A method for removing artifacts from upper respiratory tract images

Abstract

The present invention relates to the technical field of image enhancement, and particularly relates to a method for removing artifacts from upper respiratory tract images. The present invention selects an optimal analysis region from the local regions of upper respiratory tract images in the respiratory cycle, and obtains the edge detail loss degree according to the length, the concentration degree of the direction distribution, and the richness of details of the edge lines in the corresponding optimal analysis region of the local region adjacent to each optimal analysis region, as well as the tightness of the edge lines in each optimal analysis region. Then, in combination with the richness of details of the optimal analysis region and the complexity of the respiratory pattern in the respiratory cycle, a fusion adaptation factor is obtained; the filtering intensity for filtering the optimal analysis region is adjusted by using the fusion adaptation factor to obtain an artifact correction region, and these regions are fused to obtain an enhanced upper respiratory tract image. The present invention performs targeted filtering adjustment on the optimal analysis region, enhancing the effect of removing respiratory artifacts in the artifact correction region.

Description

Technical Field

[0001] The present invention relates to the technical field of image enhancement, and particularly relates to a method for removing artifacts from upper respiratory tract images. Background Art

[0002] The upper respiratory tract image examination mainly scans the patient's upper respiratory tract through X-ray irradiation to understand the morphological abnormalities of the upper respiratory tract; the upper respiratory tract image is crucial in clinical diagnoses such as bronchial asthma, allergic rhinitis, sinusitis, and vocal cord diseases. During the scanning process, the patient's respiratory movement is the main factor causing serious artifacts in the upper respiratory tract image, affecting the quality of the upper respiratory tract image and thus reducing the accuracy of the upper respiratory tract examination.

[0003] In order to remove the artifacts generated by respiration in the upper respiratory tract image, the existing filtering methods usually directly perform filtering processing on the entire upper respiratory tract image with the same filtering intensity for the pixel points in the image; however, due to the complex structure and tissue morphological differences of the upper respiratory tract, the artifacts generated by respiratory movement in different parts of the upper respiratory tract are different. For example, the image of the airway shows a clear edge, but the image of the soft tissue lacks an obvious boundary, making the soft tissue more susceptible to respiratory artifacts, resulting in a poor removal effect of the unified filtering intensity on the respiratory artifacts of the upper respiratory tract image. Summary of the Invention

[0004] In order to solve the technical problem that the artifacts generated by respiratory movement in different parts of the upper respiratory tract are different, resulting in a poor removal effect of the unified filtering intensity on the respiratory artifacts of the upper respiratory tract image, the purpose of the present invention is to provide a method for removing artifacts from upper respiratory tract images, and the specific technical solution adopted is as follows:

[0005] The present invention proposes a method for removing artifacts from upper respiratory tract images, and the method includes:

[0006] Obtain the upper respiratory tract images at each moment during the patient's respiratory cycle, and the upper respiratory images include different local regions;

[0007] Match the local regions of the upper respiratory tract images during the respiratory cycle, and select the optimal analysis region for each local region from the matched local regions based on the richness of details in the local regions;

[0008] Perform edge detection on the optimal analysis region to obtain an edge line, and obtain the edge detail loss degree of each optimal analysis region according to the length, direction distribution concentration degree and richness of details of the edge line in the optimal analysis region corresponding to the local region adjacent to each optimal analysis region, as well as the tightness of the edge line in each optimal analysis region;

[0009] According to the complexity of the breathing pattern of the patient in the breathing cycle, the richness of details of each optimal analysis region and the edge detail loss degree, obtain the fusion matching factor of each optimal analysis region; use the fusion matching factor to adjust the filtering intensity for filtering the optimal analysis region, obtain the artifact correction region, and fuse all the artifact correction regions to obtain the upper airway enhanced image.

[0010] Further, the selection of the optimal analysis region for each local region includes:

[0011] Select the upper airway image at any moment as the reference image, perform region matching on each local region of the reference image and the local regions of the other upper airway images in the breathing cycle except the reference image, and form a region set by each local region of the reference image and its corresponding local regions matched in the other upper airway images;

[0012] Select any local region as the example region. After obtaining the average gray value of the pixel points in the example region, calculate the average of the absolute values of the differences between the average values of the example region and its adjacent local regions as the adjacent gray difference of the example region; perform corner detection on the example region, and obtain the richness of details of the example region according to the total number of corner points in the example region and the adjacent gray difference;

[0013] Select the local region corresponding to the maximum richness of details in each region set as the optimal analysis region for all local regions in each region set.

[0014] Further, the obtaining of the edge detail loss degree of each optimal analysis region includes:

[0015] Obtain the edge blurriness according to the length and direction distribution concentration degree of the edge lines in each optimal analysis region;

[0016] Calculate the product of the edge blurriness and the richness of details of the corresponding optimal analysis region of each local region adjacent to each optimal analysis region, and take the average of all the products as the edge detail annoyance degree of each optimal analysis region; obtain the edge compactness according to the position distribution of the pixel points on the edge line in each optimal analysis region;

[0017] Obtain the edge detail loss degree of each optimal analysis region according to the edge detail annoyance degree and the edge compactness. The edge detail annoyance degree and the edge detail loss degree are in a positive correlation relationship, and the edge compactness and the edge detail loss degree are in a negative correlation relationship.

[0018] Further, the obtaining of the edge blurriness includes:

[0019] Perform linear fitting on the pixel points on each edge line within the optimal analysis region to obtain the fitted line; based on the included angle between the fitted lines corresponding to each edge line within the optimal analysis region and each other edge line, and the difference in the mean slope of the pixel points on the corresponding two edge lines, obtain the local concentration degree of the corresponding two edge lines.

[0020] Denote the mean value of the local concentration degrees of each edge line within the optimal analysis region with respect to each other edge line as the direction concentration degree of each edge line; calculate the product of the number of pixel points on each edge line within the optimal analysis region and the direction concentration degree, and take the mean value of all the products as the edge blur degree of the optimal analysis region.

[0021] Furthermore, the edge tightness is equal to the mean value of the distances between every two pixel points on all the edge lines within each optimal analysis region.

[0022] Furthermore, the obtaining of the fusion matching factor for each optimal analysis region includes:

[0023] Obtain the respiration signal of the patient during the respiration cycle, and based on the respiration symmetry situation and the fluctuation situation of the respiration signal of the patient during the respiration cycle, obtain the pattern complexity.

[0024] Perform a negative correlation mapping on the product of the detail richness and the edge detail loss degree, and perform a normalization process on the product of the mapping result and the pattern complexity to obtain the fusion matching factor for each optimal analysis region.

[0025] Furthermore, the obtaining of the pattern complexity includes:

[0026] Obtain the upper and lower envelope lines of the respiration signal; calculate the cumulative sum of the absolute values of the differences in the slopes of all the data points of the respiration signal with respect to the corresponding data points on the two envelope lines, and denote it as the signal fluctuation degree; convert each envelope line into frequency domain data, and calculate the phase difference between the frequency domain data corresponding to the two envelope lines of the respiration signal, and denote it as the respiration asynchrony degree.

[0027] Take the product of the signal fluctuation degree and the respiration asynchrony degree as the pattern complexity.

[0028] Furthermore, the using of the fusion matching factor to adjust the filtering intensity for filtering the optimal analysis region to obtain the artifact correction region includes:

[0029] Use the sum value of the fusion matching factor and the constant 1 to weight the preset filtering standard deviation to obtain the adjusted standard deviation for each optimal analysis region; based on the adjusted standard deviation, perform filtering processing on each optimal analysis region using Gaussian filtering to obtain the artifact correction region corresponding to each optimal analysis region.

[0030] Furthermore, the edge line is a curve obtained by performing curve fitting on edge pixel points obtained by edge detection of the optimal analysis area.

[0031] Furthermore, the number of local areas of the upper respiratory tract image at different moments in the respiratory cycle is equal.

[0032] The present invention has the following beneficial effects:

[0033] First, by matching the upper respiratory tract images within the respiratory cycle to align the images, the local area with richer details in each part of the upper respiratory tract is selected as the optimal analysis area for the corresponding part, so as to merge the same structural information and reduce the artifacts caused by breathing. The artifact correction area after filtering the optimal analysis area is then fused to better remove the respiratory artifacts caused by changes in the respiratory cycle and avoid introducing too much noise.

[0034] Second aspect: The length and direction concentration of edge lines can measure the edge blur of the optimal analysis area. The local area adjacent to each optimal analysis area corresponds to the detail richness and edge blur of the optimal analysis area, which directly reflects the edge information loss of each optimal analysis area during the regional fusion process. Combined with the tightness of edge lines in the optimal analysis area where joints may exist, the edge detail loss is obtained. Considering that the patient's breathing pattern may vary from individual to individual, and different parts of the upper respiratory tract are subject to different artifacts caused by respiratory movement, different optimal analysis areas are filtered and adjusted in a targeted manner according to the complexity of the breathing pattern, the richness of details, and the loss of edge details. When removing artifacts accurately, the important details of different parts of the upper respiratory tract in the image can be better preserved, and the removal effect of respiratory artifacts in the optimal analysis area can be enhanced, thereby improving the quality of enhanced images of the upper respiratory tract. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A flowchart of the steps of a method for removing artifacts from upper respiratory tract images provided by one embodiment of the present invention;

[0037] Figure 2 A flow chart of a method for obtaining edge detail loss provided by an embodiment of the present invention;

[0038] Figure 3 Flowchart of a method for obtaining a fusion adaptation factor provided by an embodiment of the present invention;

[0039] Figure 4 System structure diagram of an artifact removal system for upper respiratory tract images provided by an embodiment of the present invention;

[0040] Figure 5 Schematic diagram of a computer device of an artifact removal device for upper respiratory tract images provided by an embodiment of the present invention. Detailed implementation manners

[0041] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details a method for removing artifacts from upper respiratory tract images proposed according to the present invention, including its specific implementation manners, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0043] The following specifically describes the specific solution of a method for removing artifacts from upper respiratory tract images provided by the present invention with reference to the accompanying drawings.

[0044] Embodiment 1:

[0045] The present invention proposes a method for removing artifacts from upper respiratory tract images. Please refer to Figure 1 , which shows a flowchart of the steps of a method for removing artifacts from upper respiratory tract images provided by an embodiment of the present invention. The method includes:

[0046] Step S1: Obtain upper respiratory tract images at each moment during the breathing cycle of the patient. The upper respiratory tract images include different local regions.

[0047] Scan the patient's upper respiratory tract using a computed tomography scanner to obtain upper respiratory tract images at each moment during a single breathing cycle. In order to more accurately analyze the detailed information of the upper respiratory tract, each moment's upper respiratory tract image is segmented into multiple local regions. During the image acquisition process, the position and shape of the upper respiratory tract change due to the patient's breathing, causing the image to become blurred or distorted, resulting in artifacts in the upper respiratory tract images.

[0048] It should be noted that, in order to avoid subsequent matching errors, the number of local regions in the upper respiratory tract images at all moments during the breathing cycle is equal.

[0049] In one implementation of the embodiment of the present invention, the data acquisition frequency of the computed tomography scanner is set to 30 frames per second.

[0050] In one implementation of the embodiment of the present invention, the superpixel segmentation algorithm is selected to divide the thoracic cavity image into local regions. In other embodiments, a trained neural network can also be used to divide the thoracic cavity image into multiple local regions.

[0051] Step S2: Match the local regions of the upper respiratory tract image within the respiratory cycle, and select the optimal analysis region for each local region from the matched local regions based on the richness of details in the local regions.

[0052] The upper respiratory tract of the patient, such as the nasal cavity and larynx, will undergo periodic displacement during breathing. Due to the complex structure and tissue morphological differences of the upper respiratory tract, different parts may undergo different degrees of deformation during breathing, such as the expansion and contraction of soft tissues, the narrowing and dilation of airways, etc., resulting in some parts of the upper respiratory tract image being blurred or distorted, forming artifacts. Traditional image processing methods cannot effectively eliminate the artifacts in the upper respiratory tract image, resulting in missing details or incorrect representations in some areas. By matching the upper respiratory tract images within the respiratory cycle to align the images, and selecting the local regions with relatively rich details in each part of the upper respiratory tract as the optimal analysis regions for the corresponding parts, the same structural information can be combined, and the artifacts caused by breathing can be reduced.

[0053] Step S3: Perform edge detection on the optimal analysis regions to obtain edge lines, and obtain the edge detail loss degree of each optimal analysis region according to the length, direction distribution concentration degree and detail richness of the edge lines in the optimal analysis region corresponding to the local regions adjacent to each optimal analysis region, as well as the tightness of the edge lines in each optimal analysis region.

[0054] After selecting the optimal analysis regions for all parts of the upper respiratory tract, filter them to remove respiratory artifacts, and perform regional fusion on the optimal analysis regions after artifact removal to obtain a complete upper respiratory tract image without artifacts. Since the upper respiratory tract presents different detailed information in different respiratory phases, the edges of the optimal analysis regions may become blurred or lost during the regional fusion process, affecting the clarity and accuracy of the final image. It is necessary to obtain the edge lines within the optimal analysis regions to analyze the degree of edge blurring. The upper respiratory tract undergoes regular deformation during breathing, making the artifacts generated during its respiratory movement exhibit a parallel arrangement characteristic, that is, the direction distribution is relatively concentrated, and the artifacts are relatively blurred; some structures of the upper respiratory tract show relatively detailed features in the image, the edges of the airway region are clear and continuous, but the soft tissue region is vulnerable to respiratory artifacts and noise due to strong homogeneity and lack of obvious boundaries, which will exacerbate edge blurring and make the edges of the soft tissue structures shorter. Therefore, the degree of edge blurring of the optimal analysis region can be measured by combining the length of the edge line and the degree of direction concentration.

[0055] Taking any one of the optimal analysis regions as the target region as an example, during the fusion process of the optimal analysis regions, when the optimal analysis regions adjacent to the target region have rich details but blurred boundaries, these optimal analysis regions may affect the recognition of the boundary details of the target region, resulting in an unclear transition between the boundary of the target region and its adjacent optimal analysis regions, and the greater the loss of edge information of the target region during the regional fusion process, thereby reducing the accuracy of the fusion result. The structure of the upper respiratory tract is complex, and the region may contain small joints. When the joint is located at the edge of the soft tissue, the edge effect of the soft tissue may cause the boundary details of the joint to be lost or blurred, affecting the accurate recognition of the regional structure; due to the small and complex structure of the airway joint, its edge usually presents more curvatures and variations, making the edge of the joint closer than the normal region, thus resulting in a greater loss of edge information in the joint region during the regional fusion process. Therefore, by comprehensively considering the degree of edge blurring and the richness of details of the optimal analysis regions corresponding to the local regions adjacent to the target region, as well as the tightness of the edge lines within the target region, analyze the degree of edge information loss of the target region during the regional fusion process to obtain the edge detail loss degree; and the greater the above factors, the greater the loss of edge information of the target region during the regional fusion process.

[0056] It should be noted that the edge line is a curve obtained by curve fitting of the edge pixel points detected for the optimal analysis region, and the least squares method is selected for curve fitting. Part of the edge of the optimal analysis region is the edge of its adjacent local region.

[0057] In an implementation manner of the embodiment of the present invention, the Canny operator is selected for edge detection, and the Sobel operator and the Laplacian operator can also be selected, etc.

[0058] Step S4: According to the complexity of the patient's breathing pattern in the breathing cycle, the degree of detail richness of each optimal analysis region, and the edge detail loss degree, obtain the fusion adaptation factor for each optimal analysis region; use the fusion adaptation factor to adjust the filtering intensity for filtering the optimal analysis region to obtain an artifact correction region, and fuse all the artifact correction regions to obtain an upper respiratory tract enhanced image.

[0059] Since there are differences in the breathing patterns of different patients, such as frequency, depth, and rhythm, the impact of the patient's breathing on the upper respiratory tract is different, that is, there are significant differences in the artifacts in the upper respiratory tract images of different patients. A complex breathing pattern will exacerbate the movement of the patient's upper respiratory tract during image acquisition, generating obvious respiratory artifacts. To suppress high-frequency artifacts, a larger filtering intensity needs to be set; when the edge information loss of the optimal analysis region is greater and it has more abundant detailed information during the region fusion process, the high-detail region usually contains more valuable information, and strong filtering will cause detail loss, especially at the edge position of the region. To retain the local detail features of the upper respiratory tract structure, a smaller filtering intensity needs to be set so that the optimal analysis region can maximize the transmission of effective details while suppressing noise during the region fusion process, thereby ensuring the accuracy of region fusion. Therefore, the filtering intensity for filtering the optimal analysis region is adjusted by comprehensively considering the above three factors to obtain the fusion adaptation factor. The larger the fusion adaptation factor of the optimal analysis region, the greater the required filtering intensity. Filtering using the fusion adaptation factor can better retain the important details of different parts of the upper respiratory tract in the image when accurately removing artifacts, enhance the removal effect of respiratory artifacts in the artifact correction region, and thus achieve the enhancement processing of the upper respiratory tract image. Each breathing cycle corresponds to an upper respiratory tract enhanced image.

[0060] In an embodiment of the present invention, an optical flow algorithm such as FlowNet is used to calculate the displacement field of the upper respiratory tract image during the breathing cycle, generate a continuous deformation field by interpolating the timestamps of the optimal analysis regions, map each optimal analysis region to the reference coordinate system through the deformation field, and then use Poisson fusion to fuse the optimal analysis regions to obtain an upper respiratory tract enhanced image. Among them, optical flow registration, deformation field interpolation, and image fusion are all well-known technologies to those skilled in the art and will not be elaborated here.

[0061] In another embodiment of the present invention, a graph model is constructed with the optimal analysis regions as nodes and the adjacency relationship between regions as edges. The sum of the error term of optical flow feature alignment and the deformation smoothing term determined by the Demons algorithm is used as the optimization objective function, and graph cut or the Gauss-Newton method is used to solve the optimal stitching. The optimal analysis regions are stitched and fused according to the optimal stitching to obtain an upper respiratory tract enhanced image.

[0062] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for selecting the optimal analysis region includes: taking the upper respiratory tract image at any moment as the reference image, performing region matching on each local region of the reference image with the local regions of the upper respiratory tract images other than the reference image within the respiratory cycle, and forming a region set by each local region of the reference image and its corresponding matched local regions in the other upper respiratory tract images; taking any local region as the example region, after obtaining the average gray value of the pixel points in the example region, calculating the average of the absolute values of the differences between the average of the example region and the averages of its adjacent local regions as the adjacent gray difference of the example region; performing corner detection on the example region, and obtaining the richness of details of the example region according to the total number of corner points in the example region and the adjacent gray difference; and selecting the local region corresponding to the maximum richness of details from each region set as the optimal analysis region for all local regions within each region set.

[0063] It should be noted that the gray difference between the example region and its adjacent local regions reflects the possibility of high-frequency information existing in the example region, and the total number of corner points in the example region presents the degree of tortuosity of the edge structure within the example region. The larger both are, the richer the details within the example region are. Therefore, both the total number of corner points in the example region and the adjacent gray difference are positively correlated with the richness of details. In the embodiments of the present invention, the product of the total number of corner points in the example region and the adjacent gray difference is used as the richness of details. To ensure the matching effect of the local regions of the reference image and the other upper respiratory tract images, the reference image should be selected as the upper respiratory tract image without respiratory artifacts. The method for obtaining the richness of details of all local regions and the example region is the same.

[0064] In one implementation manner of the embodiments of the present invention, a motion compensation algorithm is used to perform region matching on each local region of the reference image with the local regions of the upper respiratory tract images other than the reference image within the respiratory cycle.

[0065] In one implementation method of the embodiments of the present invention, the Shi-Tomasi corner detection algorithm is used for corner detection.

[0066] Preferably, in some possible implementation manners of the embodiments of the present invention, for the method for obtaining the edge detail loss degree, please refer to Figure 2 , which shows a flowchart of a method for obtaining the edge detail loss degree provided by an embodiment of the present invention. The method includes:

[0067] Step S310: Obtain the edge blurriness according to the length and direction distribution concentration degree of the edge lines within each optimal analysis region.

[0068] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for obtaining the edge blur degree includes: performing linear fitting on the pixel points on each edge line in the optimal analysis region to obtain a fitting line; obtaining the local concentration degree of the corresponding two edge lines according to the included angle between the fitting lines corresponding to each edge line in the optimal analysis region and the remaining edge lines, and the difference between the average slopes of the pixel points on the corresponding two edge lines; taking the average of the local concentration degrees of each edge line in the optimal analysis region and the remaining edge lines as the direction concentration degree of each edge line; calculating the product of the number of pixel points on each edge line in the optimal analysis region and the direction concentration degree, and taking the average of all products as the edge blur degree of the optimal analysis region.

[0069] It should be noted that the greater the possibility that the optimal analysis region with the known edge lines being more parallel and shorter in length is a region with blurred boundaries or a soft tissue region, the greater the edge blur degree. When the included angle between each edge line and the fitting lines corresponding to the remaining edge lines, and the difference between the average slopes of the pixel points on the edge lines are smaller, it indicates that the overall directions of the corresponding two edge lines are more parallel, the edge contours are closer, the two edge lines are more parallel, and the local concentration degree is greater. Therefore, both the included angle between the fitting lines corresponding to the two edge lines and the difference between the average slopes of the pixel points on the edge lines are negatively correlated with the local concentration degree. In the embodiments of the present invention, the product of the included angle between each edge line and the difference between the average slopes of the pixel points on the corresponding two edge lines is negatively correlated and mapped to obtain the local concentration degree. By analyzing the parallelism of each edge line and the remaining edge points through the average of the local concentration degrees of each edge line and the remaining edge lines, the possibility of each edge line being affected by respiratory artifacts is measured. The edge line with a greater possibility of artifacts is more blurred. The length of the edge line is measured by the number of pixel points on the edge line. The shorter the edge line, the greater the possibility that it is the edge of the soft tissue region, and the more blurred the edge line with a greater possibility of soft tissue. Considering the blur degrees of all edge lines in the optimal analysis region, the edge blur degree of the optimal analysis region is obtained.

[0070] In an implementation method of the embodiments of the present invention, the least squares method is used to perform linear fitting on the pixel points on the edge line. The polynomial fitting method and the support vector machine and other methods can also be used for linear fitting.

[0071] In an implementation method of the embodiments of the present invention, the opposite number of the above product is used as the exponent of the exponential function with the natural constant as the base to implement the negative correlation mapping process of the product.

[0072] Step S320: Calculate the product of the edge blurriness and detail richness of the optimal analysis region corresponding to each local region adjacent to each optimal analysis region, and take the mean of all products as the edge detail annoyance degree of each optimal analysis region; according to the position distribution of the pixel points on the edge line within each optimal analysis region, obtain the edge tightness.

[0073] Preferably, in some possible implementation manners of the embodiments of the present invention, the edge tightness is equal to the mean distance between every two pixel points on all the edge lines within each optimal analysis region.

[0074] It should be noted that the closer the position distribution of the edge line within the optimal analysis region with a smaller mean distance is; the edge detail annoyance degree presents the degree of interference of the edge information of each optimal analysis region by the local region corresponding to the optimal analysis region during the region fusion process.

[0075] Step S330: Obtain the edge detail loss degree of each optimal analysis region according to the edge detail annoyance degree and the edge tightness.

[0076] It should be noted that it is known that the edge information loss is relatively large in the region corresponding to the airway joint structure during the region fusion process. The greater the possibility that the optimal analysis region with a smaller edge tightness has an airway joint structure, the greater the edge information loss during the region fusion process. The greater the edge detail annoyance degree of the optimal analysis region, the greater the edge information loss caused by the influence of its surrounding local regions during the region fusion process. Therefore, there is a positive correlation between the edge detail annoyance degree and the edge detail loss degree, and a negative correlation between the edge tightness and the edge detail loss degree. In the embodiments of the present invention, the ratio of the edge detail annoyance degree to the edge tightness of each optimal analysis region is used as the edge detail loss degree.

[0077] Preferably, in some possible implementation manners of the embodiments of the present invention, for the method of obtaining the fusion adaptation factor, please refer to Figure 3 which shows a flowchart of a method for obtaining a fusion adaptation factor provided by an embodiment of the present invention. The method includes:

[0078] Step S410: Obtain the respiratory signal of the patient during the respiratory cycle, and obtain the pattern complexity according to the respiratory symmetry and the fluctuation of the respiratory signal of the patient during the respiratory cycle.

[0079] Use an abdominal belt pressure sensor to collect the respiratory signal of the patient during the respiratory cycle. The horizontal axis represents time, and the vertical axis represents the pressure generated by the abdomen during breathing. In one implementation manner of the embodiments of the present invention, the data acquisition frequency of the pressure sensor is set to 20 Hz.

[0080] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for obtaining the pattern complexity includes: obtaining the upper and lower envelope lines of the respiratory signal; calculating the sum of the absolute values of the differences in the slopes of the corresponding data points of all the data points of the respiratory signal on the two envelope lines, which is denoted as the signal fluctuation degree; converting each envelope line into frequency-domain data, and calculating the phase difference between the frequency-domain data corresponding to the two envelope lines of the respiratory signal, which is denoted as the respiratory asynchrony degree; and taking the product of the signal fluctuation degree and the respiratory asynchrony degree as the pattern complexity.

[0081] It should be noted that in a normal respiratory cycle, the rhythm of inhalation and exhalation is relatively consistent, indicating that the acceleration and deceleration processes of inhalation and exhalation are coordinated, keeping the depth fluctuation of the respiratory signal within a relatively constant range. The greater the depth fluctuation of the respiratory signal, the more diverse the respiratory changes, and the more complex the respiratory pattern becomes. The respiratory cycle includes the inhalation and exhalation phases, and the two phases are relatively balanced in normal breathing, that is, the ratio of inhalation to exhalation is approximately equal; in the case where the durations of inhalation and exhalation are asymmetric, the respiratory changes will be more diverse, and there will be more irregular rhythm phenomena, making the respiratory pattern more complex. The signal fluctuation degree and the respiratory asynchrony degree respectively reflect the fluctuation situation of the respiratory signal and the respiratory symmetry of the patient in the respiratory cycle. When the signal fluctuation degree and the respiratory asynchrony degree are greater, the depth fluctuation of the respiratory signal is more obvious, the durations of inhalation and exhalation are more asymmetric, the respiratory pattern of the patient in the respiratory cycle is more complex, and the pattern complexity is greater.

[0082] It should be noted that the phase difference between the frequency-domain data corresponding to the two envelope lines of the respiratory signal is equal to the average of the phase differences of the components corresponding to all the equal frequencies of the two envelope lines. The time of each data point of the respiratory signal is the same as the time of the corresponding data point on the envelope line.

[0083] In an implementation method of the embodiments of the present invention, the moving extreme value method is selected to extract the envelope line of the respiratory signal, or the Hilbert transform method can also be selected to ensure that the number of data points of the respiratory signal is equal to the number of data points of each of its envelope lines.

[0084] In an implementation method of the embodiments of the present invention, the Fourier transform is selected for frequency-domain conversion.

[0085] Step S420: Perform a negative correlation mapping on the product of the detail richness and the edge detail loss degree, and perform a normalization process on the product of the mapping result and the pattern complexity to obtain the fusion matching factor of each optimal analysis region.

[0086] In a specific implementation manner of the embodiments of the present invention, the fusion matching factor is expressed by the formula:

[0087]

[0088] In the formula, is the fusion matching factor for the a-th optimal analysis region; is the pattern complexity; is the detail richness of the a-th optimal analysis region; is the edge detail loss degree of the a-th optimal analysis region; Norm is the normalization function; exp is the exponential function with the natural constant as the base. In this implementation, the negative correlation mapping is achieved through the exp function. It should be noted that the more complex the breathing pattern, i.e., the larger W, the more obvious the breathing artifacts will be, and the greater the filtering intensity is required to suppress the artifacts. Then is larger; the edge detail loss degree is larger and the detail richness is larger, the optimal analysis region contains more valuable information. In order to retain the local detail features of the upper respiratory tract structure, a smaller filtering intensity is required. Then is smaller.

[0089] Preferably, in some possible implementation manners of the embodiment of the present invention, the method for obtaining the artifact correction region includes: weighting the preset filtering standard deviation by the sum value of the fusion matching factor and the constant 1 to obtain the adjusted standard deviation of each optimal analysis region; based on the adjusted standard deviation, performing filtering processing on each optimal analysis region by using Gaussian filtering to obtain the artifact correction region corresponding to each optimal analysis region. Among them, Gaussian filtering is a well-known technology to those skilled in the art and will not be elaborated here. It should be noted that the larger the fusion matching factor, the greater the required filtering intensity, and the filtering intensity is enhanced by increasing the filtering standard deviation.

[0090] In an implementation method of the embodiment of the present invention, the preset filtering standard deviation is set to 2.

[0091] In other embodiments of the present invention, weighting the preset size of the filtering window by the sum value of the fusion matching factor and the constant 1 to obtain the adjusted filtering window of each optimal analysis region; based on the adjusted filtering window, performing filtering processing on each optimal analysis region by using Gaussian filtering to obtain the artifact correction region corresponding to each optimal analysis region.

[0092] In an implementation method of the embodiment of the present invention, the preset size is set to 13.

[0093] So far, the present invention is completed.

[0094] Embodiment 2:

[0095] The present invention provides an artifact removal system for upper respiratory tract images. Please refer to Figure 4 , which shows the system structure diagram of an artifact removal system for upper respiratory tract images provided by an embodiment of the present invention. The system includes:

[0096] A data acquisition module 510 is configured to acquire upper respiratory tract images at each moment within the respiratory cycle of a patient. The upper respiratory tract images include different local regions.

[0097] A region screening module 520 is configured to match the local regions of the upper respiratory tract images within the respiratory cycle, and select an optimal analysis region for each local region from the matched local regions based on the degree of detail richness of the local regions.

[0098] A detail loss analysis module 530 is configured to perform edge detection on the optimal analysis regions to obtain edge lines. According to the length, direction distribution concentration degree and detail richness of the edge lines in the optimal analysis regions corresponding to the local regions adjacent to each optimal analysis region, and the tightness of the edge lines in each optimal analysis region, the edge detail loss degree of each optimal analysis region is obtained.

[0099] An impact enhancement module 540 is configured to obtain a fusion adaptation factor for each optimal analysis region according to the complexity of the breathing pattern of the patient within the respiratory cycle, the detail richness and the edge detail loss degree of each optimal analysis region; use the fusion adaptation factor to adjust the filtering intensity for filtering the optimal analysis regions to obtain artifact correction regions, and fuse all the artifact correction regions to obtain an enhanced upper respiratory tract image.

[0100] It should be noted that: for the device provided in the above embodiment, only the division of the above functional modules is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, an artifact removal system for upper respiratory tract images and an artifact removal method embodiment for upper respiratory tract images provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be elaborated here.

[0101] Embodiment 3:

[0102] Figure 5 This is a schematic diagram of a computer device of an artifact removal device for upper respiratory tract images provided by an embodiment of the present invention. Exemplarily, as Figure 5 shown, the computer device includes: a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602. When the processor 602 executes the computer program 603, the computer device can execute any one of the artifact removal methods for upper respiratory tract images introduced above.

[0103] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor. Among them, executable program code is stored in the memory, and the processor is used to call and execute the executable program code to execute an artifact removal method for upper respiratory tract images provided by the embodiment of the present application.

[0104] This embodiment can divide the functions of the device according to the above method examples. For example, it can correspond to each functional module, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is illustrative, only a logical function division, and there may be other division methods in actual implementation.

[0105] It should be understood that the device provided in this embodiment is used to execute the above-mentioned artifact removal method for upper respiratory tract images, so the same effect as the above implementation method can be achieved.

[0106] In the case of adopting an integrated unit, the device may include a processing module and a storage module. Among them, when the device is applied to a device, the processing module can be used to control and manage the actions of the device. The storage module can be used to support the device to execute mutual program code, etc.

[0107] Among them, the processing module can be a processor or a controller, which can implement or execute various exemplary logical blocks, modules, and circuits included in the disclosure of the present application. The processor can also be a combination that realizes computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module can be a memory.

[0108] It should be noted that the above order of the embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0109] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0110] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for removing artifacts from upper respiratory tract images, characterized in that, The method includes: Obtaining upper respiratory tract images at each moment within the respiratory cycle of a patient, where the upper respiratory tract images include different local regions; Matching the local regions of the upper respiratory tract images within the respiratory cycle, and selecting the optimal analysis region for each local region from the matched local regions based on the richness of details in the local regions; Performing edge detection on the optimal analysis regions to obtain edge lines, and obtaining the edge detail loss degree of each optimal analysis region according to the length, direction distribution concentration degree and richness of details of the edge lines in the optimal analysis region corresponding to the local regions adjacent to each optimal analysis region, as well as the tightness of the edge lines within each optimal analysis region; According to the pattern complexity of the breathing pattern of the patient within the respiratory cycle, the richness of details of each optimal analysis region and the edge detail loss degree, obtaining the fusion adaptation factor for each optimal analysis region; using the fusion adaptation factor to adjust the filtering intensity for filtering the optimal analysis region to obtain an artifact correction region, and fusing all the artifact correction regions to obtain an enhanced upper respiratory tract image; The method for obtaining the richness of details of the local region includes: Optionally selecting a local region as an example region, after obtaining the mean gray value of the pixel points within the example region, calculating the mean of the absolute values of the differences between the mean of the example region and the means of its adjacent local regions as the adjacent gray difference of the example region; performing corner detection on the example region, and obtaining the richness of details of the example region according to the total number of corner points within the example region and the adjacent gray difference; Obtaining the edge detail loss degree of each optimal analysis region includes: Obtaining the edge blur degree according to the length and direction distribution concentration degree of the edge lines within each optimal analysis region; Calculating the product of the edge blur degree and the richness of details of each local region corresponding to the optimal analysis region adjacent to each optimal analysis region, and taking the mean of all the products as the edge detail annoyance degree of each optimal analysis region; obtaining the edge tightness according to the position distribution of the pixel points on the edge lines within each optimal analysis region; Obtaining the edge detail loss degree of each optimal analysis region according to the edge detail annoyance degree and the edge tightness; Obtaining the fusion adaptation factor for each optimal analysis region includes: Obtaining the respiratory signal of the patient within the respiratory cycle, and obtaining the pattern complexity according to the respiratory symmetry situation and the fluctuation situation of the respiratory signal of the patient within the respiratory cycle; Performing a negative correlation mapping on the product of the richness of details and the edge detail loss degree, and performing a normalization process on the product of the mapping result and the pattern complexity to obtain the fusion adaptation factor for each optimal analysis region.

2. The artifact removal method for upper respiratory tract images according to claim 1, wherein The selection of the optimal analysis region for each local region includes: Optionally selecting the upper respiratory tract image at a certain moment as a reference image, performing region matching on each local region of the reference image and the local regions of the remaining upper respiratory tract images within the respiratory cycle except the reference image, and forming a region set by each local region of the reference image and the local regions matched by it in the remaining upper respiratory tract images respectively; Select the local region corresponding to the maximum of the richness of details from each region set as the optimal analysis region for all local regions within each region set.

3. The artifact removal method for upper respiratory tract images according to claim 1, characterized in that, The obtaining of the edge fuzziness includes: Performing linear fitting on the pixel points on each edge line within the optimal analysis region to obtain a fitting line; obtaining the local concentration degree of the corresponding two edge lines according to the included angle between the fitting lines corresponding to each edge line within the optimal analysis region and each other edge line, and the difference in the average slope of the pixel points on the corresponding two edge lines. Denote the average value of the local concentration degrees of each edge line within the optimal analysis region with each other edge line as the direction concentration degree of each edge line; calculate the product of the number of pixel points on each edge line within the optimal analysis region and the direction concentration degree, and take the average value of all products as the edge fuzziness of the optimal analysis region.

4. The artifact removal method for upper respiratory tract images according to claim 1, wherein The edge tightness is equal to the average value of the distances between every two pixel points on all edge lines within each optimal analysis region.

5. A method for removing artifacts in upper respiratory tract images according to claim 1, characterized in that, The obtaining of the pattern complexity includes: Obtaining the upper and lower envelope lines of the respiratory signal; calculating the cumulative sum of the absolute values of the differences in the slopes of all data points of the respiratory signal corresponding to the data points on the two envelope lines, and denoting it as the signal fluctuation degree; converting each envelope line into frequency domain data, and calculating the phase difference between the frequency domain data corresponding to the two envelope lines of the respiratory signal, and denoting it as the respiratory asynchrony degree. Take the product of the signal fluctuation degree and the respiratory asynchrony degree as the pattern complexity.

6. The artifact removal method for upper respiratory tract images according to claim 1, characterized in that, The using of the fusion matching factor to adjust the filtering intensity for filtering the optimal analysis region to obtain an artifact correction region includes: Weight the preset filtering standard deviation with the sum value of the fusion matching factor and the constant 1 to obtain the adjusted standard deviation for each optimal analysis region; based on the adjusted standard deviation, perform filtering processing on each optimal analysis region using Gaussian filtering to obtain the artifact correction region corresponding to each optimal analysis region.

7. A method for removing artifacts from upper respiratory tract images according to claim 1, characterized in that, The edge line is a curve obtained by performing curve fitting on the edge pixel points obtained by performing edge detection on the optimal analysis region.

8. A method for removing artifacts from upper respiratory tract images according to claim 1, characterized in that, The number of local regions of the upper respiratory tract images at different moments within the respiratory cycle is equal.

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