Image smoothing method and device, imaging apparatus, and storage medium

Abstract

The application relates to the technical field of image processing, and discloses a smoothing processing method and device of an image, an imaging equipment and a storage medium. The method comprises the following steps: acquiring ultrasonic scanning data and optical scanning data; determining a correlation coefficient weight and a distance weight between the ultrasonic scanning data and the optical scanning data based on the ultrasonic scanning data and the optical scanning data; and performing smoothing processing on the ultrasonic scanning data and the optical scanning data based on the correlation coefficient weight and the distance weight, so as to generate a multi-mode image for ultrasonic scanning and optical coherence tomography. Through the technical scheme, the loss of image features is avoided, the display grain feeling and line feeling of the multi-mode image are reduced, and the imaging quality of the multi-mode image is improved to the maximum extent.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and more specifically to an image smoothing method, apparatus, imaging device, and storage medium. Background Technology

[0002] Since both Intravascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT) have their own advantages, combining the two can achieve technological complementarity and give full play to the advantages of both intravascular imaging technologies.

[0003] To ensure that images can reflect more image features, denoising and smoothing are usually required for the scanned images. However, conventional smoothing only analyzes and processes one signal in one dimension. For multimodal images combining IVUS and OCT, this can lead to blurring of image edge and feature information, resulting in the loss of many image features and affecting the quality of the multimodal image. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide an image smoothing processing method, apparatus, imaging device, and storage medium to solve the problem of difficulty in smoothing multi-modal images.

[0005] In a first aspect, embodiments of the present invention provide an image smoothing method, comprising: acquiring ultrasound scan data and optical scan data; determining the correlation coefficient weight and distance weight between the ultrasound scan data and the optical scan data based on the ultrasound scan data and the optical scan data; and performing smoothing processing on the ultrasound scan data and the optical scan data based on the correlation coefficient weight and the distance weight to generate a multimode image for ultrasound scan and optical coherence tomography.

[0006] The image smoothing method provided in this embodiment of the invention combines the correlation coefficient weight and distance weight between ultrasound scan data and optical scan data to smooth the ultrasound scan data and optical scan data. This not only considers the distance relationship between different scan data, but also the correlation degree between different scan data, avoids the loss of image features, reduces the display graininess and lineiness of multi-mode images, and improves the imaging quality of multi-mode images to the greatest extent.

[0007] In one optional implementation, the correlation coefficient weight and distance weight between the ultrasound scan data and the optical scan data are determined based on ultrasound scan data and optical scan data, including: acquiring ultrasound scan data and optical scan data at the same time; determining the correlation coefficient between the ultrasound scan data and the optical scan data at the same time, a first autocorrelation coefficient corresponding to the ultrasound scan data, and a second autocorrelation coefficient corresponding to the optical scan data; determining the correlation coefficient weight between ultrasound and optics based on the correlation coefficient, the first autocorrelation coefficient, and the second autocorrelation coefficient; and performing distance weighting on the ultrasound scan data and the optical scan data respectively based on a preset weighting method to obtain the distance weights of ultrasound and optics respectively.

[0008] The image smoothing method provided in this invention determines the correlation coefficient and autocorrelation coefficient of scan data generated by different scanning methods at the same time, thereby clarifying the degree of correlation of scan data obtained by different scanning methods. This facilitates the elimination of irrelevant data and reduces noise in multi-mode images. Simultaneously, it determines the distance weights corresponding to ultrasound scan data and optical scan data respectively, facilitating image smoothing by combining the distance between scan data to eliminate image graininess and lines.

[0009] In one optional implementation, the correlation coefficient weights include: a first correlation coefficient weight corresponding to ultrasound scan data and a second correlation coefficient weight corresponding to optical scan data; determining the correlation coefficient weights for ultrasound and optical scans based on the correlation coefficient, the first autocorrelation coefficient, and the second autocorrelation coefficient includes: comparing the correlation coefficient with a first threshold, comparing the first autocorrelation coefficient with a second threshold, and comparing the second autocorrelation coefficient with a third threshold; determining the first correlation coefficient weight for ultrasound scan data based on the comparison results of the correlation coefficient with the first threshold and the comparison results of the first autocorrelation coefficient with the second threshold; and determining the second correlation coefficient weight for optical scan data based on the comparison results of the correlation coefficient with the first threshold and the comparison results of the second autocorrelation coefficient with the third threshold.

[0010] The image smoothing method provided in this embodiment of the invention compares the correlation coefficient and autocorrelation coefficient with their corresponding thresholds, and determines the correlation coefficient weight based on the comparison results, thereby accurately determining the correlation degree of the scanned data.

[0011] In one optional implementation, based on the comparison results of the correlation coefficient and a first threshold, and the comparison results of the first autocorrelation coefficient and a second threshold, a first correlation coefficient weight for the ultrasound scan data is determined, including: when the correlation coefficient exceeds the first threshold and the first autocorrelation coefficient exceeds the second threshold, determining the first correlation coefficient weight of the ultrasound scan data as a first preset value; when the correlation coefficient exceeds the first threshold and the first autocorrelation coefficient is less than the second threshold, determining the first correlation coefficient weight of the ultrasound scan data as a second preset value; when the correlation coefficient is less than the first threshold and the first autocorrelation coefficient exceeds the second threshold, determining the first correlation coefficient weight of the ultrasound scan data as a third preset value; when the correlation coefficient is less than the first threshold and the first autocorrelation coefficient is less than the second threshold, determining the first correlation coefficient weight of the ultrasound scan data as a fourth preset value; wherein the first preset value, the second preset value, the third preset value, and the fourth preset value are all different; and / or

[0012] Based on the comparison results of the correlation coefficient and the first threshold, and the comparison results of the second autocorrelation coefficient and the third threshold, the weight of the second correlation coefficient for the optical scanning data is determined, including: when the correlation coefficient exceeds the first threshold and the second autocorrelation coefficient exceeds the third threshold, the weight of the second correlation coefficient for the optical scanning data is determined to be a fifth preset value; when the correlation coefficient exceeds the first threshold and the second autocorrelation coefficient is less than the third threshold, the weight of the second correlation coefficient for the optical scanning data is determined to be a sixth preset value; when the correlation coefficient is less than the first threshold and the second autocorrelation coefficient exceeds the third threshold, the weight of the second correlation coefficient for the optical scanning data is determined to be a seventh preset value; when the correlation coefficient is less than the first threshold and the second autocorrelation coefficient is less than the third threshold, the weight of the second correlation coefficient for the optical scanning data is determined to be an eighth preset value; wherein the fifth, sixth, seventh, and eighth preset values ​​are all different.

[0013] The image smoothing method provided in this embodiment of the invention sets different correlation coefficient weight values ​​for different comparison results, which facilitates image smoothing based on the correlation of scanned data and avoids loss of feature information.

[0014] In one optional implementation, the distance weighting includes: a first distance weight corresponding to the ultrasound scan data and a second distance weight corresponding to the optical scan data; performing distance weighting on the ultrasound scan data and the optical scan data respectively based on a preset weighting method to obtain the distance weights for ultrasound and optics respectively, including: determining distance weighting parameters for the ultrasound scan data and the optical scan data respectively based on the preset weighting method; performing weighting processing on the ultrasound scan data according to the distance weighting parameters to obtain the first distance weight; and performing weighting processing on the optical scan data according to the distance weighting parameters to obtain the second distance weight.

[0015] The image smoothing method provided in this embodiment of the invention performs weighted processing on ultrasound scan data and optical scan data respectively by using distance weighting parameters to obtain corresponding distance weights. The distance weights characterize the relative distance between each pixel point corresponding to the scan data, thereby enabling further smoothing of the image based on the distance weights and reducing abrupt gradient changes in the image.

[0016] In one optional implementation, determining the correlation coefficient, the first autocorrelation coefficient corresponding to the ultrasound scan data, and the second autocorrelation coefficient corresponding to the optical scan data generated at the same time further includes: calculating the modulus of the ultrasound scan data and the optical scan data respectively to obtain the first modulus value corresponding to the ultrasound scan data and the second modulus value corresponding to the optical scan data; determining the correlation coefficient based on the first modulus value and the second modulus value; determining the first autocorrelation coefficient based on the first modulus value and determining the second autocorrelation coefficient based on the second modulus value.

[0017] The image smoothing method provided in this embodiment of the invention calculates the magnitude values ​​of ultrasound scan data and optical scan data, and determines the correlation coefficient and autocorrelation coefficient based on the magnitude values, which can accurately reflect the degree of correlation between ultrasound scan data and optical scan data at the same time.

[0018] In one optional implementation, ultrasound scan data and optical scan data are smoothed based on correlation coefficient weights and distance weights to generate a multimode image for ultrasound scans and optical coherence tomography (OCT) scans. This includes: multiplying the first correlation coefficient weight corresponding to the ultrasound scan data by a first distance weight to smooth the ultrasound scan data, obtaining smoothed ultrasound scan pixels; multiplying the second correlation coefficient weight corresponding to the optical scan data by a second distance weight to smooth the optical scan data, obtaining smoothed optical scan pixels; and combining the ultrasound scan pixels and the optical scan pixels to generate a multimode image.

[0019] The image smoothing method provided in this embodiment of the invention determines the smoothed ultrasound scan pixels and optical scan pixels, and generates a multi-mode image based on the ultrasound scan pixels and optical scan pixels, thereby achieving smoothing processing for different scan data and ensuring the generation quality of the multi-mode image.

[0020] Secondly, embodiments of the present invention provide an image smoothing processing apparatus, comprising: an acquisition module for acquiring ultrasound scan data and optical scan data; a weight determination module for determining the correlation coefficient weight and distance weight between the ultrasound scan data and the optical scan data based on the ultrasound scan data and the optical scan data; and a generation module for smoothing the ultrasound scan data and the optical scan data based on the correlation coefficient weight and the distance weight to generate a multimode image for ultrasound scan and optical coherence tomography.

[0021] Thirdly, embodiments of the present invention provide a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the image smoothing method described in the first aspect or any corresponding embodiment.

[0022] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions for causing a computer to perform an image smoothing method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of an image smoothing method according to some embodiments of the present invention;

[0025] Figure 2 This is a schematic flowchart of another image smoothing method according to some embodiments of the present invention;

[0026] Figure 3 This is a schematic diagram of a distance-weighted target according to some embodiments of the present invention;

[0027] Figure 4 This is a schematic diagram illustrating the determination of distance weights corresponding to pixels according to some embodiments of the present invention;

[0028] Figure 5 This is a structural block diagram of an image smoothing processing apparatus according to an embodiment of the present invention;

[0029] Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Intravascular ultrasound (IVUS) and optical coherence tomography (OCT) are commonly used intravascular imaging techniques in interventional treatment of coronary artery disease. IVUS is characterized by its strong penetrating power, enabling it to penetrate the blood and achieve deeper imaging depth, but its resolution is relatively low. On the other hand, OCT is characterized by its high resolution, which can better compare plaque components such as lipids, calcifications, fibrous tissue, and thrombi, but its penetrating power is relatively weak. The combined application of these two techniques achieves technological complementarity, leveraging the advantages of both intravascular imaging techniques, thus saving surgical time and improving treatment accuracy.

[0032] Currently, the display mode for extravascular imaging in related technologies is B-mode, meaning the echo or brightness of the observed structure depends on the intensity of the reflected signal. It displays the image of the tissue being examined at a specific moment in the form of a planar graphic. During the examination, the reflected signal from the human body interface is first converted into light spots of varying intensity. These light spots can be displayed on a fluorescent screen; the brighter the image, the stronger the reflected signal.

[0033] However, intravascular imaging display modes also include L-mode, which involves digitizing and accumulating a series of cross-sectional images and using them to construct a longitudinal view of the vascular anatomy of the selected section. This reflects the morphology of the vessel over a period of time and can accurately display the vessel's diameter, area, and branching. Therefore, L-mode can accurately display vascular morphology, such as diameter, area, and branching, and is of great reference value for observing vascular stenosis, calcification, fibrosis, and thrombosis. Therefore, if the images in L-mode are not smoothed, they will exhibit excessive graininess or obvious lines, affecting the observation of the region of interest and consequently impacting the accuracy of measurements such as vessel length, diameter, and area.

[0034] However, conventional smoothing only analyzes and processes one signal in one dimension. For multimodal images combining IVUS and OCT, this can lead to blurring of image edge and feature information, resulting in the loss of many image features and affecting the quality of the multimodal image.

[0035] Based on this, the technical solution of the present invention combines different scanning data to calculate the correlation coefficient and autocorrelation coefficient of the two signals. It not only considers the distance relationship between the signals, but also the similarity between the signals and the similarity of the signals themselves. This reduces the graininess and lines of the image, makes the image brightness change smoothly and gradually, reduces abrupt gradients, and improves the image display quality in various display modes.

[0036] According to an embodiment of the present invention, an embodiment of an image smoothing processing method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] This embodiment provides an image smoothing method that can be used in imaging devices, such as multi-modal imaging devices. Figure 1 This is a flowchart of an image smoothing method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0038] Step S101: Acquire ultrasound scan data and optical scan data.

[0039] Ultrasound scan data refers to ultrasound imaging data acquired using Intra Vascular Ultrasound (IVUS) technology; optical scan data refers to optical imaging data acquired using Optical Coherence Tomography (OCT) technology.

[0040] Multimodal imaging devices have both ultrasound scanning and optical scanning modes. When a scan of the tissue to be examined is initiated, the multimodal imaging device can acquire ultrasound scanning data (i.e., IVUS data) and optical scanning data (i.e., OCT data) of the tissue to be examined in real time.

[0041] Step S102: Based on the ultrasound scan data and optical scan data, determine the correlation coefficient weight and distance weight between the ultrasound scan data and the optical scan data.

[0042] The correlation coefficient weight is used to characterize the degree of correlation between ultrasound scan data and optical scan data. Specifically, ultrasound scan data and optical scan data acquired at the same time are for the same location. For ultrasound scan data and optical scan data acquired at the same acquisition time, the correlation coefficient between ultrasound scan data and optical scan data can be calculated to determine the degree of correlation between them; alternatively, the autocorrelation coefficients of ultrasound scan data and optical scan data acquired at adjacent time points can be calculated separately to determine the degree of correlation between scan data acquired at adjacent time points.

[0043] Distance weights are used to characterize the distance between each pixel in ultrasound and optical scan data and the scan center. Specifically, for ultrasound and optical scan data, a Gaussian function can be used to set a distance weighting template, and this template is used to determine the distance weights of the ultrasound and optical scan data.

[0044] Step S103: Based on the correlation coefficient weight and distance weight, smooth the ultrasound scan data and optical scan data to generate multi-mode images for ultrasound scan and optical scan.

[0045] By combining the correlation coefficient weights and distance weights of the ultrasound scan data, the smoothing value of each pixel in the ultrasound scan data is calculated. Simultaneously, by combining the correlation coefficient weights and distance weights of the optical scan data, the smoothing value of each pixel in the optical scan data can be calculated. By generating an image based on the smoothing values ​​corresponding to each pixel, a smoothed multimodal image can be obtained.

[0046] For multi-mode images in L-mode display mode, by considering the distance relationship between signals, the similarity between signals, and the similarity of the signals themselves, the image graininess and lineiness in L-mode display mode are reduced, the image brightness can change smoothly and gradually, the abrupt gradient is reduced, the image quality in L-mode display mode is improved, the image features are clearer, and it is easier to accurately measure and observe the tissue to be detected.

[0047] The image smoothing method provided in this embodiment combines the correlation coefficient weight and distance weight between ultrasound scan data and optical scan data to smooth the ultrasound scan data and optical scan data. This not only takes into account the distance relationship between different scan data, but also the correlation degree between different scan data, avoids the loss of image features, reduces the display graininess and lineiness of multi-mode images, and improves the imaging quality of multi-mode images to the greatest extent.

[0048] This embodiment provides an image smoothing method that can be used in imaging devices, such as multi-modal imaging devices. Figure 2This is a flowchart of an image smoothing method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0049] Step S201: Acquire ultrasound scan data and optical scan data. For detailed explanation, please refer to the relevant descriptions in the above embodiments; they will not be repeated here.

[0050] Step S202: Based on the ultrasound scan data and optical scan data, determine the correlation coefficient weight and distance weight between the ultrasound scan data and the optical scan data.

[0051] Specifically, step S202 above may include:

[0052] Step S2021: Acquire ultrasound scan data and optical scan data at the same time.

[0053] The multi-mode imaging device scans the tissue to be examined according to a pre-set scanning sequence to acquire ultrasound and optical scanning data of the tissue in real time. By combining the scanning sequence, the ultrasound and optical scanning data at the same moment can be determined from the acquired ultrasound and optical scanning data.

[0054] Step S2022: Determine the correlation coefficient between the ultrasound scan data and the optical scan data at the same time, the first autocorrelation coefficient corresponding to the ultrasound scan data, and the second autocorrelation coefficient corresponding to the optical scan data.

[0055] The correlation coefficient is used to characterize the degree of correlation between ultrasound scan data and optical scan data. This correlation reflects the degree of correlation between the two data points, helping to determine whether they represent scans of the same location. Specifically, the correlation coefficient is determined as follows:

[0056]

[0057] Where X and Y represent the modulus values ​​corresponding to ultrasound scan data and optical scan data, respectively; Cov(X,Y) represents the covariance of X and Y, Var[X] is the variance of X, and Var[Y] is the variance of Y. The closer r(X,Y) is to 1, the stronger the correlation between X and Y, and vice versa.

[0058] The first autocorrelation coefficient is used to characterize the correlation between ultrasound scan data at adjacent time points. Specifically, the first autocorrelation coefficient is determined as follows:

[0059]

[0060] in, x represents the population mean;i This represents the ultrasound scan data at the current moment; x i+h Indicates with x i Ultrasound scan data at adjacent time points; n represents the acquisition time.

[0061] The second autocorrelation coefficient is used to characterize the correlation between optical scanning data at adjacent time points. The determination method for the second autocorrelation coefficient is the same as that for the first autocorrelation coefficient, and will not be repeated here.

[0062] In some alternative embodiments, step S2022 may include:

[0063] Step a1: Perform modulus calculation on the ultrasound scan data and the optical scan data respectively to obtain the first modulus value corresponding to the ultrasound scan data and the second modulus value corresponding to the optical scan data.

[0064] Step a2: Determine the correlation coefficient based on the first and second modulus values;

[0065] Step a3: Determine the first autocorrelation coefficient based on the first modulus value, and determine the second autocorrelation coefficient based on the second modulus value.

[0066] The first modulus value corresponding to the ultrasound scan data was obtained by performing modulus calculations on both the ultrasound scan data and the optical scan data. Among them, I A Q represents the in-phase component of ultrasound scan data; A This represents the orthogonal components of ultrasound scan data.

[0067] Similarly, the second modulus value corresponding to the optical scanning data can be obtained. Among them, I B Q represents the in-phase component of the optical scanning data. B This represents the orthogonal components of optical scanning data.

[0068] Based on the correlation coefficient determination method described above, the correlation coefficient is calculated using the first and second modulus values. Similarly, based on the autocorrelation coefficient determination method described above, the first autocorrelation coefficient is determined using the first modulus value, and the second autocorrelation coefficient is determined using the second modulus value.

[0069] Here, by determining the modulus values ​​of ultrasound scan data and optical scan data, and then determining the correlation coefficient and autocorrelation coefficient based on the modulus values, the degree of correlation between ultrasound scan data and optical scan data at the same moment can be accurately reflected.

[0070] Step S2023: Determine the correlation coefficient weights of ultrasound and optics based on the correlation coefficient, the first autocorrelation coefficient, and the second autocorrelation coefficient.

[0071] By combining the correlation coefficient, we can determine whether the optical scan data of ultrasound scans are strongly correlated. Combining the first autocorrelation coefficient, we can determine whether the ultrasound scan data are strongly correlated within adjacent acquisition cycles. Combining the second autocorrelation coefficient, we can determine whether the optical scan data are strongly correlated within adjacent acquisition cycles. Different weights are assigned to the strong and weak correlations of the scan data to obtain the corresponding correlation coefficient weights for various scenarios.

[0072] The correlation coefficient weights include: a first correlation coefficient weight corresponding to the ultrasound scan data and a second correlation coefficient weight corresponding to the optical scan data. In some optional embodiments, step S2023 may include:

[0073] Step b1: Compare the correlation coefficient with the first threshold, the first autocorrelation coefficient with the second threshold, and the second autocorrelation coefficient with the third threshold.

[0074] Step b2: Based on the comparison results of the correlation coefficient and the first threshold, and the comparison results of the first autocorrelation coefficient and the second threshold, determine the weight of the first correlation coefficient for the ultrasound scan data.

[0075] Step b3: Based on the comparison results of the correlation coefficient and the first threshold, and the comparison results of the second autocorrelation coefficient and the third threshold, determine the weight of the second correlation coefficient for the optical scanning data.

[0076] The first threshold is a pre-set correlation coefficient representing a strong correlation, such as 0.8, 0.85, or 0.9. The second threshold is a pre-set autocorrelation coefficient representing a strong correlation within the ultrasound scan data itself, such as 0.8, 0.85, or 0.9. The third threshold is a pre-set autocorrelation coefficient representing a strong correlation within the optical scan data itself, such as 0.8, 0.85, or 0.9.

[0077] The correlation coefficient is compared with a first threshold to determine whether the correlation coefficient exceeds the first threshold; the first autocorrelation coefficient is compared with a second threshold to determine whether the first autocorrelation coefficient exceeds the second threshold; the second autocorrelation coefficient is compared with a third threshold to determine whether the second autocorrelation coefficient exceeds the third threshold.

[0078] Based on the above comparison results, the weights of the first correlation coefficient for ultrasound scanning data and the weights of the second correlation coefficient for optical scanning data can be determined respectively.

[0079] In the above implementation, the correlation coefficient and autocorrelation coefficient are compared with their corresponding thresholds, and the correlation coefficient weight is determined by combining the comparison results, thereby accurately determining the correlation degree of the scanned data.

[0080] Specifically, step b2 above may include:

[0081] Step b21: When the correlation coefficient exceeds the first threshold and the first autocorrelation coefficient exceeds the second threshold, the weight of the first correlation coefficient of the ultrasound scan data is determined to be the first preset value.

[0082] Step b22: When the correlation coefficient exceeds the first threshold and the first autocorrelation coefficient is less than the second threshold, the weight of the first correlation coefficient of the ultrasound scan data is determined to be the second preset value.

[0083] Step b23: When the correlation coefficient is less than the first threshold and the first autocorrelation coefficient exceeds the second threshold, the weight of the first correlation coefficient of the ultrasound scan data is determined to be the third preset value.

[0084] Step b24: When the correlation coefficient is less than the first threshold and the first autocorrelation coefficient is less than the second threshold, the weight of the first correlation coefficient of the ultrasound scan data is determined to be the fourth preset value.

[0085] The first, second, third, and fourth preset values ​​are all different, each representing a different degree of correlation.

[0086] When the correlation coefficient exceeds the first threshold and the first autocorrelation coefficient exceeds the second threshold, it indicates that the ultrasound scan data and the optical scan data are strongly correlated and strongly correlated with each other. At this time, the weight of the first correlation coefficient can be assigned to the first preset value Wa, for example, Wa = 1.2.

[0087] When the correlation coefficient exceeds the first threshold and the first autocorrelation coefficient is less than the second threshold, it indicates that the ultrasound scan data and the optical scan data are strongly correlated and weakly correlated with each other. At this time, the weight of the first correlation coefficient can be assigned to the first preset value Wb, for example, Wb = 1.0.

[0088] When the correlation coefficient is less than the first threshold and the first autocorrelation coefficient exceeds the second threshold, it indicates that the ultrasound scan data and the optical scan data are weakly correlated, but strongly correlated with each other. In this case, the weight of the first correlation coefficient can be assigned to the first preset value Wc, for example, Wc = 0.8.

[0089] When the correlation coefficient is less than the first threshold and the first autocorrelation coefficient is less than the second threshold, it indicates that the ultrasound scan data and the optical scan data are weakly correlated, but weakly correlated with each other. In this case, the weight of the first correlation coefficient can be assigned to the first preset value Wd, for example, Wd = 0.5.

[0090] Specifically, step b3 above may include:

[0091] Step b31: When the correlation coefficient exceeds the first threshold and the second autocorrelation coefficient exceeds the third threshold, the weight of the second correlation coefficient of the optical scanning data is determined to be the fifth preset value.

[0092] Step b32: When the correlation coefficient exceeds the first threshold and the second autocorrelation coefficient is less than the third threshold, the weight of the second correlation coefficient of the optical scanning data is determined to be the sixth preset value.

[0093] Step b33: When the correlation coefficient is less than the first threshold and the second autocorrelation coefficient exceeds the third threshold, the weight of the second correlation coefficient of the optical scanning data is determined to be the seventh preset value.

[0094] Step b34: When the correlation coefficient is less than the first threshold and the second autocorrelation coefficient is less than the third threshold, the weight of the second correlation coefficient of the optical scanning data is determined to be the eighth preset value.

[0095] Among them, the fifth, sixth, seventh, and eighth preset values ​​are all different.

[0096] When the correlation coefficient exceeds the first threshold and the second autocorrelation coefficient exceeds the third threshold, it indicates that the optical scanning data and the ultrasound scanning data are strongly correlated and strongly correlated with each other. At this time, the weight of the second correlation coefficient can be assigned to the fifth preset value We, for example, We = 1.0.

[0097] When the correlation coefficient exceeds the first threshold and the second autocorrelation coefficient is less than the third threshold, it indicates that the optical scanning data and the ultrasound scanning data are strongly correlated, but weakly correlated on their own. In this case, the weight of the second correlation coefficient can be assigned to the sixth preset value Wf, for example, Wf = 0.8.

[0098] When the correlation coefficient is less than the first threshold and the second autocorrelation coefficient exceeds the third threshold, it indicates that the optical scanning data and the ultrasound scanning data are weakly correlated, but strongly correlated with each other. In this case, the weight of the second correlation coefficient can be assigned to the seventh preset value Wg, for example, Wg = 0.6.

[0099] When the correlation coefficient is less than the first threshold and the second autocorrelation coefficient is less than the second threshold, it indicates that the optical scanning data and the ultrasound scanning data are weakly correlated, but weakly correlated with each other. In this case, the weight of the second correlation coefficient can be assigned to the eighth preset value Wh, for example, Wh = 0.3.

[0100] Here, by setting different correlation coefficient weights for different comparison results, it is convenient to perform image smoothing based on the correlation of the scanned data, thus avoiding the loss of feature information.

[0101] Step S2024: Based on a preset weighting method, distance weights are applied to the ultrasound scan data and optical scan data respectively to obtain the distance weights for ultrasound and optical scans.

[0102] The preset weighting method is a pre-defined distance weighting method. The relative distances of each pixel in the ultrasound scan data and optical scan data are weighted by this preset weighting method to obtain the corresponding distance weights.

[0103] The distance weights include: a first distance weight corresponding to the ultrasound scan data and a second distance weight corresponding to the optical scan data. In some optional embodiments, step S2024 above may include:

[0104] Step c1: Determine the distance weighting parameters for ultrasound scan data and optical scan data respectively based on the preset weighting method.

[0105] Step c2: Weight the ultrasound scan data according to the distance weighting parameters to obtain the first distance weight.

[0106] Step c3: The optical scanning data is weighted according to the distance weighting parameters to obtain the second distance weight.

[0107] The distance weighting template is determined based on a preset weighting method, such as calculating an N*N distance template using a Gaussian function. Taking a 3*3 distance template as an example, the specific determination method is as follows:

[0108]

[0109] Where Δx and Δy represent the relative distances from a pixel within the template to the template center, and σ is the standard deviation, which is taken as σ = 1 here. The distance-weighted parameters can be calculated using multiple sets of Δx and Δy. To ensure computational convenience, the calculated distance-weighted parameters can be rounded to obtain... Figure 3 The diagram shows a 3x3 distance template. The closer the distance to the center, the greater the weight; conversely, the farther away, the smaller the weight.

[0110] Based on the distance template, the first distance weight corresponding to the ultrasound scan data is obtained by performing a weighting coefficient transformation on each scan data and its surrounding data. Similarly, the second distance weight corresponding to the optical scan data is obtained by performing a weighting coefficient transformation on each scan data and its surrounding data.

[0111] by Figure 4Taking the ultrasound scan data shown as an example, if we want to calculate the first distance weight corresponding to the intermediate data 226, we can convert the intermediate data and its surrounding data into weight coefficients based on the distance template. The value of the intermediate data 226 after weight coefficient conversion can be obtained. The specific calculation method is: (40×0.05+107×0.1+5×0.05)+(198×0.1+226×0.4+223×0.1)+(37×0.05+68×0.1+193×0.05)=164.

[0112] Here, the ultrasound scan data and optical scan data are weighted by distance weighting parameters to obtain the corresponding distance weights. The distance weights represent the relative distance between each pixel in the scan data, thereby enabling further smoothing of the image and reducing abrupt gradient changes in the image.

[0113] Step S203: Based on the correlation coefficient weight and distance weight, smooth the ultrasound scan data and optical scan data to generate multimode images for ultrasound scan and optical coherence tomography.

[0114] Specifically, step S203 above may include:

[0115] Step S2031: Multiply the weight of the first correlation coefficient corresponding to the ultrasound scan data with the weight of the first distance to smooth the ultrasound scan data and obtain the smoothed ultrasound scan pixels.

[0116] By performing a weighted coefficient transformation on each pixel of the ultrasound scan data, we can obtain the first distance weight after the weighted coefficient transformation. Multiplying the first correlation coefficient weight of the ultrasound scan data, as determined above, with the first distance weight corresponding to each pixel in the ultrasound scan data, yields the pixel value corresponding to each ultrasound scan pixel. For example, if the first correlation coefficient weight Wb = 1.0, then each value after the weighted coefficient transformation can be multiplied by 1.0 (e.g., 164 * 1.0 = 164.0) to obtain the pixel value corresponding to each ultrasound scan pixel.

[0117] Step S2032: Multiply the second correlation coefficient weight corresponding to the optical scanning data with the second distance weight to smooth the optical scanning data and obtain the smoothed optical scanning pixels.

[0118] By transforming the weight coefficients of each pixel in the optical scan data, we can obtain the second distance weight after weight coefficient transformation. Multiplying the determined second correlation coefficient weight of the optical scan data with the second distance weight corresponding to each pixel in the optical scan data, we can obtain the pixel value corresponding to the optical scan pixel.

[0119] Step S2033: Combine the ultrasound scanning pixels with the optical scanning pixels to generate a multi-mode image.

[0120] The smoothed ultrasound scan pixels and optical scan pixels are combined separately, and image generation processing is performed according to the pixel values ​​corresponding to the ultrasound scan pixels and optical scan pixels to generate a smoothed multi-mode image.

[0121] The image smoothing method provided in this embodiment determines the correlation coefficient and autocorrelation coefficient of scan data generated by different scanning methods at the same time. This clarifies the correlation degree of scan data obtained by different scanning methods, facilitating the elimination of irrelevant data and reducing noise in multimodal images. Simultaneously, it determines the distance weights corresponding to ultrasound scan data and optical scan data respectively, enabling image smoothing based on the distance between scan data to eliminate graininess and lines in the image. By determining the smoothed ultrasound scan pixels and optical scan pixels, a multimodal image is generated based on these pixels, thereby achieving smoothing of different scan data and ensuring the quality of multimodal image generation.

[0122] This embodiment also provides an image smoothing processing apparatus for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0123] This embodiment provides an image smoothing processing device, such as... Figure 5 As shown, it includes:

[0124] The acquisition module 301 is used to acquire ultrasound scan data and optical scan data.

[0125] The weight determination module 302 is used to determine the correlation coefficient weight and distance weight between the ultrasound scan data and the optical scan data based on the ultrasound scan data and the optical scan data.

[0126] The generation module 303 is used to smooth the ultrasound scan data and optical scan data based on the correlation coefficient weight and distance weight, and generate multi-mode images for ultrasound scan and optical scan.

[0127] In some alternative embodiments, the weight determination module 302 described above may include:

[0128] The data acquisition unit is used to acquire ultrasound scan data and optical scan data at the same time.

[0129] The coefficient determination unit is used to determine the correlation coefficient between the ultrasound scan data and the optical scan data at the same time, the first autocorrelation coefficient corresponding to the ultrasound scan data, and the second autocorrelation coefficient corresponding to the optical scan data.

[0130] The correlation coefficient weighting unit is used to determine the correlation coefficient weights of ultrasound and optics based on the correlation coefficient, the first autocorrelation coefficient, and the second autocorrelation coefficient.

[0131] The distance weight determination unit is used to perform distance weighting on ultrasound scan data and optical scan data respectively based on a preset weighting method to obtain the distance weights for ultrasound and optics respectively.

[0132] In some alternative embodiments, the coefficient determination unit may include:

[0133] The model-finding sub-unit is used to calculate the model values ​​of the ultrasonic scanning data and the optical scanning data respectively, so as to obtain the first model value corresponding to the ultrasonic scanning data and the second model value corresponding to the optical scanning data.

[0134] The correlation coefficient determination subunit is used to determine the correlation coefficient based on the first modulus and the second modulus.

[0135] The autocorrelation coefficient determination sub-unit is used to determine the first autocorrelation coefficient based on the first modulus value and the second autocorrelation coefficient based on the second modulus value.

[0136] The correlation coefficient weights include: a first correlation coefficient weight corresponding to the ultrasound scan data and a second correlation coefficient weight corresponding to the optical scan data. Accordingly, the correlation coefficient weight determination unit may include:

[0137] The comparison subunit is used to compare the correlation coefficient with the first threshold, the first autocorrelation coefficient with the second threshold, and the second autocorrelation coefficient with the third threshold, respectively.

[0138] The first weight determination subunit is used to determine the first correlation coefficient weight for ultrasound scan data based on the comparison results of the correlation coefficient and the first threshold, and the comparison results of the first autocorrelation coefficient and the second threshold.

[0139] The second weighting determination subunit is used to determine the second correlation coefficient weight for optical scanning data based on the comparison results of the correlation coefficient and the first threshold, and the comparison results of the second autocorrelation coefficient and the third threshold.

[0140] In some optional embodiments, the first weight determination subunit is specifically used to: determine the weight of the first correlation coefficient of the ultrasound scan data as a first preset value when the correlation coefficient exceeds a first threshold and the first autocorrelation coefficient exceeds a second threshold; determine the weight of the first correlation coefficient of the ultrasound scan data as a second preset value when the correlation coefficient exceeds the first threshold and the first autocorrelation coefficient is less than the second threshold; determine the weight of the first correlation coefficient of the ultrasound scan data as a third preset value when the correlation coefficient is less than the first threshold and the first autocorrelation coefficient exceeds the second threshold; and determine the weight of the first correlation coefficient of the ultrasound scan data as a fourth preset value when the correlation coefficient is less than the first threshold and the first autocorrelation coefficient is less than the second threshold; wherein the first preset value, the second preset value, the third preset value, and the fourth preset value are all different.

[0141] In some optional embodiments, the second weight determination subunit is specifically used to: determine the second correlation coefficient weight of the optical scanning data as a fifth preset value when the correlation coefficient exceeds a first threshold and the second autocorrelation coefficient exceeds a third threshold; determine the second correlation coefficient weight of the optical scanning data as a sixth preset value when the correlation coefficient exceeds the first threshold and the second autocorrelation coefficient is less than the third threshold; determine the second correlation coefficient weight of the optical scanning data as a seventh preset value when the correlation coefficient is less than the first threshold and the second autocorrelation coefficient exceeds the third threshold; and determine the second correlation coefficient weight of the optical scanning data as an eighth preset value when the correlation coefficient is less than the first threshold and the second autocorrelation coefficient is less than the third threshold; wherein the fifth, sixth, seventh, and eighth preset values ​​are all different.

[0142] The distance weights include: a first distance weight corresponding to the ultrasound scan data and a second distance weight corresponding to the optical scan data. Accordingly, the distance weight determination unit may include:

[0143] The weighted parameter determination subunit is used to determine the distance weighting parameters for ultrasound scan data and optical scan data respectively based on a preset weighting method.

[0144] The first weighted processing subunit is used to weight the ultrasound scan data according to the distance weighting parameters to obtain the first distance weight.

[0145] The second weighted processing subunit is used to weight the optical scanning data according to the distance weighting parameters to obtain the second distance weight.

[0146] In some alternative embodiments, the generation module 303 described above may include:

[0147] The first smoothing unit is used to multiply the correlation coefficient weight corresponding to the ultrasound scan data with the distance weight to smooth the ultrasound scan data and obtain smoothed ultrasound scan pixels.

[0148] The second smoothing unit is used to multiply the second correlation coefficient weight corresponding to the optical scanning data with the second distance weight to smooth the optical scanning data and obtain smoothed optical scanning pixels.

[0149] The combination unit is used to combine ultrasonic scanning pixels with optical scanning pixels to generate multi-mode images.

[0150] The further functional descriptions of each module and unit are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0151] In this embodiment, the image smoothing device is presented in the form of a functional unit. Here, a unit refers to an ASIC circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0152] The image smoothing device provided in this embodiment combines the correlation coefficient weight and distance weight between ultrasound scan data and optical scan data to smooth the ultrasound scan data and optical scan data. This not only takes into account the distance relationship between different scan data, but also the correlation degree between different scan data, avoids the loss of image features, reduces the display graininess and lineiness of multi-mode images, and improves the imaging quality of multi-mode images to the greatest extent.

[0153] This invention also provides an imaging device having the above-described features. Figure 5 The image smoothing device shown.

[0154] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of an imaging device provided in an optional embodiment of the present invention, such as... Figure 6As shown, the imaging device includes one or more processors 10, a memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple other devices can be connected, each providing some of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0155] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0156] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0157] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the imaging device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0158] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0159] The imaging device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 20 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0160] Input device 30 can receive input digital or character information, and generate key signal inputs related to user settings and function control of the imaging device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0161] The imaging device also includes a communication interface for data communication between the imaging device and other devices or communication networks.

[0162] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0163] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for smoothing an image, characterized in that, The method includes: Acquire ultrasound and optical scanning data; Based on the ultrasound scan data and the optical scan data, determining the correlation coefficient weight and distance weight between the ultrasound scan data and the optical scan data includes: acquiring the ultrasound scan data and the optical scan data at the same time; determining the correlation coefficient between the ultrasound scan data and the optical scan data at the same time, a first autocorrelation coefficient corresponding to the ultrasound scan data, and a second autocorrelation coefficient corresponding to the optical scan data; determining the correlation coefficient weight between ultrasound and optics based on the correlation coefficient, the first autocorrelation coefficient, and the second autocorrelation coefficient; determining distance weighting parameters for the ultrasound scan data and the optical scan data respectively based on the preset weighting method; weighting the ultrasound scan data according to the distance weighting parameters to obtain a first distance weight; weighting the optical scan data according to the distance weighting parameters to obtain a second distance weight; the distance weight includes: a first distance weight corresponding to the ultrasound scan data and a second distance weight corresponding to the optical scan data; Based on the correlation coefficient weight and the distance weight, the ultrasound scan data and the optical scan data are smoothed to generate a multimodal image for ultrasound and optical scans. This includes: multiplying the first correlation coefficient weight corresponding to the ultrasound scan data by the first distance weight to smooth the ultrasound scan data, obtaining smoothed ultrasound scan pixels; multiplying the second correlation coefficient weight corresponding to the optical scan data by the second distance weight to smooth the optical scan data, obtaining smoothed optical scan pixels; and combining the ultrasound scan pixels and the optical scan pixels to generate the multimodal image.

2. The method according to claim 1, characterized in that, The correlation coefficient weights include: a first correlation coefficient weight corresponding to the ultrasound scan data and a second correlation coefficient weight corresponding to the optical scan data. Determining the correlation coefficient weights for ultrasound and optical scans based on the correlation coefficients, the first autocorrelation coefficient, and the second autocorrelation coefficient includes: The correlation coefficient is compared with a first threshold, the first autocorrelation coefficient is compared with a second threshold, and the second autocorrelation coefficient is compared with a third threshold. Based on the comparison results of the correlation coefficient and the first threshold, and the comparison results of the first autocorrelation coefficient and the second threshold, the weight of the first correlation coefficient for the ultrasound scan data is determined; Based on the comparison between the correlation coefficient and the first threshold, and the comparison between the second autocorrelation coefficient and the third threshold, a second correlation coefficient weight is determined for the optical scanning data.

3. The method according to claim 2, characterized in that, The determination of the first correlation coefficient weight for the ultrasound scan data based on the comparison result of the correlation coefficient and the first threshold, and the comparison result of the first autocorrelation coefficient and the second threshold, includes: When the correlation coefficient exceeds the first threshold and the first autocorrelation coefficient exceeds the second threshold, the weight of the first correlation coefficient of the ultrasound scan data is determined to be a first preset value. When the correlation coefficient exceeds the first threshold and the first autocorrelation coefficient is less than the second threshold, the weight of the first correlation coefficient of the ultrasound scan data is determined to be the second preset value. When the correlation coefficient is less than the first threshold and the first autocorrelation coefficient exceeds the second threshold, the weight of the first correlation coefficient of the ultrasound scan data is determined to be a third preset value. When the correlation coefficient is less than the first threshold and the first autocorrelation coefficient is less than the second threshold, the weight of the first correlation coefficient of the ultrasound scan data is determined to be a fourth preset value. Wherein, the first preset value, the second preset value, the third preset value, and the fourth preset value are all different; and / or The determination of the second correlation coefficient weight for the optical scanning data based on the comparison result of the correlation coefficient and the first threshold, and the comparison result of the second autocorrelation coefficient and the third threshold, includes: When the correlation coefficient exceeds the first threshold and the second autocorrelation coefficient exceeds the third threshold, the weight of the second correlation coefficient of the optical scanning data is determined to be a fifth preset value. When the correlation coefficient exceeds the first threshold and the second autocorrelation coefficient is less than the third threshold, the weight of the second correlation coefficient of the optical scanning data is determined to be a sixth preset value. When the correlation coefficient is less than the first threshold and the second autocorrelation coefficient exceeds the third threshold, the weight of the second correlation coefficient of the optical scanning data is determined to be a seventh preset value. When the correlation coefficient is less than the first threshold and the second autocorrelation coefficient is less than the third threshold, the weight of the second correlation coefficient of the optical scanning data is determined to be an eighth preset value. The fifth preset value, the sixth preset value, the seventh preset value, and the eighth preset value are all different.

4. The method according to claim 1, characterized in that, The determination of the correlation coefficient generated at the same time, the first autocorrelation coefficient corresponding to the ultrasound scan data, and the second autocorrelation coefficient corresponding to the optical scan data further includes: The ultrasonic scanning data and the optical scanning data are respectively modulated to obtain the first modulus value corresponding to the ultrasonic scanning data and the second modulus value corresponding to the optical scanning data; The correlation coefficient is determined based on the first modulus and the second modulus; Based on the first modulus, the first autocorrelation coefficient is determined, and based on the second modulus, the second autocorrelation coefficient is determined.

5. An image smoothing processing apparatus, characterized in that, The device includes: The acquisition module is used to acquire ultrasound scan data and optical scan data; The weight determination module is used to determine the correlation coefficient weight and distance weight between the ultrasound scan data and the optical scan data based on the ultrasound scan data and the optical scan data. The generation module is used to smooth the ultrasound scan data and the optical scan data based on the correlation coefficient weight and the distance weight, and generate multimode images for ultrasound scan and optical coherence tomography. The weight determination module includes: a data acquisition unit for acquiring ultrasound scan data and optical scan data at the same time; a coefficient determination unit for determining the correlation coefficient between the ultrasound scan data and the optical scan data at the same time, a first autocorrelation coefficient corresponding to the ultrasound scan data, and a second autocorrelation coefficient corresponding to the optical scan data; a correlation coefficient weight determination unit for determining the correlation coefficient weight of ultrasound and optics based on the correlation coefficient, the first autocorrelation coefficient, and the second autocorrelation coefficient; and a distance weight determination unit for performing distance weighting on the ultrasound scan data and the optical scan data respectively based on a preset weighting method to obtain the distance weights of ultrasound and optics respectively. The distance weights include: a first distance weight corresponding to the ultrasound scan data and a second distance weight corresponding to the optical scan data; the distance weight determination unit includes: a weighting parameter determination subunit, used to determine distance weighting parameters for the ultrasound scan data and the optical scan data respectively based on the preset weighting method; a first weighting processing subunit, used to perform weighting processing on the ultrasound scan data according to the distance weighting parameters to obtain the first distance weight; and a second weighting processing subunit, used to perform weighting processing on the optical scan data according to the distance weighting parameters to obtain the second distance weight; The generation module includes: a first smoothing unit, used to multiply the first correlation coefficient weight corresponding to the ultrasound scan data with the first distance weight to smooth the ultrasound scan data and obtain smoothed ultrasound scan pixels; a second smoothing unit, used to multiply the second correlation coefficient weight corresponding to the optical scan data with the second distance weight to smooth the optical scan data and obtain smoothed optical scan pixels; and a combination unit, used to combine the ultrasound scan pixels and the optical scan pixels to generate the multimodal image.

6. An imaging device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the image smoothing method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the image smoothing method according to any one of claims 1 to 4.

Images