OCT (Optical Coherence Tomography) image processing method and system for adaptive weighted bilateral filtering deconvolution

Abstract

The invention relates to an adaptive weighted bilateral filtering deconvolution OCT image processing method and system, and the method comprises the steps: carrying out the preprocessing of an original OCT signal, including background noise removal, wave number calibration, spectrum shaping and dispersion compensation, so as to obtain an OCT reconstructed image; enhancing the contrast ratio through histogram stretching, adjusting the space weight and the intensity weight of the OCT stretching image, and performing adaptive denoising on the image; and further optimizing the image by using a blind deconvolution algorithm, and iteratively updating the estimated image and a point spread function (PSF) to obtain a high-definition OCT reconstructed image. According to the method, efficient denoising and detail reservation of the OCT image are realized through adaptive weighted bilateral filtering and blind deconvolution technologies, so that a high-definition reconstructed image is obtained.

Description

Technical Field

[0001] The present invention relates to the field of OCT image processing, and in particular to an OCT image processing method based on adaptive weighted bilateral filtering deconvolution. Background Art

[0002] The point spread function (PSF) describes the spot distribution formed on the imaging plane by an ideal point light source passing through an optical system. It is used to evaluate and improve the quality of imaging systems and is a key tool in image restoration, deblurring, and optical system design and correction.

[0003] Blind deconvolution is an image processing technique that restores a sharp original image from a blurred image without knowing or fully understanding the point spread function (PSF). Unlike traditional deconvolution methods, which require the precise knowledge of the PSF, blind deconvolution algorithms can simultaneously estimate the PSF and restore the image content. This makes it particularly suitable for practical applications where direct PSF measurement is difficult, such as astronomical observations, microscopy, and general image restoration tasks. This method can effectively improve image quality and detail clarity.

[0004] Optical coherence tomography (OCT) is an important non-invasive imaging technology in medical imaging. OCT imaging can provide high-resolution three-dimensional images of tissue structures. However, due to the coherence of light during the imaging process, OCT images are often affected by speckle noise. Speckle noise is a type of random noise caused by coherent scattering during the imaging process. It significantly reduces image contrast and clarity, affecting doctors' ability to accurately identify and diagnose lesions. Summary of the Invention

[0005] To solve the above problems, the present invention proposes an adaptive weighted bilateral filtering deconvolution of OCT images. By combining adaptive bilateral filtering with blind deconvolution technology, the spatial kernel and intensity kernel parameters of the filter are dynamically adjusted to effectively improve the processing quality of OCT images, enhance the structure and detail integrity of the image, and provide technical support for clinical OCT imaging.

[0006] The specific plan is as follows:

[0007] In one aspect, an OCT image processing method using adaptive weighted bilateral filtering deconvolution is provided, comprising the following steps:

[0008] S1, preprocessing the original tissue OCT signal to obtain the OCT reconstructed image;

[0009] S2, performing histogram stretching on the OCT reconstructed image to obtain an OCT stretched image;

[0010] S3, adjusting the spatial weight and intensity weight of the OCT stretched image; performing adaptive denoising on the OCT stretched image based on the adjusted spatial weight and intensity weight to obtain an OCT denoised image;

[0011] S4, taking the OCT denoised image and the initialized point spread function PSF as input, performs a blind deconvolution algorithm to obtain the final OCT output image.

[0012] Furthermore, the pre-processing specifically includes:

[0013] The original tissue OCT signal is subjected to background noise removal, wavenumber calibration, spectrum shaping and dispersion compensation.

[0014] Furthermore, the histogram stretching specifically includes:

[0015] Determine the lowest non-zero intensity value L′ from the OCT reconstructed image min and the highest non-zero intensity value L′ max , a linear intensity transformation is applied to map the original intensity value I(x,y) of each pixel in the OCT reconstructed image to a new pixel value p(i,j); the linear intensity transformation formula is as follows:

[0016]

[0017] Among them, x is the horizontal coordinate of the image, which indicates the position of the pixel in the horizontal direction; y is the vertical coordinate of the image, which indicates the position of the pixel in the vertical direction; i is the horizontal coordinate of the transformed image, and j is the vertical coordinate of the transformed image; L max and L min represent the maximum and minimum intensity values ​​in the original OCT reconstructed image;

[0018] The pixel value of each pixel in the image processed by the linear intensity transform is counted, and the number of pixels at each intensity level is accumulated to complete the histogram stretching.

[0019] Furthermore, the intensity weight is an actual weight calculated by the intensity kernel. The intensity kernel is used to measure the intensity difference between two pixels in the image. The intensity weight is the output value of the intensity kernel. The calculation formula of the intensity weight is as follows:

[0020] σ r (m,n)=βμ p

[0021]

[0022] Among them, β represents the coefficient for adjusting the standard deviation of intensity; μ pRepresents the local mean; m and n represent the horizontal and vertical coordinates of the current center pixel, which are the core reference points for weight calculation; σ r (m,n) represents the local intensity standard deviation based on the center point (m,n); w r (m,n) represents the intensity weight; k and l represent the horizontal and vertical coordinates of a pixel traversed in the neighborhood window; I(m,n) represents the intensity value of the center pixel currently being processed; I(k,l) represents the intensity value of a point in the neighborhood.

[0023] Furthermore, the spatial weight is an actual weight calculated by the spatial kernel, which is a function used to measure the similarity of two pixels in the image in terms of spatial position. The spatial weight represents the output value of the spatial kernel. First, the local entropy value H[I p ]Calculate σ s After the spatial standard deviation, the spatial weight is calculated, and the formula is as follows:

[0024]

[0025] Among them, p(i) represents the probability of pixel value i appearing in the neighborhood; B N Represents the total number of pixel values ​​in the neighborhood; and Respectively represent the maximum and minimum values ​​of the preset spatial standard deviation; ζ represents a preset threshold value used to adjust the range of variation of the spatial kernel size; H[I p ] represents the entropy of a local area of ​​the image; I represents the intensity value of the image; I p Indicates the pixel intensity value of a local area in the image; w s (u,v) represents the spatial weight, u and v represent the horizontal and vertical coordinates of the center pixel respectively; s and t represent the horizontal and vertical coordinates of a pixel traversed in the neighborhood window; (u,v) represents the local spatial standard deviation based on the center point (u,v).

[0026] Furthermore, the adaptive denoising process is based on the calculated intensity weight w r and spatial weight w s , bilateral filtering is performed on the OCT reconstructed image after histogram stretching. The filtering operation slides pixel by pixel on the image until the entire image is processed, and an OCT reconstructed image that retains the image edge and detail information is obtained. The specific formula is:

[0027] w h (i,j)=w r (i,j)·w s (i,j)

[0028]

[0029] Where i represents the horizontal coordinate of the image after histogram stretching transformation, j represents the vertical coordinate of the transformed image; I b represents the final filtered output image; Indicates that a bilateral filtering operation is performed using a specific spatial standard deviation size and intensity standard deviation size; and Represent the minimum and maximum values ​​of the spatial standard deviation, respectively, and are used to optimize different features of the image; the weight w h is the final weight calculated based on the intensity weight and spatial weight.

[0030] Furthermore, the blind deconvolution algorithm is performed to estimate the OCT denoised image and optimize the point spread function PSF. The calculation formula is as follows:

[0031]

[0032] Among them, I out [k] represents the image estimation after the kth iteration; I obs represents the observed denoised OCT reconstructed image; H represents the estimated point spread function PSF; represents the inverse or pseudo-inverse of the point spread function PSF; λ represents the iterative step size, which is used to control the update speed.

[0033] On the other hand, an OCT image processing system for adaptive weighted bilateral filtering deconvolution is characterized by comprising:

[0034] A preprocessing module is used to preprocess the original tissue OCT signal to obtain an OCT reconstructed image;

[0035] A histogram stretching module is used to perform histogram stretching on the OCT reconstructed image to obtain an OCT stretched image;

[0036] An adaptive denoising module is used to adjust the spatial weight and intensity weight of the stretched OCT image; based on the adjusted spatial weight and intensity weight, it is used to perform adaptive denoising on the stretched OCT image to obtain an OCT denoised image;

[0037] The blind deconvolution module is used to take the OCT denoised image and the initialized point spread function (PSF) as input, perform the blind deconvolution algorithm, and obtain the final OCT output image.

[0038] The present invention adopts the above technical solution and has the following beneficial effects:

[0039] (1) The present invention uses adaptive weighted bilateral filtering technology to effectively remove speckle noise and other Gaussian white noise in OCT images, significantly improving the contrast and clarity of the image;

[0040] (2) The present invention uses histogram stretching and local statistical characteristics to adjust spatial weights and intensity weights, retaining important edge and detail information during the denoising process, thereby enhancing the visual effect and diagnostic value of the image;

[0041] (3) The present invention combines a blind deconvolution algorithm to optimize the point spread function (PSF), and estimates the original unblurred image through iterative updating, ultimately obtaining a high-quality, high-resolution OCT reconstructed image, which contributes to more accurate medical analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Flowchart of the OCT image processing method of adaptive weighted bilateral filtering deconvolution according to an embodiment of the present invention;

[0043] Figure 2 Schematic diagram of an implementation of an OCT image processing method using adaptive weighted bilateral filtering deconvolution according to an embodiment of the present invention;

[0044] Figure 3 The skin OCT noise image and processed image according to an embodiment of the present invention;

[0045] Figure 4 The tape OCT noise image and processed image are shown in an embodiment of the present invention;

[0046] Figure 5 This is a system diagram of the OCT image processing method of adaptive weighted bilateral filtering deconvolution according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The present invention will be described in further detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0048] On the one hand, if Figure 1 and Figure 2 As shown, the present invention provides an OCT image processing method of adaptive weighted bilateral filtering deconvolution, which includes the following steps.

[0049] S1: Preprocess the original tissue OCT signal to obtain the OCT reconstructed image.

[0050] Specifically, the preprocessing of the original tissue OCT signal includes:

[0051] The original tissue OCT signal is subjected to background noise removal, wavenumber calibration, spectrum shaping and dispersion compensation.

[0052] Specifically, OCT images are often affected by various noises, the most important of which is speckle noise, which is generated by multipath reflection and scattering effects. In addition, OCT images may also be affected by laser intensity noise, photoelectron shooting noise, and thermal noise generated by electronic devices. These noises are generally considered to be Gaussian white noise, which has a smaller impact than speckle noise and can be ignored. Therefore, in this embodiment, the OCT image I is defined as * as follows:

[0053] I * =N s I+N w

[0054] Where I represents the noise-free image, N s represents speckle noise, N w Represents Gaussian white noise. Given that Gaussian white noise has less influence than speckle noise, a simplified model is proposed to model the OCT image as:

[0055] I * =N s I

[0056] A method is proposed to convert multiplicative noise into additive noise by performing a logarithmic transformation on the OCT image as follows:

[0057] log(I * )=log(N s )+log(I)

[0058] After the transformation, the noise model is close to the additive model, making noise processing simpler.

[0059] S2, performing histogram stretching on the OCT reconstructed image to obtain an OCT stretched image.

[0060] Specifically, the histogram stretching includes:

[0061] Determine the lowest non-zero intensity value L′min and the highest non-zero intensity value L′min from the OCT reconstructed image, and apply a linear intensity transformation to map the original intensity value I(x,y) of each pixel in the OCT reconstructed image to a new pixel value p(i,j). The linear intensity transformation formula is as follows:

[0062]

[0063] Among them, x is the horizontal coordinate of the image, which indicates the position of the pixel in the horizontal direction; y is the vertical coordinate of the image, which indicates the position of the pixel in the vertical direction; i is the horizontal coordinate of the transformed image, and j is the vertical coordinate of the transformed image; L max and L minrepresent the maximum and minimum intensity values ​​in the original OCT reconstructed image;

[0064] The OCT reconstructed image after the linear intensity transformation has improved contrast; then, each pixel value of the image is counted, and the number of pixels at each intensity level is accumulated to form a histogram, where the predetermined intensity range (L′ max ,L′ min ) is set in advance based on the image display or processing requirements.

[0065] Specifically, before applying the linear intensity transform, the OCT reconstructed image is subjected to color space conversion, converting the OCT reconstructed image from the RGB color space to the grayscale space; before outputting the OCT reconstructed image after histogram stretching, the image processed by the linear intensity transform is converted from the grayscale space back to the RGB color space.

[0066] Specifically, local statistical characteristics refer to local contrast, which measures the difference in brightness within a region. For OCT images, local contrast can help identify boundaries or characteristic regions of different tissues.

[0067] S3, based on local statistical characteristics, adjust the spatial weight and intensity weight of the OCT reconstructed image after histogram stretching; based on the adjusted spatial weight and intensity weight, perform adaptive denoising on the OCT reconstructed image after histogram stretching to obtain a denoised OCT reconstructed image.

[0068] Specifically, the intensity weight is the actual weight calculated by the intensity kernel. The intensity kernel is used to measure the intensity difference between two pixels in the image. The intensity weight is the output value of the intensity kernel. The calculation formula of the intensity weight is as follows:

[0069] σ r (m,n)=βμ p

[0070]

[0071] Among them, β represents the coefficient for adjusting the standard deviation of intensity; μ p Represents the local mean; m and n represent the horizontal and vertical coordinates of the current center pixel, which are the core reference points for weight calculation; σ r (m,n) represents the local intensity standard deviation based on the center point (m,n); w r (m,n) represents the intensity weight; k and l represent the horizontal and vertical coordinates of a pixel traversed in the neighborhood window; I(m,n) represents the intensity value of the center pixel currently being processed; I(k,l) represents the intensity value of a point in the neighborhood.

[0072] Specifically, the spatial weight is the actual weight calculated by the spatial kernel, which is a function used to measure the similarity of two pixels in the image in terms of spatial position. The spatial weight represents the output value of the spatial kernel. First, the local entropy value H[I p ]Calculate σ s After the spatial standard deviation, the spatial weight is calculated, and the formula is as follows:

[0073]

[0074] Among them, p(i) represents the probability of pixel value i appearing in the neighborhood; B N Represents the total number of pixel values ​​in the neighborhood; and Respectively represent the maximum and minimum values ​​of the preset spatial standard deviation; ζ represents a preset threshold value used to adjust the range of variation of the spatial kernel size; H[I p ] represents the entropy of a local area of ​​the image; I represents the intensity value of the image; I p Indicates the pixel intensity value of a local area in the image; w s (u,v) represents the spatial weight, u and v represent the horizontal and vertical coordinates of the center pixel respectively; s and t represent the horizontal and vertical coordinates of a pixel traversed in the neighborhood window; (u,v) represents the local spatial standard deviation based on the center point (u,v).

[0075] Specifically, the adaptive denoising process is based on the calculated intensity weight w r and spatial weight w s , bilateral filtering is performed on the OCT reconstructed image after histogram stretching. The filtering operation slides pixel by pixel on the image until the entire image is processed, and an OCT reconstructed image that retains the image edge and detail information is obtained. The specific formula is:

[0076] w h (i,j)=w r (i,j)·w s (i,j)

[0077]

[0078] Where i represents the horizontal coordinate of the image after histogram stretching transformation, j represents the vertical coordinate of the transformed image; I b represents the final filtered output image; Indicates that a bilateral filtering operation is performed using a specific spatial standard deviation size and intensity standard deviation size; and Represent the minimum and maximum values ​​of the spatial standard deviation, respectively, and are used to optimize different features of the image; the weight w his the final weight calculated based on the intensity weight and spatial weight.

[0079] S4, takes the denoised OCT reconstructed image and the initialized point spread function PSF as input, performs a blind deconvolution algorithm, and obtains the final OCT reconstructed image.

[0080] Specifically, the blind deconvolution algorithm is executed to estimate the denoised OCT reconstructed image and optimize the point spread function PSF. The calculation formula is as follows:

[0081]

[0082] Among them, I out [k] represents the image estimation after the kth iteration; I obs represents the observed denoised OCT reconstructed image; H represents the estimated point spread function PSF; represents the inverse or pseudo-inverse of the point spread function PSF; λ represents the iterative step size, which is used to control the update speed.

[0083] Specifically, the image and PSF estimation are iteratively updated until the predetermined convergence condition is met or the set number of iterations is reached, and the deconvolved image and optimized PSF are output. The calculation formula is as follows:

[0084]

[0085] Finally, the output pixel value I out The image composed of the output pixel values ​​corresponding to the pixel point I(x, y) in the original image is the final OCT reconstructed image of the denoising algorithm of the present invention.

[0086] Figure 3 and Figure 4 The denoising results of the tape and skin using the algorithm are shown in Figure 2. It can be seen that the image denoising method of the present invention better reduces the noise signal in the image and protects the important details in the image.

[0087] like Figure 5 As shown, this embodiment also discloses an OCT image processing system for adaptive weighted bilateral filtering deconvolution, including:

[0088] A preprocessing module 51 preprocesses the original tissue OCT signal to obtain an OCT reconstructed image;

[0089] A histogram stretching module 52 performs histogram stretching on the OCT reconstructed image to obtain an OCT stretched image;

[0090] An adaptive denoising module 53 is configured to adjust the spatial weight and intensity weight of the stretched OCT image; based on the adjusted spatial weight and intensity weight, the stretched OCT image is adaptively denoised to obtain an OCT denoised image;

[0091] The blind deconvolution module 54 is configured to take the OCT denoised image and the initialized point spread function (PSF) as input, execute a blind deconvolution algorithm, and obtain a final OCT output image.

[0092] The specific implementation of the OCT image processing system using adaptive weighted bilateral filtering deconvolution is the same as that of the OCT image processing method using adaptive weighted bilateral filtering deconvolution, and will not be described repeatedly in this embodiment.

[0093] Although the present invention has been particularly shown and described in conjunction with preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made to the present invention without departing from the spirit and scope of the invention as defined in the appended claims, and all such changes are within the scope of protection of the present invention.

Claims

1. An OCT image processing method of adaptive weighted bilateral filtering deconvolution, characterized in that: The following steps are involved: S1, preprocessing the original tissue OCT signal to obtain the OCT reconstructed image; S2, performing histogram stretching on the OCT reconstructed image to obtain an OCT stretched image; S3, adjusting the spatial weight and intensity weight of the OCT stretch image; Based on the adjusted spatial weight and intensity weight, the OCT stretched image is subjected to adaptive denoising to obtain an OCT denoised image; S4, taking the OCT denoised image and the initialized point spread function PSF as input, performing a blind deconvolution algorithm to obtain the final OCT output image.

2. The OCT image processing method of adaptive weighted bilateral filtering deconvolution according to claim 1, characterized in that: In S1, the preprocessing specifically includes: The original tissue OCT signal was subjected to background noise removal, wavenumber calibration, spectrum shaping and dispersion compensation.

3. The OCT image processing method of adaptive weighted bilateral filtering deconvolution according to claim 1, characterized in that: In S2, the histogram stretching specifically includes: Determine the lowest non-zero intensity value L′ from the OCT reconstructed image min and the highest non-zero intensity value L′ max , a linear intensity transformation is applied to map the original intensity value I(x,y) of each pixel in the OCT reconstructed image to a new pixel value p(i,j); the linear intensity transformation formula is as follows: Where x is the horizontal coordinate of the image, indicating the position of the pixel in the horizontal direction; y is the vertical coordinate of the image, indicating the position of the pixel in the vertical direction; i is the horizontal coordinate of the transformed image, and j is the vertical coordinate of the transformed image; L max and L min They represent the maximum and minimum intensity values ​​in the original OCT reconstructed image, respectively; The pixel value of each pixel in the image processed by the linear intensity transform is counted, and the number of pixels at each intensity level is accumulated to complete the histogram stretching.

4. The OCT image processing method of adaptive weighted bilateral filtering deconvolution according to claim 1, characterized in that In S3, the intensity weight is the actual weight calculated by the intensity kernel. The intensity kernel is used to measure the intensity difference between two pixels in the image. The intensity weight is the output value of the intensity kernel. The calculation formula of the intensity weight is as follows: s r (m,n)=bμ p Among them, β represents the coefficient for adjusting the standard deviation of intensity; μ p represents the local mean; m and n represent the horizontal and vertical coordinates of the current center pixel, which are the core reference points for weight calculation; σ r (m,n) represents the local intensity standard deviation based on the center point (m,n); w r (m,n) represents the intensity weight; k and l represent the horizontal and vertical coordinates of a pixel traversed in the neighborhood window; I(m,n) represents the intensity value of the center pixel currently being processed; I(k,l) represents the intensity value of a point in the neighborhood.

5. The OCT image processing method of adaptive weighted bilateral filtering deconvolution according to claim 4, characterized in that: In S3, the spatial weight is the actual weight calculated by the spatial kernel, which is a function used to measure the similarity of two pixels in the image in spatial position. The spatial weight represents the output value of the spatial kernel. First, the local entropy value H[I p ] Calculate σ s After the spatial standard deviation, the spatial weight is calculated, and the formula is as follows: Among them, p(i) represents the probability of pixel value i appearing in the neighborhood; B N Represents the total number of pixel values ​​in the neighborhood; and Respectively represent the maximum and minimum values ​​of the preset spatial standard deviation; ζ represents a preset threshold value used to adjust the range of the spatial kernel size; H[I p ] represents the entropy of a local area of ​​the image; I represents the intensity value of the image; I p Represents the pixel intensity value of a local area in the image; w s (u,v) represents the spatial weight, u and v represent the horizontal and vertical coordinates of the center pixel respectively; s and t represent the horizontal and vertical coordinates of a pixel traversed in the neighborhood window; (u,v) represents the local spatial standard deviation based on the center point (u,v).

6. The OCT image processing method of adaptive weighted bilateral filtering deconvolution according to claim 5, characterized in that: In S3, the adaptive denoising process is based on the calculated intensity weight w r and the spatial weight w s , bilateral filtering is performed on the OCT reconstructed image after histogram stretching. The filtering operation slides pixel by pixel on the image until the entire image is processed, and an OCT reconstructed image that retains the image edge and detail information is obtained. The specific formula is: w h (i,j)=w r (i,j)·w s (i,j) Where i represents the horizontal coordinate of the image after the histogram stretching transformation, and j represents the vertical coordinate of the transformed image; b represents the final filtered output image; Indicates that a bilateral filtering operation is performed using a specific spatial standard deviation and intensity standard deviation. and Respectively represent the minimum and maximum values ​​of the spatial standard deviation, which are used to optimize different features of the image; weight w h is the final weight calculated based on the intensity weight and spatial weight.

7. The OCT image processing method of adaptive weighted bilateral filtering deconvolution according to claim 1, characterized in that In S4, the blind deconvolution algorithm is executed to estimate the OCT denoised image and optimize the point spread function PSF, and the calculation formula is as follows: Among them, I out [k] represents the image estimation after the kth iteration; I obs represents the observed denoised OCT reconstructed image; H represents the estimated point spread function PSF; represents the inverse or pseudo-inverse of the point spread function PSF; λ represents the iteration step size, which is used to control the update speed.

8. An OCT image processing system with adaptive weighted bilateral filtering deconvolution, characterized in that: include: A preprocessing module, used for preprocessing the original tissue OCT signal to obtain an OCT reconstructed image; A histogram stretching module, used for performing histogram stretching on the OCT reconstructed image to obtain an OCT stretched image; Adaptive denoising module, used to adjust the spatial weight and intensity weight of OCT stretched images; Based on the adjusted spatial weight and intensity weight, the OCT stretched image is adaptively denoised to obtain an OCT denoised image; The blind deconvolution module is used to take the OCT denoised image and the initialized point spread function PSF as input, execute the blind deconvolution algorithm, and obtain the final OCT output image.

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