted without loss), since S0 = constant, what is represented by S1, S2, and S3 is a single sphere. The sphere with S0 = 1 is called the Poincaré sphere, and each point on the spherical surface corresponds one-to-one with the fully polarized state of light. The following is the calculation process of the polarization state.

[0030] In step 1,

Number

[0031] In step 2, normalize, Q = S1 / S0, U = S2 / S0, V = S3 / S0, and Q, U, and V are the coordinates after normalization.

[0032] In step 3, filter. The filtering method may be, but is not limited to, median filtering, Gaussian filtering, mean filtering, imbox filtering, Wiener filtering, dilation, and contraction, etc.

[0033] In step 4, construct a three-dimensional array. After constructing Q, U, and V into a three-dimensional array Stokes of x * y * 3, filter Stokes with the contour Msk_Thr array, set the pixel points outside the contour to 0, and finally draw the obtained three-dimensional array in the RGB format to obtain the polarization state color image of the contour of the sample waiting to be measured.

[0034] As shown in FIGS. 2 to 3, when calculating the local optical axis image at S3, Using the Poincaré sphere to extract the spatial normal vector B of the plane of the PS-OCT image n and exchanging the second and third dimensions of B n to obtain a matrix of x*y*3, filtering through the contour of the sample to be measured, and obtaining an optical axis image, further including that x and y represent the number of rows and columns of the pixels of the image.

[0035] Using the above result Stokes of the polarization state, Stokes may be represented by the Poincaré sphere. In FIG. 3, P1, P2, P3 are three polarization states represented by the Stokes parameters (S1, S2, S3), located on the sphere, the plane a is the plane fitted by the three points P1, P2, P3, and A1 is the normal vector of the plane of a, that is, the optical axis required here. P1 is the incident polarization state or the input polarization state. P1 is incident on the surface layer of the sample and is directly reflected at the surface layer to become the output polarization state, and since the polarization information does not change, P1 is also the output polarization state. P2 and P3 are obtained by rotating P1 by a certain angle around the optical axis A1 of the sample. P1, P2, P3 are the output polarization states received by the balance detector,

Number

[0036] B n The method for calculating the optical axis based on is as follows.

[0037] First, B nExchange the second and third dimensions, obtain a matrix of x*y*3 (where x and y represent the number of rows and columns of the pixels in the image), and through filtering by Msk_Thr, an optical axis image can be obtained.

[0038] Furthermore, the local phase delay is calculated by adopting the following formula:

Equation

[0039] And after converting δ n into an RGB three-dimensional array and then filtering by Msk_Thr, the local phase delay LocDP image of the sample can be obtained.

[0040] B n The optical axis represented is the result of superimposing the birefringence effects of tissues at different depths. Since the cumulative birefringence effect causes distortion of the result deep inside the tissue, if the cumulative birefringence effect according to the depth is not removed, the real optical axis information deep inside the tissue, that is, the local optical axis such as A2 in the following figure, cannot be restored.

[0041] The calculation process of the local optical axis A n is as follows:

Equation

[0042] In S4, performing average gradient fusion on multiple types of images includes normalizing three types of images, namely the polarization state image, the local optical axis image, and the local phase retardation image, and fusing the calculation results of the three types of images by using the gradient feature and the adjustable fusion weight coefficient.

[0043] First, normalize the results of the three calculation methods and calculate them within the same range. [Number] In the formula, P represents six types of images, namely the polarization state, the local phase retardation, and the local optical axis, all of which are normalized by the above formula.

[0044] The arithmetic formula for calculating the gradient feature vector G is as follows: [Number] In the formula, M and N represent the width and height of the image respectively, and Δ x P(x, y) and Δ y P(x, y) represent the calculation of the differences in the x and y directions of the image P(x, y) respectively, and the arithmetic formula is as follows: [Number] Using the gradient feature to fuse the three calculation results, the fusion arithmetic formula is as follows: [Number] Wherein, a1 + a2 + a3, which are fusion weight coefficients, satisfy a1 + a2 + a3 = 1. By selecting different fusion weight coefficients, a clearer fusion image can be obtained, and the same applies hereinafter. The greater the amount of information in the image, the greater the weight. In a specific experiment, a1 = 0.4, a2 = 0.3, and a3 = 0.3.

[0045] In S4, performing weighted fusion on multiple types of images includes S401 that converts three types of images, namely a polarization state image, a local optical axis image, and a local phase retardation image, into grayscale images, and S402 that performs grayscale feature fusion, shape feature fusion, and texture feature fusion on the three grayscale images respectively, then fuses them again to obtain a final fusion image, and obtains a final PS - OCT image according to the following fusion formula,

Equation

[0046] In S402, the grayscale feature fusion performs extraction of grayscale feature values including mean value, variance, energy, slope, and kurtosis on the grayscale image, adopts a weighted fusion method to fuse the original three types of images into a figure based on the five grayscale feature values, calculates the fusion images of the five grayscale feature values respectively, and includes the process of fusing the fusion images of the five grayscale feature values after fusion again so as to finally form one grayscale fusion image.

[0047] In S402, the shape feature fusion includes performing extraction of shape features on the three grayscale images, normalizing the central moment to obtain the shape features of seven invariant moments, using the seven shape features as a shape feature vector to form one shape feature matrix, and performing shape fusion using the shape feature matrix to obtain a shape fusion image.

[0048] In S402, the texture feature fusion includes extracting texture features including energy, entropy, contrast, and correlation for three grayscale images, constructing a texture feature vector using the texture features, fusing the three grayscale images according to the constructed four texture feature vectors to form four texture feature images, and then fusing the four texture feature images according to equal weights to form a texture fusion image.

[0049] 1) Tone feature a. Extraction of tone feature The tone feature includes five statistics: the average value m, the variance v 2 , the energy e, the slope s, and the kurtosis u. The meanings and calculation formulas of these five statistics are as follows.

[0050] First,

Equation

[0051] The average value m represents the average value of the energy of the image, and the calculation formula is as follows:

Equation

Equation

Equation

Number

Number

[0052] b. Tone feature fusion Based on the five tone features, an approach of weighted fusion is adopted. The results of the three calculation methods are respectively fused into five feature value diagrams. The feature vector is denoted as h, and h = (m, v 2 , e, s, u). The feature vectors of the three calculation results are respectively denoted as h1, h2, and h3. The weights of each feature quantity are as follows:

Number

[0053] The fused images F j of the five feature quantities are respectively calculated. The formula is as follows:

Number

[0054] The five fused feature quantities are fused again to obtain an image of tone features. The formula is as follows:

Number

[0055] 2) Shape features a. Extraction of shape features For the discrete digital image P(x, y), the p + q-th order standard moment of the image is shown as follows:

Number

[0056] The p + q-th order central moment is shown as follows:

Number

Number

Number

Number

Number

[0057] b. Shape feature fusion The shape feature vector is represented by M, and M = (φ1, φ2, φ3, φ4, φ5, φ6, φ7).

[0058] One matrix composed of the shape feature vectors of the three calculation results, denoted as M1, M2, and M3, is shown as follows:

Number

Number

[0059] 3) Texture feature a. Extraction of texture features The extraction of texture features is based on the gray-level co-occurrence matrix. The gray-level histogram can directly describe the distribution of the gray level of only one pixel, while the gray-level co-occurrence matrix can describe the distribution of the combined gray levels of two pixels. Let a point in the image be (x, y), and its gray-level distribution be (gx, gy). When (x, y) moves, the point (x + i, y + j) is obtained, and the corresponding (gx’, gy’) is also generated. Count the number of occurrences of the gray-level values appearing in one image, and form all the gray-level values into a square matrix. Normalize the number of occurrences of a certain gray-level value and the total number of occurrences to obtain P(gx, gy), that is, the probability of occurrence, which is called the gray-level co-occurrence matrix. The normalization formula of the gray-level co-occurrence matrix is as follows, where Z represents the width and height of the square matrix image, that is, the size of the image is Z×Z.

Number

[0060] Since the gray-level co-occurrence matrix alone cannot fully represent the texture features of the image, four scalars of energy, entropy, contrast, and correlation are introduced to supplementarily represent the texture features of the image. The meanings and calculation methods of these four scalars are as follows.

[0061] Regarding the energy E, the numerical value of the energy can not only describe the uniformity of the tone distribution, but also express the roughness situation of the texture to a certain extent. When the numerical values of all the parameters in the tone co-occurrence matrix P(gx, gy) are equal, E is relatively small, and when the magnitudes of the parameter numerical values are clearly distinguishable, E becomes larger. When the parameters in the tone co-occurrence matrix are close to the center, E is large, which indicates that the texture of the image is uniform and the changes are regular. The calculation method is, as shown in the following formula, to square each parameter of the elements in the tone co-occurrence matrix P(gx, gy) first and then sum them up.

Number

[0062] Regarding the entropy S, the entropy is used to indicate the complexity of the texture of the image. When the values of the co-occurrence matrix are relatively uniform, the entropy is large. Its calculation formula is as follows.

Number

[0063] Regarding the contrast I, the contrast is used to express the sharpness degree of the image. The smaller the contrast is, the shallower the grooves for expressing the unevenness degree of the object surface become, and the lower the sharpness degree of the image is. Its calculation formula is as follows.

Number

[0064] Regarding the correlation R, the correlation is used to indicate the identity degree of the parameters of the tone co-occurrence matrix P(gx, gy) in the horizontal and vertical directions. When the value of the correlation in a certain direction is larger than that in other directions, since the texture characteristics in that direction are obvious, the correlation can be used to find the direction with relatively strong texture. Its calculation formula is as follows,

Number

Number

[0065] b. Texture feature fusion For energy E, entropy S, contrast I, and correlation R, construct a texture feature vector Y = (E, S, I, R). The feature vectors of the three operation result images are Y1, Y2, and Y3 respectively, and the calculation formula for the weights is as follows.

Number

[0066] First, fuse the different texture features of the three images respectively to obtain four fused feature images. The fusion formula is as follows.

Number

[0067] Finally, fuse the four feature images. The fusion formula is as follows.

Number

[0068] 4) Feature weighted fusion The fusion results of tone, shape, and texture are obtained respectively by the above three features. Then, fuse the results of these three features again to obtain the final fused image. The fusion formula is as follows.

Number

Number

[0069] As shown in Figure 5, the present invention further provides a PS-OCT visibility improvement system based on polarization multi-parameter fusion for implementing the above-mentioned PS-OCT visibility improvement method based on polarization multi-parameter fusion. It includes an image acquisition module that acquires the original PS-OCT image, performs preprocessing on the original PS-OCT image, and acquires the contour of the sample waiting to be measured, and an image processing module that uses the contour of the sample waiting to be measured to filter the QUV three-dimensional array constructed based on the Stokes matrix, obtains the polarization state image, the degree of polarization DOPU image, and the phase delay DPPR image, and calculates the optical axis image, the local optical axis image, and the local phase delay image using the Poincaré sphere based on the polarization state obtained by calculation, and an image fusion module that performs average gradient fusion or weighted fusion on the above-mentioned obtained multiple types of images to obtain the final PS-OCT image after fusion.

[0070] Of course, the above embodiments are only examples given for clear explanation and do not limit the embodiments. A person skilled in the art can further make other different forms of changes or variations based on the above description. Here, it is neither necessary nor possible to list all embodiments. The obvious changes or variations derived therefrom are also within the protection scope of the present invention.

Claims

1. S1: Obtain the original PS-OCT image, perform preprocessing on the original PS-OCT image, and obtain the contour of the sample to be measured; S2: Use the contour of the sample to be measured to filter the QUV three-dimensional array constructed based on the Stokes matrix to obtain a polarization state image; S3: Calculate a local optical axis image and a local phase retardation image using a Poincaré sphere based on the calculated polarization state; S4: Perform average gradient fusion or weighted fusion on the multiple types of images obtained in S2 and S3, and obtain a final PS-OCT image after fusion, A method for improving the visibility of PS-OCT based on polarization multi-parameter fusion, characterized in that.

2. In S1, the preprocessing includes: S101: Multiply the H-channel data and V-channel data of the two polarization states in the original PS-OCT image by a cosine taper window for shaping; S102: Perform Fourier transform on the shaped data to obtain Fourier domain matrices for the H-channel and V-channel; S103: After taking the average value of the Fourier domain matrices for the H-channel and V-channel, fuse the original images of the H-channel and V-channel as the original images of the H-channel and V-channel; S104: Filter and remove noise from the fused image according to a set threshold to obtain the contour of the sample to be measured, A method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to claim 1, characterized in that.

3. In S103, when fusing the original images of the H-channel and V-channel, the fusion formula is as follows: 【Number 1】 Stru ｔｏｔａｌ is the fused image, pH １ , pH ２ are the two images above and below the H channel respectively, pV １ and pV ２ are the two images above and below the V channel respectively. A method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to claim 2, characterized in that.

4. In S2, the polarization state image is obtained by: Normalize the Stokes matrix to form a QUV three-dimensional array, use the contour of the sample to be measured to filter the QUV three-dimensional array, set pixel points outside the contour to 0, draw the QUV three-dimensional array in RGB format, and obtain a polarization state color image of the contour of the sample to be measured, A method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to claim 1, characterized in that.

5. In S3, when calculating the local optical axis image, Using the Poincaré sphere, extract the spatial normal vector B of the plane of the PS-OCT image ｎ and, after exchanging the second and third dimensions of B ｎ to obtain a matrix of x*y*3, filter through the contour of the sample to be measured, obtain an optical axis image, further including that x and y represent the number of rows and columns of the pixels of the image The method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to claim 1, characterized in that...

6. The local phase retardation is calculated by adopting the following mathematical formula: 【Number 2】 δ ｎ is the local phase delay, and N ｎ is the normal vector in the n-th contact plane, and N ｎ－１ is the normal vector of the (n - 1)-th contact plane, The method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to claim 5, characterized in that...

7. The local optical axis image is calculated by adopting the following mathematical formula: 【Number 3】 A ｎ is the local optical axis, and B ｎ represents the optical axis obtained by superimposing the birefringence effects of tissues at different depths, and R ｎ is the 3×3 rotation matrix from the (n−1)-th optical axis to the n-th optical axis, and δ ｎ is the phase retardation of the n-th contact plane, and A ｎ (x), A ｎ (y), A ｎ (z) are the three dimensions of the local optical axis A of the three-dimensional array respectively, ｎ ​ The method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to claim 6, characterized in that...

8. In S4, performing average gradient fusion on multiple types of images means: Normalizing three types of images, namely the polarization state image, the local optical axis image, and the local phase retardation image, and using the gradient feature and the adjustable fusion weight coefficient to fuse the calculation results of the three types of images. The method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to claim 1, characterized in that...

9. In S4, performing weighted fusion on multiple types of images means: S401: Converting three types of images, namely the polarization state image, the local optical axis image, and the local phase retardation image, into grayscale images. After performing grayscale feature fusion, shape feature fusion, and texture feature fusion on the three grayscale images respectively, fusing them again to obtain the final fused image, and obtaining the final PS-OCT image according to the following fusion mathematical formula, including the method of S402. 【Number 4】 d ｉ is the fusion coefficient, and d １ = 0.4, d ２ = 0.2, d ３ = 0.4, and Fusiimage ｉ represents the image after tone feature fusion, shape feature fusion, and texture feature fusion. The method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to claim 1, characterized in that...

10. In S402, the grayscale feature fusion means: Extracting grayscale feature values including the average value, variance, energy, slope, and kurtosis for the grayscale image. Adopting the weighted fusion method, fusing the original three types of images into a figure based on the five grayscale feature values. Calculating the fused images of the five grayscale feature values respectively. Finally, including the process of fusing the fused images of the five grayscale feature values again so as to form one grayscale fused image. The method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to claim 9, characterized in that...

11. In S402, the shape feature fusion means extracting shape features for the three grayscale images, normalizing the central moment to obtain the shape features of seven invariant moments, using the seven shape features as a shape feature vector to form one shape feature matrix, and performing shape fusion using the shape feature matrix to obtain a shape fusion image. The method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to claim 9, characterized in that...

12. In S402, the texture feature fusion includes extracting texture features including energy, entropy, contrast, and correlation for three kinds of tone images, constructing a texture feature vector using the texture features, fusing the three kinds of tone images according to the constructed four kinds of texture feature vectors to form four kinds of texture feature images, and then fusing the four kinds of texture feature images according to equal weights to form a texture fusion image. The method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to claim 9, characterized in that...

13. It is used to implement the method for improving the visibility of PS-OCT based on polarization multi-parameter fusion according to any one of claims 1 to 12 above. An image acquisition module that acquires an original PS-OCT image, performs preprocessing on the original PS-OCT image, and acquires the contour of the sample to be measured. An image processing module that filters a QUV three-dimensional array constructed based on the Stokes matrix using the contour of the sample to be measured to obtain a polarization state image, and calculates a local optical axis image and a local phase retardation image using a Poincare sphere based on the obtained polarization state. An image fusion module that performs average gradient fusion or weighted fusion on the above-obtained multiple kinds of images to obtain a final PS-OCT image after fusion. A system for improving the visibility of PS-OCT based on polarization multi-parameter fusion, characterized in that...

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