A method, device and system for correcting laser speckle contrast images
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XIAN INST OF OPTICS & PRECISION MECHANICS CHINESE ACAD OF SCI
- Filing Date
- 2026-04-15
- Publication Date
- 2026-07-10
Smart Images
Figure CN122089616B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of image processing technology, and in particular to a method, apparatus and system for correcting laser speckle contrast images. Background Technology
[0002] Laser speckle imaging is an important non-contact, non-destructive imaging technique widely used in medical imaging, biological experiments, and industrial inspection. Its principle utilizes the coherence properties of laser light to illuminate the surface of biological tissue. Under the influence of moving scatterers (such as blood cells), a time-varying speckle image is generated. The speckle contrast of local areas in the image is calculated to reflect blood flow velocity and microstructure. Traditional laser speckle contrast imaging typically assumes that the illumination intensity is approximately uniform within the field of view, so that changes in speckle contrast can be directly correlated with changes in blood flow. However, in practical applications, the illumination intensity distribution is often not uniform. For example, the laser beam may have a Gaussian intensity distribution, making the central area bright and the edge areas dark; furthermore, the field of view of the optical system may be affected by vignetting or tissue curvature, leading to uneven brightness in the speckle image. If there is uneven light intensity, there will be systematic errors in speckle contrast calculation and subsequent blood flow velocity estimation. That is, in areas with weak illumination, due to the reduced signal-to-noise ratio, speckle contrast may appear too high or too low, causing blood flow in these areas to be misjudged as abnormal changes. This can lead to false differences in laser speckle contrast imaging images that match the light intensity distribution field under actual uniform blood flow conditions, reducing the accuracy of diagnosis and quantitative analysis.
[0003] In related technologies, in order to reduce the impact of uneven illumination on laser speckle imaging, the speckle image is usually subjected to overall equalization processing during data post-processing. However, simple post-equalization may overstretch the dark area signal, amplify noise, introduce new errors, or weaken the visibility of actual blood flow differences.
[0004] Therefore, how to reduce the impact of uneven illumination on laser speckle images, so that the blood flow images have uniform brightness and accurate contrast, thereby improving the accuracy of blood flow velocity estimation, is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0005] The purpose of this invention is to provide a method, apparatus, and system for correcting laser speckle contrast images, thereby reducing the impact of uneven illumination on laser speckle images, resulting in blood flow images with uniform brightness and accurate contrast, and thus improving the accuracy of blood flow velocity estimation. The specific technical solution is as follows:
[0006] A first aspect of this application provides a method for correcting laser speckle contrast images, the method comprising:
[0007] Acquire multiple speckle images;
[0008] Based on each speckle image, a light intensity distribution map is determined, wherein each pixel in the light intensity distribution map is used to characterize the spatial non-uniformity of the illumination of the pixel.
[0009] Determine the original speckle contrast image of the target speckle image, wherein the target speckle image is one speckle image or the average image of multiple speckle images;
[0010] Based on the light intensity distribution map, a local light intensity variation coefficient map is determined, wherein each pixel in the local light intensity variation coefficient map is used to represent the variation coefficient of pixel intensity in the neighborhood of the pixel in the light intensity distribution map;
[0011] The values of the corresponding pixels in the original speckle contrast image are corrected based on the values of each pixel in the local light intensity variation coefficient image to obtain and output the corrected speckle contrast image.
[0012] In one possible implementation, correcting the pixel values at corresponding positions in the original speckle contrast image based on the values of each pixel in the local intensity variation coefficient map includes:
[0013] The original speckle contrast image is normalized.
[0014] The values of the corresponding pixels in the normalized original speckle contrast image are corrected based on the values of each pixel in the local light intensity variation coefficient map.
[0015] In one possible implementation, determining the intensity distribution map based on each of the speckle images includes:
[0016] Calculate the average value of each speckle image to obtain the average light intensity distribution map;
[0017] High-frequency details are removed from the average light intensity distribution map to obtain the light intensity distribution map.
[0018] In one possible implementation, correcting the pixel values at corresponding positions in the original speckle contrast image based on the values of each pixel in the local intensity variation coefficient map includes:
[0019] The original speckle contrast image is corrected using the following formula:
[0020] ;
[0021] in, This is the corrected speckle contrast image. This is the original speckle contrast image. This is a graph showing the coefficient of variation of local light intensity.
[0022] In one possible implementation, the original speckle contrast image is obtained by the following formula:
[0023] ;
[0024] in, For the pixels in the target speckle image The standard deviation of pixel intensity within the preset window, For the pixels in the target speckle image The average pixel intensity within the preset window.
[0025] In one possible implementation, the local light intensity variation coefficient map is obtained by the following formula:
[0026] ;
[0027] in, For the pixels in the light intensity distribution map The standard deviation of pixel intensity within the preset window, For the pixels in the light intensity distribution map The average pixel intensity within the preset window.
[0028] In one possible implementation, the method further includes:
[0029] Calculate the first correlation coefficient between the original speckle contrast image and the light intensity distribution map, and calculate the second correlation coefficient between the corrected speckle contrast image and the light intensity distribution map;
[0030] Output the first correlation coefficient and the second correlation coefficient.
[0031] A second aspect of this application provides a correction apparatus for laser speckle contrast images, the apparatus comprising:
[0032] The image acquisition module is used to acquire multiple speckle images;
[0033] The first determining module is used to determine a light intensity distribution map based on each of the speckle images, wherein each pixel in the light intensity distribution map is used to characterize the spatial non-uniformity of the illumination of the pixel.
[0034] The second determining module is used to determine the original speckle contrast image of the target speckle image, wherein the target speckle image is one speckle image or the average image of multiple speckle images;
[0035] The third determining module is used to determine a local light intensity variation coefficient map based on the light intensity distribution map, wherein each pixel in the local light intensity variation coefficient map is used to represent the variation coefficient of pixel intensity in the neighborhood of the pixel in the light intensity distribution map;
[0036] The image correction module is used to correct the values of the corresponding pixels in the original speckle contrast image according to the values of each pixel in the local light intensity variation coefficient map, so as to obtain and output the corrected speckle contrast image.
[0037] In one possible implementation, the image correction module is specifically used for:
[0038] The original speckle contrast image is normalized.
[0039] The values of the corresponding pixels in the normalized original speckle contrast image are corrected based on the values of each pixel in the local light intensity variation coefficient map.
[0040] In one possible implementation, the first determining module is specifically used for:
[0041] Calculate the average value of each speckle image to obtain the average light intensity distribution map;
[0042] High-frequency details are removed from the average light intensity distribution map to obtain the light intensity distribution map.
[0043] In one possible implementation, the image correction module is specifically used for:
[0044] The original speckle contrast image is corrected using the following formula:
[0045] ;
[0046] in, This is the corrected speckle contrast image. This is the original speckle contrast image. This is a graph showing the coefficient of variation of local light intensity.
[0047] In one possible implementation, the original speckle contrast image is obtained by the following formula:
[0048] ;
[0049] in, For the pixels in the target speckle image The standard deviation of pixel intensity within the preset window, For the pixels in the target speckle image The average pixel intensity within the preset window.
[0050] In one possible implementation, the local light intensity variation coefficient map is obtained by the following formula:
[0051] ;
[0052] in, For the pixels in the light intensity distribution map The standard deviation of pixel intensity within the preset window, For the pixels in the light intensity distribution map The average pixel intensity within the preset window.
[0053] In one possible implementation, the device further includes:
[0054] The coefficient calculation module is used to calculate the first correlation coefficient between the original speckle contrast image and the light intensity distribution map, and to calculate the second correlation coefficient between the corrected speckle contrast image and the light intensity distribution map.
[0055] The coefficient output module is used to output the first correlation coefficient and the second correlation coefficient.
[0056] A third aspect of the embodiments of this application provides a correction system for laser speckle contrast images, the system including an image acquisition device and a processor;
[0057] The image acquisition device is used to capture speckle images;
[0058] The processor is used to implement the laser speckle contrast image correction method described in the first aspect.
[0059] In one possible implementation, the image acquisition device includes a visible light source, a near-infrared light source, a dichroic beam combiner, a beam expander, a homogenizing mirror, a dichroic mirror, a fluorescence channel, a fluorescence camera, a speckle channel, and a speckle camera.
[0060] The visible light beam generated by the visible light source and the near-infrared beam generated by the near-infrared light source are coupled into a target beam by the dichroic beam combiner, and then expanded and homogenized by the beam expander and the beam homogenizer in sequence. The expanded and homogenized target beam illuminates the target biological tissue. The beam reflected by the target biological tissue is transmitted through the dichroic mirror and then enters the speckle camera through the speckle channel for imaging. The beam reflected by the target biological tissue is reflected by the dichroic mirror and then enters the fluorescence camera through the fluorescence channel for imaging.
[0061] Beneficial effects of the embodiments of the present invention:
[0062] This invention provides a method, apparatus, and system for correcting laser speckle contrast images. After acquiring a speckle image, an intensity distribution map is determined based on the speckle image, and an original speckle contrast map of the target speckle image is also determined. Then, a local intensity variation coefficient map is determined based on the intensity distribution map. Finally, the values of corresponding pixels in the original speckle contrast map are corrected based on the values of each pixel in the local intensity variation coefficient map. Since the intensity distribution map can reflect the spatial non-uniformity of illumination, the local intensity variation coefficient map determined based on the intensity distribution map quantifies the degree of fluctuation of illumination non-uniformity in a local range. By using the local intensity variation coefficient map to perform pixel-by-pixel correction on the original speckle contrast map, the variation introduced by illumination non-uniformity can be reduced without changing the speckle contrast change caused by blood flow motion. That is, the influence of illumination non-uniformity on the laser speckle image is reduced, making the blood flow image brighter and more accurate in contrast, thereby improving the accuracy of blood flow velocity estimation.
[0063] Of course, implementing any product or method of the present invention does not necessarily require achieving all of the advantages described above at the same time. Attached Figure Description
[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings.
[0065] Figure 1 A schematic diagram of a laser speckle contrast image correction method provided in an embodiment of this application;
[0066] Figure 2 Another schematic diagram of the laser speckle contrast image correction method provided in the embodiments of this application;
[0067] Figure 3 Another schematic diagram of the laser speckle contrast image correction method provided in the embodiments of this application;
[0068] Figure 4 Another schematic diagram of the laser speckle contrast image correction method provided in the embodiments of this application;
[0069] Figure 5 A flowchart illustrating a method for correcting laser speckle contrast images provided in an embodiment of this application;
[0070] Figure 6 This is a schematic diagram of the acquisition and processing timing provided in the embodiments of this application;
[0071] Figure 7Example image of the target speckle contrast image provided in the embodiments of this application;
[0072] Figure 8 for Figure 7 The light intensity distribution map corresponding to the image shown;
[0073] Figure 9 To Figure 7 Comparison of speckle patterns after correction;
[0074] Figure 10 To Figure 7 A comparison chart of contrast values before and after correction;
[0075] Figure 11 A schematic diagram of the structure of the laser speckle contrast image correction device provided in the embodiments of this application;
[0076] Figure 12 A schematic diagram of the structure of the laser speckle contrast image correction system provided in the embodiments of this application;
[0077] Figure 13 This is a schematic diagram of the structure of the image acquisition device provided in the embodiments of this application. Detailed Implementation
[0078] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art based on this application are within the scope of protection of the present invention.
[0079] A first aspect of this application provides a method for correcting laser speckle contrast images, see [link to relevant documentation]. Figure 1 , Figure 1 A schematic diagram of a laser speckle contrast image correction method provided in an embodiment of this application, the method including the following steps:
[0080] Step S10: Acquire multiple speckle images;
[0081] Step S20: Determine the light intensity distribution map based on each speckle image;
[0082] In the light intensity distribution map, each pixel is used to characterize the spatial non-uniformity of the pixel illumination.
[0083] Step S30: Determine the original speckle contrast image of the target speckle image;
[0084] Step S40: Determine the local light intensity variation coefficient map based on the light intensity distribution map;
[0085] Step S50: Correct the values of the corresponding pixels in the original speckle contrast image according to the values of each pixel in the local light intensity variation coefficient map, and output the corrected speckle contrast image.
[0086] In this embodiment of the application, after acquiring the speckle image, a light intensity distribution map is determined based on the speckle image, and an original speckle contrast map of the target speckle image is determined. Then, a local light intensity variation coefficient map is determined based on the light intensity distribution map. Finally, the values of the corresponding pixels in the original speckle contrast map are corrected based on the values of each pixel in the local light intensity variation coefficient map. Since the light intensity distribution map can reflect the spatial non-uniformity of illumination, the local light intensity variation coefficient map determined based on the light intensity distribution map quantifies the degree of fluctuation of illumination non-uniformity in the local range. By using the local light intensity variation coefficient map to perform pixel-by-pixel correction on the original speckle contrast map, the variation introduced by illumination non-uniformity can be reduced without changing the speckle contrast change caused by blood flow motion. That is, the influence of illumination non-uniformity on the laser speckle image is reduced, making the blood flow image brighter and more accurate in contrast, thereby improving the accuracy of blood flow velocity estimation.
[0087] The following is a detailed explanation of steps S10 to S50:
[0088] In step S10 above, multiple speckle images are multiple frames of speckle images acquired for the same scene, that is, the illumination distribution of each speckle image in the multiple speckle images is the same or approximately the same.
[0089] Multiple speckle images can be multiple consecutive frame speckle images, or they can be a subset of images extracted from multiple consecutive frame speckle images. For example, extracting one frame every other frame from 10 consecutive frame speckle images will result in multiple speckle images.
[0090] In step S20, determining the light intensity distribution map based on each speckle image means extracting the light intensity of each pixel in the speckle image. The value of each pixel in the light intensity distribution map is the average value of the light intensity of the same pixel in each speckle image.
[0091] It is understood that, in one possible implementation, step S20 may be performed after all speckle images have been acquired in step S10 above.
[0092] In another possible implementation, the obtained speckle image can be updated using a sliding window method during the execution of step S10, and step S20 can be executed after the update. This can obtain a real-time light intensity distribution map, thereby achieving real-time correction of the effects of uneven illumination.
[0093] In step S30, the target speckle image can be any one of the multiple speckle images obtained in step S10, or it can be the average image of the multiple speckle images obtained in step S10. This application embodiment does not limit this.
[0094] In the original speckle contrast image, each pixel represents the degree of speckle fluctuation in the neighborhood of that pixel in the target speckle image, and is inversely proportional to the blood flow velocity in that neighborhood. The contrast value of each pixel in the original speckle contrast image can be calculated using a sliding window method, a pixel time series method, or other methods; these are all possible, and this application does not limit the scope of the calculation.
[0095] The contrast value of each pixel in the original speckle contrast image is the ratio of the average intensity to the standard deviation of the pixel intensity in the neighborhood of that pixel in the target speckle image. The specific calculation process is described below and will not be repeated here. The contrast value of each pixel in the original speckle contrast image can be either a spatial contrast value or a temporal contrast value; this application does not limit this.
[0096] In step S40 above, each pixel in the local light intensity variation coefficient map represents the variation coefficient of the pixel intensity in its neighborhood in the light intensity distribution map, that is, the ratio of the average value to the standard deviation of the pixel intensity in its neighborhood in the light intensity distribution map. This is used to quantify the additional variance contribution introduced by uneven illumination. The specific calculation process is described below and will not be repeated here.
[0097] In one possible implementation, the coefficient of variation for each pixel can be calculated using a sliding window method, or by using a pixel time series method, or by other methods. These are all possible, and this application does not limit the implementation of these methods.
[0098] In steps S30 and S40 above, the neighborhood size of each pixel is the same and is predetermined based on experience and requirements. For example, the neighborhood size can be 3×3 or 5×5, or other sizes. It is understood that the neighborhood size cannot be too large or too small. When setting the neighborhood size based on experience and requirements, it should satisfy the following condition: the neighborhood size covers a sufficient number of speckle particles, but cannot smooth out the actual spatial variation of the flow velocity. Specifically, the pixel diameter of the speckle particles in the image can be estimated based on the autocorrelation function of the speckle image at half maximum width or optical parameters, and then the neighborhood size can be determined accordingly.
[0099] In step S50 above, the values of the corresponding pixels in the original speckle contrast image are corrected according to the values of each pixel in the local light intensity variation coefficient map. This correction can be performed by normalization; or by determining the illumination intensity deviation of each pixel based on the local light intensity variation coefficient map, and then correcting the values of the corresponding pixels in the original speckle contrast image according to the illumination intensity deviation of each pixel; or by other methods.
[0100] When outputting the corrected speckle contrast image, you can output the corrected speckle contrast image directly, or you can convert the corrected speckle contrast image into an image representing blood flow velocity before outputting it. Specifically, you can use pseudo-color encoding to render different contrasts (or corresponding flow velocities) into color images for output.
[0101] In another possible implementation, the corrected speckle contrast image can be converted into a relative perfusion index for output and displayed or stored in pseudocolor.
[0102] In one possible implementation, the light intensity distribution map can be determined using a time-smoothing method. See also Figure 2 , Figure 2 Another schematic diagram of a laser speckle contrast image correction method provided in this application embodiment, the method includes the following steps:
[0103] Step S10: Acquire multiple speckle images;
[0104] Step S201: Calculate the average value of each speckle image to obtain the average light intensity distribution map;
[0105] Step S202: Remove high-frequency details from the average light intensity distribution map to obtain the light intensity distribution map.
[0106] In the light intensity distribution map, each pixel is used to characterize the spatial non-uniformity of the pixel illumination.
[0107] Step S30: Determine the original speckle contrast image of the target speckle image;
[0108] Step S40: Determine the local light intensity variation coefficient map based on the light intensity distribution map;
[0109] Step S50: Correct the values of the corresponding pixels in the original speckle contrast image according to the values of each pixel in the local light intensity variation coefficient map, and output the corrected speckle contrast image.
[0110] Steps S10 and S30 to S50 are described above and will not be repeated here. Steps S201 to S202 are one possible implementation of step S20. Steps S201 and S202 will be described below.
[0111] In step S201, the average value of each speckle image is calculated to obtain the average light intensity distribution map. This means that for the same pixel position in the image, the average gray value or light intensity value at that position in each speckle image is calculated as the value at that position in the average light intensity distribution map.
[0112] For example, the average light intensity distribution map can be calculated using the following formula:
[0113] ;
[0114] in, The pixel coordinates in the average light intensity distribution map are: The pixel value of each pixel; N is the number of speckle images, N≥1; The pixel coordinates in the nth speckle image are The pixel value of the pixel.
[0115] In step S202, the high-frequency details in the average light intensity distribution map may be removed by performing spatial low-pass filtering, fitting, and morphological processing. Other methods may also be used to remove the high-frequency details in the average light intensity distribution map. This application embodiment does not limit this.
[0116] By using the embodiments of this application, high-frequency details in the average light intensity distribution map can be removed, and structures such as blood vessels and textures can be removed, leaving only the effective light intensity background and reducing the impact of high-frequency details on the correction process.
[0117] In another possible implementation, a low-pass smoothing method can be used to determine the light intensity distribution map. Specifically, after acquiring each speckle image, a low-pass smoothing method can be used to determine the average light intensity distribution map for one of the speckle images. Then, high-frequency details in the average light intensity distribution map are removed to obtain the light intensity distribution map.
[0118] The process of determining the light intensity distribution map has been explained above. The specific calculation process of the original speckle contrast image and the local light intensity variation coefficient map is explained below:
[0119] First, the calculation process of the original speckle contrast image will be explained. Based on the above, the contrast value of each pixel in the original speckle contrast image can be either a spatial contrast value or a temporal contrast value.
[0120] Spatial contrast is defined as: ,in and respectively with The mean and standard deviation of pixel intensity within the preset window centered on the pixel; temporal contrast is defined as: ,in and Pixels Time window (or The mean and standard deviation within a frame. The time window T is also set based on experience and requirements, and this application does not impose any limitations on it.
[0121] Taking the contrast value of each pixel in the original speckle contrast image as the spatial contrast value as an example, in one possible implementation, the original speckle contrast image is obtained by the following formula:
[0122] ;
[0123] in, The pixel coordinates in the original speckle contrast image are The pixel value of the pixel, For the pixels in the target speckle image The standard deviation of pixel intensity within the preset window For the pixels in the target speckle image The average pixel intensity within a preset window. This is understandable. It can also be directly regarded as the original speckle contrast image. The preset window is the neighborhood mentioned above, and its specific size is set according to experience and needs. This application does not limit it.
[0124] In one possible implementation, the local light intensity variation coefficient map is obtained by the following formula:
[0125] ;
[0126] in, Pixels in the light intensity distribution map The standard deviation of pixel intensity within the preset window Pixels in the light intensity distribution map The average pixel intensity within a preset window.
[0127] The following explains the various correction methods mentioned above:
[0128] Method 1: Correction using normalization.
[0129] Specifically, the original speckle contrast image can be multiplied pixel by pixel by a normalization formula or a preset weighting function. In one possible implementation, the following normalization formula can be used for correction:
[0130] ;
[0131] in, This represents the average light intensity in the light intensity distribution map. The pixel coordinates in the light intensity distribution map are... The pixel value of the pixel, This indicates the pixel coordinates in the original speckle contrast image. The pixel value of the pixel, This indicates the pixel coordinates in the corrected speckle contrast image. The pixel value of the pixel.
[0132] In one possible implementation, the following weighting function can also be used for correction:
[0133] ;
[0134] Where w(x,y) is the weight at pixel (x,y). The pixel coordinates in the light intensity distribution map are... The pixel value of the pixel; for The gradient vector represents the rate of change of illumination in space; Represents the magnitude of the gradient; p is the scaling factor used to scale the gradient magnitude to a suitable numerical range.
[0135] Method 2: Correct based on lighting intensity deviation:
[0136] In this case, the original speckle contrast image can be corrected using the following formula:
[0137] ;
[0138] in, This indicates the pixel coordinates in the corrected speckle contrast image. The pixel value of the pixel, The pixel coordinates in the original speckle contrast image are The pixel value of the pixel, The pixel coordinates in the local light intensity variation coefficient image are The pixel value of each pixel. This is understandable. It can also be directly regarded as a corrected speckle contrast image. It can also be directly regarded as the original speckle contrast image. It can also be directly regarded as a local light intensity variation coefficient diagram.
[0139] In this formula, max(·,0) is used to avoid imaginary results caused by negative values in low-light or strong gradient regions.
[0140] Using this method to correct the original speckle image can suppress background errors caused by non-blood flow factors. The corrected speckle contrast image can more accurately reflect the dynamic scattering information of hemodynamics, thus improving the contrast and accuracy of the corrected image.
[0141] In another possible implementation, Method 1 and Method 2 can be combined to correct the original speckle contrast image. For details, see [link to relevant documentation]. Figure 3 , Figure 3 Another schematic diagram of a laser speckle contrast image correction method provided in this application embodiment, the method includes the following steps:
[0142] Step S10: Acquire multiple speckle images;
[0143] Step S20: Determine the light intensity distribution map based on each speckle image;
[0144] Step S30: Determine the original speckle contrast image of the target speckle image;
[0145] Step S40: Determine the local light intensity variation coefficient map based on the light intensity distribution map;
[0146] Step S501: Normalize the original speckle contrast image;
[0147] Step S502: Correct the values of the corresponding pixels in the normalized original speckle contrast image according to the values of each pixel in the local light intensity variation coefficient map, and output the corrected speckle contrast image.
[0148] Steps S10 to S40 are described above and will not be repeated here. Steps S501 to S502 are one possible implementation of step S50.
[0149] The original speckle image can be normalized using the normalization method described in Method 1, or other normalization methods. This application does not limit the specific form of normalization in its embodiments.
[0150] Suppose that the image obtained after normalizing the original speckle image is used Therefore, the correction formula used in method two above can be transformed into:
[0151] .
[0152] By using the embodiments of this application, normalizing the original speckle contrast image and then correcting the normalized original speckle image based on the illumination intensity deviation, the illumination intensity gradient can be effectively reduced, making the corrected image more visually uniform and the blood flow contrast distribution more realistically reflect the hemodynamic changes of biological tissues.
[0153] It is understandable that the contrast value of each pixel in the original speckle image is often correlated with the illumination intensity it receives. After correction, this correlation is significantly weakened or even eliminated; that is, the correlation coefficient between illumination intensity and speckle contrast can be reduced from a significant value before correction to near zero. Based on this, in one possible implementation, such as... Figure 4 The diagram shown is another schematic representation of a laser speckle contrast image correction method provided in this application embodiment. The method includes the following steps:
[0154] Step S10: Acquire multiple speckle images;
[0155] Step S20: Determine the light intensity distribution map based on each speckle image. Each pixel in the light intensity distribution map is used to characterize the spatial non-uniformity of the pixel illumination.
[0156] Step S30: Determine the original speckle contrast image of the target speckle image;
[0157] Step S40: Determine the local light intensity variation coefficient map based on the light intensity distribution map;
[0158] Step S50: Correct the values of the corresponding pixels in the original speckle contrast image according to the values of each pixel in the local light intensity variation coefficient map, and output the corrected speckle contrast image.
[0159] Step S60: Calculate the first correlation coefficient between the original speckle contrast image and the light intensity distribution map, and calculate the second correlation coefficient between the corrected speckle contrast image and the light intensity distribution map.
[0160] Step S70: Output the first correlation coefficient and the second correlation coefficient.
[0161] In step S60, the first correlation coefficient and the second correlation coefficient are calculated in the same way, such as using the Pearson correlation coefficient, or using other correlation coefficient calculation methods.
[0162] For example, the first correlation coefficient between the original speckle contrast image and the intensity distribution map can be calculated using the following formula:
[0163] ;
[0164] in, The first correlation coefficient, This is the original speckle contrast image. This is a light intensity distribution map. Describing covariance, and They represent and The standard deviation.
[0165] The second correlation coefficient between the corrected speckle contrast image and the intensity distribution map is calculated using the following formula:
[0166] ;
[0167] in, The first correlation coefficient, This is the corrected speckle contrast image. and They represent and The standard deviation.
[0168] Understandable The closer a value is to 0, the weaker the correlation between the corrected speckle contrast image and the intensity distribution map, indicating a smaller impact of illumination non-uniformity on contrast; conversely, if... The larger the value, the more significantly the contrast is affected by changes in the illumination field, and the image still needs to be corrected.
[0169] In addition, if the difference between the first correlation coefficient and the second correlation coefficient is large, it indicates that the correction is effective; conversely, if the difference between the first correlation coefficient and the second correlation coefficient is small, the correction is considered invalid.
[0170] In step S70, the first correlation coefficient and the second correlation coefficient are output. This can be done either in step S50 along with the corrected speckle contrast image, or after the corrected speckle contrast image is output. Both are acceptable.
[0171] By selecting the embodiments of this application, the first correlation coefficient between the original speckle contrast image and the light intensity distribution map, and the second correlation coefficient between the corrected speckle contrast image and the light intensity distribution map are calculated, and the first and second correlation coefficients are output, so that users can judge whether the illumination non-uniformity has been completely eliminated and whether the correction is effective.
[0172] The correction method of this application will be described below with reference to specific embodiments. See also Figure 5 , Figure 5 A flowchart of a laser speckle contrast image correction method provided in an embodiment of this application includes the following steps:
[0173] Step S1, speckle sequence acquisition;
[0174] Step S2, light intensity distribution field estimation;
[0175] Step S3: Measure and calculate the contrast map;
[0176] Step S4: Calculation of local light intensity variation coefficient;
[0177] Step S5, contrast calibration;
[0178] Step S6: Output and display of infusion parameters;
[0179] Step S1 corresponds to the aforementioned step S10, and the speckle sequence in step S1 is the aforementioned multiple speckle images; Step S2 corresponds to the aforementioned step S20, and the light intensity distribution field is the aforementioned light intensity distribution map; Step S3 corresponds to the aforementioned step S30, and the measured contrast map is the aforementioned original speckle contrast image; Step S4 corresponds to the aforementioned step S40, and Steps S5 and S6 correspond to the aforementioned step S50.
[0180] Combination Figure 6 , Figure 6 This is a schematic diagram of the acquisition and processing timing provided in an embodiment of this application. The time axis on the left side of the image represents the acquisition stage. Image frames 1 to 8 represent N consecutive image frames acquired within a continuous acquisition window. The exposure time of each image frame is t, and the frame period is T. After acquiring multiple consecutive image frames, the processing stage begins. The processing stage mainly includes the following steps: calculation... ,calculate Calibration, Output .
[0181] Assuming the target speckle contrast image is as follows Figure 7 As shown, its light intensity distribution diagram is as follows: Figure 8 As shown, when the correction method of this application is used to... Figure 7 After correction, the corrected speckle contrast image is obtained as follows: Figure 9 By calculating a comparison chart of contrast values before and after correction, as shown in the image... Figure 10 As shown in the figure, the contrast value before correction (i.e., the measured value) and the contrast value after correction are respectively. It can be seen that the method of this application can significantly reduce the influence of uneven illumination on laser speckle images, making the blood flow image uniform in brightness and accurate in contrast.
[0182] Corresponding to the first aspect mentioned above, a second aspect of the embodiments of this application provides a correction device for laser speckle contrast images, such as... Figure 11 As shown, Figure 11 This is a schematic diagram of the structure of the laser speckle contrast image correction device provided in the embodiments of this application. The device includes:
[0183] Image acquisition module 1101 is used to acquire multiple speckle images;
[0184] The first determining module 1102 is used to determine a light intensity distribution map based on each of the speckle images, wherein each pixel in the light intensity distribution map is used to characterize the spatial non-uniformity of the illumination of the pixel.
[0185] The second determining module 1103 is used to determine the original speckle contrast image of the target speckle image, wherein each pixel in the original speckle contrast image is used to represent the variation coefficient of the pixel intensity in the neighborhood of the pixel in the target speckle image, and the target speckle image is a speckle image or the average image of multiple speckle images.
[0186] The third determining module 1104 is used to determine a local light intensity variation coefficient map based on the light intensity distribution map, wherein each pixel in the local light intensity variation coefficient map is used to represent the variation coefficient of pixel intensity in the neighborhood of the pixel in the light intensity distribution map.
[0187] The image correction module 1105 is used to correct the values of the corresponding pixels in the original speckle contrast image according to the values of each pixel in the local light intensity variation coefficient map, so as to obtain and output the corrected speckle contrast image.
[0188] In this embodiment of the application, after acquiring the speckle image, a light intensity distribution map is determined based on the speckle image, and an original speckle contrast map of the target speckle image is determined. Then, a local light intensity variation coefficient map is determined based on the light intensity distribution map. Finally, the values of the corresponding pixels in the original speckle contrast map are corrected based on the values of each pixel in the local light intensity variation coefficient map. Since the light intensity distribution map can reflect the spatial non-uniformity of illumination, the local light intensity variation coefficient map determined based on the light intensity distribution map quantifies the degree of fluctuation of illumination non-uniformity in the local range. By using the local light intensity variation coefficient map to perform pixel-by-pixel correction on the original speckle contrast map, the variation introduced by illumination non-uniformity can be reduced without changing the speckle contrast change caused by blood flow motion. That is, the influence of illumination non-uniformity on the laser speckle image is reduced, making the blood flow image brighter and more accurate in contrast, thereby improving the accuracy of blood flow velocity estimation.
[0189] In one possible implementation, the image correction module is specifically used for:
[0190] The original speckle contrast image is normalized.
[0191] The values of the corresponding pixels in the normalized original speckle contrast image are corrected based on the values of each pixel in the local light intensity variation coefficient map.
[0192] In one possible implementation, the first determining module is specifically used for:
[0193] Calculate the average value of each speckle image to obtain the average light intensity distribution map;
[0194] High-frequency details are removed from the average light intensity distribution map to obtain the light intensity distribution map.
[0195] In one possible implementation, the image correction module is specifically used for:
[0196] The original speckle contrast image is corrected using the following formula:
[0197] ;
[0198] in, This is the corrected speckle contrast image. This is the original speckle contrast image. This is a graph showing the coefficient of variation of local light intensity.
[0199] In one possible implementation, the original speckle contrast image is obtained by the following formula:
[0200] ;
[0201] in, For the pixels in the target speckle image The standard deviation of pixel intensity within the preset window, For the pixels in the target speckle image The average pixel intensity within the preset window.
[0202] In one possible implementation, the local light intensity variation coefficient map is obtained by the following formula:
[0203] ;
[0204] in, For the pixels in the light intensity distribution map The standard deviation of pixel intensity within the preset window, For the pixels in the light intensity distribution map The average pixel intensity within the preset window.
[0205] In one possible implementation, the device further includes:
[0206] The coefficient calculation module is used to calculate the first correlation coefficient between the original speckle contrast image and the light intensity distribution map, and to calculate the second correlation coefficient between the corrected speckle contrast image and the light intensity distribution map.
[0207] The coefficient output module is used to output the first correlation coefficient and the second correlation coefficient.
[0208] A third aspect of this application provides a correction system for laser speckle contrast images, such as... Figure 12 As shown, the system includes an image acquisition device 1201 and a processor 1202;
[0209] Image acquisition device 1201 is used to capture speckle images;
[0210] Processor 1202 is used to implement the laser speckle contrast image correction method described in the first aspect.
[0211] In this embodiment of the application, after acquiring the speckle image, a light intensity distribution map is determined based on the speckle image, and an original speckle contrast map of the target speckle image is determined. Then, a local light intensity variation coefficient map is determined based on the light intensity distribution map. Finally, the values of the corresponding pixels in the original speckle contrast map are corrected based on the values of each pixel in the local light intensity variation coefficient map. Since the light intensity distribution map can reflect the spatial non-uniformity of illumination, the local light intensity variation coefficient map determined based on the light intensity distribution map quantifies the degree of fluctuation of illumination non-uniformity in the local range. By using the local light intensity variation coefficient map to perform pixel-by-pixel correction on the original speckle contrast map, the variation introduced by illumination non-uniformity can be reduced without changing the speckle contrast change caused by blood flow motion. That is, the influence of illumination non-uniformity on the laser speckle image is reduced, making the blood flow image brighter and more accurate in contrast, thereby improving the accuracy of blood flow velocity estimation.
[0212] In one possible implementation, see Figure 13The image acquisition device 1201 includes a visible light source a, a near-infrared light source b, a dichroic beam combiner c, a beam expander d, a homogenizer e, a dichroic mirror g, a fluorescence channel h, a fluorescence camera i, a speckle channel j, and a speckle camera k. The visible light beam generated by the visible light source a and the near-infrared light beam generated by the near-infrared light source b are coupled into a target beam by the dichroic beam combiner c, and then passed through the beam expander d and the homogenizer e to expand and homogenize the target beam, respectively. The expanded and homogenized target beam illuminates the target biological tissue f. The beam reflected by the target biological tissue f is transmitted through the dichroic mirror g and then enters the speckle camera k through the speckle channel j for imaging. The beam reflected by the target biological tissue f is reflected by the dichroic mirror g and then enters the fluorescence camera i through the fluorescence channel h for imaging. In the embodiments of this application, a dichroic beam combiner combines white light and laser light into a single beam for illumination. A dichroic mirror then separates the returned mixed light according to wavelength, and the light is simultaneously acquired by a fluorescence camera and a speckle camera. This allows users to dynamically correlate the fluorescence changes of a specific molecular signal with local blood flow. Furthermore, the beam expander and homogenizer optimize the quality of the illumination beam, making it more uniform and collimated, thereby forming a high-quality, stable illumination spot on the target biological tissue, resulting in high-contrast, low-noise images.
[0213] The visible light beam and the near-infrared beam can be transmitted to the dichroic beam combiner c via a light guide component. The light guide component can be an optical fiber, a beam guide, or other light guide components. The materials of the light guide components used for transmitting different beams can be the same or different, and this application embodiment does not limit this.
[0214] like Figure 13 As shown, the dichroic beam combiner c, beam expander d, homogenizer e, and dichroic mirror g are all placed at 45°. The wavelength of the near-infrared beam generated by the near-infrared light source b is 650-1000 nm, and the wavelength of the visible light beam generated by the visible light source a is 400-650 nm.
[0215] In one possible implementation, the laser speckle contrast image correction system specifically includes a coherent light source assembly, a beam shaping / expanding assembly, a sample support and fixation assembly, an imaging lens assembly, an image sensor, a processor, and a display / storage unit. The system operates in a "light source—sample—lens—sensor—processing—display" chain. The processor is used to perform image acquisition timing management, light intensity distribution field estimation, contrast calculation and correction operations, and outputs the corrected contrast map or perfusion parameter map. It is understood that the processor mentioned here may be the same as or different from the processor described below, and this application embodiment does not limit this.
[0216] It is understood that, since the laser speckle contrast image correction method, device and system provided in this application can reduce the influence of uneven illumination on laser speckle images, making the blood flow image uniform in brightness and accurate in contrast, thereby improving the accuracy of blood flow velocity estimation, in one possible embodiment, the laser speckle contrast image correction method, device and system provided in this application can be applied to endoscopes and other equipment.
[0217] This invention also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus.
[0218] Memory, used to store computer programs;
[0219] When a processor executes a program stored in memory, it performs the following steps:
[0220] Acquire multiple speckle images;
[0221] Based on each speckle image, a light intensity distribution map is determined, wherein each pixel in the light intensity distribution map is used to characterize the spatial non-uniformity of the illumination of the pixel.
[0222] Determine the original speckle contrast image of the target speckle image, wherein each pixel in the original speckle contrast image is used to represent the coefficient of variation of the pixel intensity in the neighborhood of the pixel in the target speckle image, and the target speckle image is a speckle image of one speckle image or the mean image of multiple speckle images.
[0223] Based on the light intensity distribution map, a local light intensity variation coefficient map is determined, wherein each pixel in the local light intensity variation coefficient map is used to represent the variation coefficient of pixel intensity in the neighborhood of the pixel in the light intensity distribution map;
[0224] The values of the corresponding pixels in the original speckle contrast image are corrected based on the values of each pixel in the local light intensity variation coefficient image to obtain and output the corrected speckle contrast image.
[0225] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.
[0226] The communication interface is used for communication between the aforementioned electronic devices and other devices.
[0227] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.
[0228] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0229] In another embodiment of the present invention, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, implements the steps of any of the above-described laser speckle contrast image correction methods.
[0230] In another embodiment of the present invention, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the laser speckle contrast image correction methods described in the above embodiments.
[0231] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).
[0232] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0233] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and system embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0234] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.
Claims
1. A method for correcting laser speckle contrast images, characterized in that, The method includes: Acquire multiple speckle images; Based on each speckle image, a light intensity distribution map is determined, wherein each pixel in the light intensity distribution map is used to characterize the spatial non-uniformity of the illumination of the pixel. Determine the original speckle contrast image of the target speckle image, wherein the target speckle image is one speckle image or the average image of multiple speckle images; Based on the light intensity distribution map, a local light intensity variation coefficient map is determined, wherein each pixel in the local light intensity variation coefficient map is used to represent the variation coefficient of pixel intensity in the neighborhood of the pixel in the light intensity distribution map; The values of the corresponding pixels in the original speckle contrast image are corrected based on the values of each pixel in the local light intensity variation coefficient image to obtain and output the corrected speckle contrast image.
2. The method according to claim 1, characterized in that, The step of correcting the pixel values at corresponding positions in the original speckle contrast image based on the values of each pixel in the local intensity variation coefficient map includes: The original speckle contrast image is normalized. The values of the corresponding pixels in the normalized original speckle contrast image are corrected based on the values of each pixel in the local light intensity variation coefficient map.
3. The method according to claim 1, characterized in that, The step of determining the light intensity distribution map based on each of the speckle images includes: Calculate the average value of each speckle image to obtain the average light intensity distribution map; High-frequency details are removed from the average light intensity distribution map to obtain the light intensity distribution map.
4. The method according to claim 1, characterized in that, The step of correcting the pixel values at corresponding positions in the original speckle contrast image based on the values of each pixel in the local intensity variation coefficient map includes: The original speckle contrast image is corrected using the following formula: ； in, This is the corrected speckle contrast image. This is the original speckle contrast image. This is a graph showing the coefficient of variation of local light intensity.
5. The method according to claim 4, characterized in that, The original speckle contrast image was obtained using the following formula: ； in, For the pixels in the target speckle image The standard deviation of pixel intensity within the preset window For the pixels in the target speckle image The average pixel intensity within the preset window.
6. The method according to claim 4, characterized in that, The local light intensity variation coefficient map is obtained by the following formula: ； in, For the pixels in the light intensity distribution map The standard deviation of pixel intensity within the preset window For the pixels in the light intensity distribution map The average pixel intensity within the preset window.
7. The method according to claim 1, characterized in that, The method further includes: Calculate the first correlation coefficient between the original speckle contrast image and the light intensity distribution map, and calculate the second correlation coefficient between the corrected speckle contrast image and the light intensity distribution map; Output the first correlation coefficient and the second correlation coefficient.
8. A correction device for laser speckle contrast images, characterized in that, The device includes: The image acquisition module is used to acquire multiple speckle images; The first determining module is used to determine a light intensity distribution map based on each of the speckle images, wherein each pixel in the light intensity distribution map is used to characterize the spatial non-uniformity of the illumination of the pixel. The second determining module is used to determine the original speckle contrast image of the target speckle image, wherein the target speckle image is one speckle image or the average image of multiple speckle images; The third determining module is used to determine a local light intensity variation coefficient map based on the light intensity distribution map, wherein each pixel in the local light intensity variation coefficient map is used to represent the variation coefficient of pixel intensity in the neighborhood of the pixel in the light intensity distribution map; The image correction module is used to correct the values of the corresponding pixels in the original speckle contrast image according to the values of each pixel in the local light intensity variation coefficient map, so as to obtain and output the corrected speckle contrast image.
9. A correction system for laser speckle contrast images, characterized in that, The system includes an image acquisition device and a processor; The image acquisition device is used to capture speckle images; The processor is configured to implement the steps of the method according to any one of claims 1-7.
10. The calibration system according to claim 9, characterized in that, The image acquisition device includes a visible light source, a near-infrared light source, a dichroic beam combiner, a beam expander, a homogenizing mirror, a dichroic mirror, a fluorescence channel, a fluorescence camera, a speckle channel, and a speckle camera. The visible light beam generated by the visible light source and the near-infrared beam generated by the near-infrared light source are coupled into a target beam by the dichroic beam combiner, and then expanded and homogenized by the beam expander and the beam homogenizer in sequence. The expanded and homogenized target beam illuminates the target biological tissue. The beam reflected by the target biological tissue is transmitted through the dichroic mirror and then enters the speckle camera through the speckle channel for imaging. The beam reflected by the target biological tissue is reflected by the dichroic mirror and then enters the fluorescence camera through the fluorescence channel for imaging.