Image processing method and device, computed tomography equipment and storage medium

Abstract

The invention discloses an image processing method and device, computed tomography equipment and a storage medium, the image processing method is applied to the computed tomography equipment, and the image processing method comprises the following steps: determining a first image and a second image, adjacent to each other, of a target image in a plurality of images; determining a rotated first image and a rotated second image corresponding to the target image according to the first image and the second image; determining at least one abnormal pixel area in the target image; determining a pixel value of a central point of each abnormal pixel region according to a pixel value of each pixel point in each abnormal pixel region, a first abnormal region corresponding to each abnormal pixel region on the rotated first image and a second abnormal region corresponding to each abnormal pixel region on the rotated second image; and determining a final target image. Therefore, according to the first image and the second image which are adjacent to the target image, the pixel values of the pixel points, which are not consistent with the actual situation, on the target image are corrected, so that the target image with higher precision is obtained.

Description

Technical Field

[0001] The present application relates to the technical field of industrial measurement, and more specifically, to an image processing method, device, computer tomography equipment and storage medium. Background Art

[0002] Industrial Computerized Tomography (CT) is a technology that generates a three-dimensional image of the internal structure of a target object by scanning it with X-rays. Industrial CT is widely used in non-destructive testing, material analysis, quality control and other fields.

[0003] However, during the scanning process of the target object using industrial computed tomography technology, the imaging quality may be affected by various interferences (for example, noise interference, multipath interference or scattering effects, etc.), resulting in a decrease in the clarity of the three-dimensional image and an increase in artifacts, thereby affecting the final three-dimensional reconstruction result of the target object, thereby affecting the imaging accuracy of industrial computed tomography technology. Summary of the invention

[0004] In view of the above problems, the present application proposes an image processing method, device, computer tomography equipment and storage medium, which can effectively improve the imaging accuracy of industrial computer tomography technology.

[0005] In a first aspect, an embodiment of the present application provides an image processing method, which is applied to a computer tomography device, wherein the computer tomography device is used to rotate about a preset rotation axis to continuously acquire multiple images of the object to be detected at different angles around the object to be detected, and the image processing method includes: determining a first image and a second image adjacent to a target image in the multiple images; the target image is any one of the multiple images; rotating the first image and the second image about the preset rotation axis respectively to obtain a rotated first image and a second image corresponding to the target image; determining at least one abnormal pixel area in the target image according to the rotated first image and the rotated second image; determining the pixel value of the center point of each abnormal pixel area according to the pixel value of each pixel point in each abnormal pixel area, the first abnormal area corresponding to each abnormal pixel area on the rotated first image, and the second abnormal area corresponding to each abnormal pixel area on the rotated second image; determining the final target image according to the pixel value of the center point of each abnormal pixel area in at least one abnormal pixel area.

[0006] In a second aspect, an embodiment of the present application further provides an image processing device, which is applied to a computer tomography device, wherein the computer tomography device is used to rotate about a preset rotation axis to continuously acquire multiple images of the object to be detected at different angles around the object to be detected, and the image processing device includes: a first determination module, which is used to determine the first image and the second image adjacent to the target image in the multiple images; the target image is any one of the multiple images; a rotation module, which is used to rotate the first image and the second image respectively about the preset rotation axis to obtain the rotated first image and the second image corresponding to the target image; a second determination module, which is used to determine at least one abnormal pixel area in the target image according to the rotated first image and the rotated second image; a first execution module, which is used to determine the pixel value of the center point of each abnormal pixel area according to the pixel value of each pixel point in each abnormal pixel area, the first abnormal area corresponding to each abnormal pixel area on the rotated first image, and the second abnormal area corresponding to each abnormal pixel area on the rotated second image; a second execution module, which is used to determine the final target image according to the pixel value of the center point of each abnormal pixel area in at least one abnormal pixel area.

[0007] In a third aspect, an embodiment of the present application also provides a computed tomography device, comprising a processor, a memory, and one or more applications; the one or more applications are stored in the memory and configured to be executed by the processor to implement the above-mentioned image processing method.

[0008] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, in which a program code is stored, wherein the above-mentioned image processing method is executed when the program code is executed by a processor.

[0009] The technical solution provided in the present application is applied to a computer tomography device, which is used to rotate about a preset rotation axis to continuously obtain multiple images of the object to be detected at different angles around the object to be detected. The image processing method includes: determining a first image and a second image adjacent to a target image in the multiple images; the target image is any one of the multiple images; rotating the first image and the second image about the preset rotation axis respectively to obtain a rotated first image and a second image corresponding to the target image; determining at least one abnormal pixel area in the target image according to the rotated first image and the rotated second image; determining the pixel value of the center point of each abnormal pixel area according to the pixel value of each pixel point in each abnormal pixel area, the first abnormal area corresponding to each abnormal pixel area on the rotated first image, and the second abnormal area corresponding to each abnormal pixel area on the rotated second image; and determining the final target image according to the pixel value of the center point of each abnormal pixel area in at least one abnormal pixel area. Therefore, according to the first image and the second image adjacent to the target image, the pixel values ​​of the pixel points on the target image that do not conform to the actual situation are corrected so that the pixel values ​​of the pixel points on the target image are closer to the actual situation, thereby obtaining a target image with higher accuracy, so as to improve the imaging accuracy of industrial computed tomography technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments and drawings obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0011] Figure 1 A flow chart of an image processing method provided in an embodiment of the present application is shown.

[0012] Figure 2 A schematic diagram of the structure of an image provided in an embodiment of the present application is shown.

[0013] Figure 3 A schematic structural diagram of an abnormal pixel region provided in an embodiment of the present application is shown.

[0014] Figure 4 A schematic structural diagram of an image processing device provided in an embodiment of the present application is shown.

[0015] Figure 5 A schematic structural diagram of a computer tomography device provided in an embodiment of the present application is shown.

[0016] Figure 6 A schematic diagram of the structure of a computer-readable storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0018] In the following description, reference is made to "some embodiments", which describes a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. In the following description, reference is made to the term "multiple" to mean at least two.

[0019] In the following description, the terms "first\second" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0021] Industrial Computerized Tomography (CT) is a technology that generates a three-dimensional image of the internal structure of a target object by scanning it with X-rays. Industrial CT is widely used in non-destructive testing, material analysis, quality control and other fields.

[0022] However, during the scanning process of the target object using industrial computed tomography technology, the imaging quality may be affected by various interferences (for example, noise interference, multipath interference or scattering effects, etc.), resulting in a decrease in the clarity of the three-dimensional image and an increase in artifacts, thereby affecting the final three-dimensional reconstruction result of the target object, thereby affecting the imaging accuracy of industrial computed tomography technology.

[0023] Noise interference refers to random noise introduced during the image acquisition and transmission of the target object. This random noise mainly comes from the electronic noise of the equipment itself (for example, the detector), the instability of the radiation source, and the electromagnetic interference in the environment. Noise interference will reduce the contrast of the image and make important structural details difficult to identify.

[0024] Multipath interference means that when rays pass through an object, part of the signal will reach the detector through multiple paths due to reflection, refraction, etc. These multipath signals are superimposed on the main path signal, resulting in artifacts and blur in the image.

[0025] The scattering effect is caused by the interaction between the rays and the atoms inside the object. Scattering will cause the rays to deviate from the original path during transmission, resulting in the weakening or deviation of the received signal, which in turn causes blurring and distortion of the image. In other words, noise interference, multipath interference and scattering effects will affect the imaging accuracy of industrial computed tomography technology.

[0026] In related technologies, filtering technology is used to sort local areas of an image and replace the central pixel value with an intermediate value, which can effectively remove isolated noise points while retaining edge details. Gaussian filtering can also be used to perform convolution operations on the image to reduce high-frequency noise. Deep learning methods can also be used to obtain high-quality denoised images. For example, convolutional neural networks (CNN). Specifically, by first training with a large number of noisy and denoised images, CNN can automatically learn the characteristics of noise and effectively remove noise. Its generative adversarial network consists of a generator and a discriminator. The generator is responsible for generating denoised images, and the discriminator judges the authenticity of the image. Through adversarial training between the two, high-quality denoised images can be generated.

[0027] By adopting the above method, the influence of noise interference on the imaging of industrial computed tomography technology is reduced, thereby improving the imaging effect of industrial computed tomography technology. However, while reducing image noise, the above method will also lead to the loss of some details of the image, low adaptability, high sample requirements or high equipment performance requirements. For example, details are easily lost in the edge area of ​​the image. For another example, under different image and noise conditions, parameters need to be manually adjusted to better reduce image noise, which makes it difficult for the above method to automatically adapt to complex noise patterns. For another example, a large amount of labeled data is required for training. If the training data is insufficient or unrepresentative, the denoising effect may be significantly reduced. For another example, due to the high computational complexity of the above method, especially when processing high-resolution CT images, this may lead to the need for high-performance computing resources in practical applications, increasing the complexity and cost of the system.

[0028] In other related technologies, multipath interference suppression can use blind equalization technology, such as constant modulus algorithm and particle filtering. The constant modulus algorithm does not rely on prior signal information. By adjusting the coefficients of the filter, the modulus value of the filter's output signal tends to be constant, thereby suppressing multipath interference. Among them, the minimum mean square error algorithm can be used to minimize the mean square error between the received signal and the expected signal, and the multipath effect can be gradually eliminated by continuously adjusting the parameters of the filter. Particle filtering is suitable for nonlinear and non-Gaussian systems. Particle filtering generates a series of particles and updates the weights of the particles according to the observed signals, thereby estimating the most likely true signal to reduce the impact of multipath interference.

[0029] Although blind equalization algorithms (e.g., constant modulus algorithm (CMA)) and least mean square error (LMS) algorithms perform well in suppressing multipath interference, they may face convergence and stability issues in practical applications. These algorithms are sensitive to the initial conditions of the signal, which may lead to unstable results. In addition, although particle filtering performs well in dealing with complex multipath interference, it has the problem of particle degradation. As the filtering process proceeds, the weights of some particles will gradually decrease or even disappear, resulting in an insufficient number of effective particles, affecting the filtering accuracy, and thus affecting the imaging accuracy of industrial computed tomography technology.

[0030] In other related technologies, scattering effect suppression can be achieved through scattering compensation or compensation based on the Bayesian method. Specifically, Monte Carlo simulation corrects the distortion caused by scattering in the image by simulating the propagation path of rays inside the object, including scattering and absorption processes. The compensation based on the Bayesian method calculates the most likely scattering effect and corrects it based on prior information and observation data. In addition, the scattering effect on the imaging accuracy of industrial computed tomography technology can be reduced by multi-view imaging and image fusion. Through multi-view imaging and image fusion, CT images of the target object are obtained from multiple viewpoints, and image fusion technology is used to reduce the impact of scattering effects, thereby effectively enhancing the contrast and clarity of the image.

[0031] However, although the above method can provide high-precision compensation effects, its computational complexity is extremely high, especially when processing large 3D data, which makes real-time processing impractical. Moreover, compensation based on the Bayesian method requires accurate prior information. Insufficient or incorrect prior information may introduce new errors. The image fusion algorithm used in multi-view imaging and image fusion needs to be efficient and accurate to ensure accurate registration and fusion between images from different viewpoints, which places high demands on hardware.

[0032] In summary, the imaging accuracy of industrial computed tomography is improved by reducing the influence of noise interference, multipath interference or scattering effect on the imaging effect of industrial computed tomography. However, the above method has low imaging accuracy of industrial computed tomography due to its low limitation or high hardware requirements.

[0033] In order to improve the above-mentioned problems, the present application provides an image processing method, an apparatus, a computer tomography device and a storage medium, which are applied to a computer tomography device, wherein the computer tomography device is used to rotate about a preset rotation axis to continuously acquire multiple images of the object to be detected at different angles around the object to be detected. The image processing method includes: determining a first image and a second image adjacent to a target image in multiple images; the target image is any one of the multiple images; rotating the first image and the second image about a preset rotation axis respectively to obtain a rotated first image and a second image corresponding to the target image; determining at least one abnormal pixel area in the target image according to the rotated first image and the rotated second image; determining the pixel value of the center point of each abnormal pixel area according to the pixel value of each pixel point in each abnormal pixel area, the first abnormal area corresponding to each abnormal pixel area on the rotated first image, and the second abnormal area corresponding to each abnormal pixel area on the rotated second image; determining the final target image according to the pixel value of the center point of each abnormal pixel area in at least one abnormal pixel area.

[0034] Therefore, according to the first image and the second image adjacent to the target image, the pixel values ​​of the pixel points on the target image that do not conform to the actual situation are corrected so that the pixel values ​​of the pixel points on the target image are closer to the actual situation, thereby obtaining a target image with higher accuracy, so as to improve the imaging accuracy of industrial computed tomography technology.

[0035] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0036] See also Figure 1 , Figure 1 FIG. 1 is a flow chart of an image processing method provided by an embodiment of the present application. Figure 1 As shown, the image processing method is applied to a computer tomography device, which is used to rotate about a preset rotation axis to continuously acquire multiple images of the object to be detected at different angles around the object to be detected. The image processing method may include steps 110 to 150.

[0037] In step 110 , a first image and a second image adjacent to the target image in a plurality of images are determined.

[0038] The computer tomography device may include a workbench, a scanning module, and a processing module. The workbench may be used to carry an object to be detected. The scanning module may be used to rotate about a preset rotation axis, around the object to be detected, and emit X-rays to the object to be detected, so as to obtain multiple images of the object to be detected at different angles. For example, the scanning module rotates about a preset rotation axis, and obtains an image of the object to be detected at every preset angle, so as to obtain multiple images of the object to be detected.

[0039] In some embodiments, the scanning module rotates about a preset rotation axis, and acquires an image of the object to be detected every one angle, so as to acquire 360 ​​images of the object to be detected. For ease of explanation, the present application uses the scanning module to acquire an image of the object to be detected every one degree, so as to acquire 360 ​​images of the object to be detected. It is understandable that any one of the 360 ​​images is separated from its adjacent image by one degree.

[0040] The processing module can be used to process the image information collected by the scanning module to obtain a clearer image. That is, the processing module executes the image processing method of the present application to obtain the final target image.

[0041] In some embodiments, the coordinate system corresponding to the computer tomography device includes an X-axis, a Y-axis, and a Z-axis. The computer tomography device is specifically used to obtain an image of the object to be detected once at every preset angle around the object to be detected, using the X-axis of the computer tomography device as a rotation axis, so as to obtain multiple images.

[0042] In other embodiments, the computed tomography device is specifically used to obtain an image of the object to be detected once at every preset angle around the object to be detected, using the Y-axis of the computed tomography device as a rotation axis, so as to obtain multiple images.

[0043] In other embodiments, the computed tomography device is specifically used to obtain an image of the object to be detected once at every preset angle around the object to be detected, using the Z axis of the computed tomography device as a rotation axis, so as to obtain multiple images.

[0044] For ease of explanation, the present application uses the X-axis of the computed tomography device as the rotation axis, and acquires an image of the object to be detected once at every preset angle around the object to be detected, taking acquiring multiple images as an example for specific explanation.

[0045] The target image is any one of the multiple images. The multiple images are sorted according to the order in which the scanning module acquires the images, that is, they are sorted in order according to the acquisition time corresponding to each of the multiple images. In other words, there is a difference in the angle of the corresponding scanning module between any one of the multiple images and its two adjacent images, that is, there is a preset angle between any one of the multiple images and its two adjacent images.

[0046] After determining the target image, the processing module determines the images adjacent to the target image as the first image and the second image, respectively, among the multiple images arranged in the order of the acquired images. For example, the multiple images include images A, B, and C arranged in the order of the acquired images, and image B is determined as the target image, then image A is the first image and image C is the second image.

[0047] For example, please refer to Figure 2 , Figure 2 A schematic diagram of the structure of an image provided by an embodiment of the present application is shown. Figure 2 The computer tomography device is used to scan multiple images of the object to be detected. Figure 2 2A in is the first image, Figure 2 2B in is the target image, Figure 2 2C in the figure is the second image.

[0048] After determining the first image and the second image adjacent to the target image in the plurality of images, the first image and the second image are processed in the subsequent steps to reconfirm the pixel values ​​corresponding to the pixel points on the target image according to the processed first image and the second image, so as to reduce the influence of interference and other factors on the target image, thereby improving the imaging accuracy of the computer tomography device. Specifically: In step 120, the first image and the second image are respectively rotated about a preset rotation axis to obtain a rotated first image and a rotated second image corresponding to the target image.

[0049] From the above description, it can be seen that the first image differs from the target image by a preset angle, and the target image differs from the second image by a preset angle. The processing module rotates the first image along the X-axis to the angle corresponding to the target image, so that the angle corresponding to the first image after rotation is the same as that of the target image; the processing module rotates the second image along the X-axis to the angle corresponding to the target image, so that the angle corresponding to the second image after rotation is the same as that of the target image.

[0050] Since the computed tomography device rotates on the X-axis, the pixel points corresponding to a point on the object to be detected in multiple images can form a circle. By calculating the specific position information of each point on the circle, the specific position information of the pixel point on each image can be determined.

[0051] Specifically, in some embodiments, the step of rotating the first image and the second image respectively about a preset rotation axis to obtain the rotated first image and the second image corresponding to the target image may include the following steps: (1) Determine the first rotation point corresponding to each pixel point on the X-axis in the first image.

[0052] (2) Determine the coordinate information of each pixel in the first image after rotation based on the coordinate information of each pixel in the first image, the coordinate information of the first rotation point corresponding to each pixel in the first image, and the preset angle, so as to determine the rotated first image.

[0053] (3) Determine the second rotation point corresponding to each pixel point in the second image on the X-axis.

[0054] (4) Determine the coordinate information of each pixel in the second image after rotation based on the coordinate information of each pixel in the second image, the coordinate information of the second rotation point corresponding to each pixel in the second image, and the preset angle, so as to determine the rotated second image.

[0055] The first rotation point corresponding to each pixel in the first image is the center of the circle corresponding to each pixel in the first image. For example, assuming that there is a pixel A in the first image, pixel A can form a circle with the center located on the X-axis on multiple images, and the center is determined as the first rotation point corresponding to pixel A.

[0056] The processing module can determine the radius information of the circle corresponding to each pixel in the first image according to the coordinate information of each pixel in the first image and the first rotation point corresponding to each pixel, so as to determine the first equation group corresponding to the circle (i.e., the parametric equation of the circle) according to the radius information of the circle corresponding to each pixel in the first image, the first rotation point corresponding to each pixel, and the preset angle, so as to determine the coordinate information corresponding to the pixel in the first image on the target image according to the parametric equation of the circle. The pixel in the first image is rotated to obtain the coordinate information of the corresponding pixel on the target image. Each pixel in the first image is rotated in the above manner to obtain the position information corresponding to the pixel in the first image on the target image, so as to obtain the rotated first image.

[0057] Exemplarily, the first image includes pixel point A, and the center of the circle formed by pixel point A on multiple images is center A. According to the coordinate information of pixel point A and the coordinate information of center A, the distance between pixel point A and center A can be determined, that is, the radius of the circle formed by pixel point A on multiple images can be determined. According to the coordinate information of center A, the radius of the circle formed by pixel point A on multiple images, and the preset angle, the coordinate information of pixel point A on the target image can be determined. Each pixel in the first image determines the corresponding coordinate information of each pixel on the target image in the above manner, thereby determining the corresponding coordinate information of each pixel in the first image on the target image to obtain the rotated first image.

[0058] It can be understood that the second rotation point corresponding to each pixel in the second image is the center of the circle corresponding to each pixel in the second image. Similarly, the processing module determines the coordinate information of each pixel in the second image after rotation to obtain the rotated second image. The processing module can refer to the coordinate information of each pixel in the first image after rotation to obtain the corresponding description of the rotated first image, which will not be repeated here.

[0059] More specifically, in some embodiments, the step of determining the coordinate information of each pixel in the first image after rotation according to the coordinate information of each pixel in the first image, the coordinate information of the first rotation point corresponding to each pixel in the first image, and the preset angle may include the following steps: (1) Determine a first distance value between each pixel point in the first image and a first rotation point corresponding to each pixel point in the first image.

[0060] (2) Determine the coordinate information of each pixel in the first image after rotation according to the coordinate information of the first rotation point corresponding to each pixel in the first image, the preset angle, and the first distance value corresponding to each pixel in the first image.

[0061] The first distance value may be the radius of the circle corresponding to each pixel in the first image, that is, the first distance value is the distance between each pixel in the first image and the center of the circle corresponding to it. For example, assuming that there is a pixel A in the first image, the center of the circle formed by the pixel A on the multiple images is the center A, and the first distance value corresponding to the pixel A is the distance between the coordinate information of the pixel A and the coordinate information of the center A.

[0062] Based on the coordinate information of the center of the circle corresponding to each pixel point in the first image, the radius of the circle corresponding to each pixel point in the first image, and the angle difference between the first image and the target image, the coordinate information of the pixel point corresponding to each pixel point in the first image in the target image can be determined, so as to correspond each pixel point in the first image with each pixel point on the target image.

[0063] In a specific embodiment, the step of determining the coordinate information of each pixel point in the first image after rotation based on the coordinate information of the first rotation point corresponding to each pixel point in the first image, a preset angle, and a first distance value corresponding to each pixel point in the first image may include: determining the coordinate information of each pixel point in the first image after rotation through a first set of equations.

[0064] The expression of the first system of equations is: ; Among them, the coordinate information (x(t), y(t)) is the coordinate information of each pixel point in the first image after rotation, the coordinate information (h, k) is the coordinate information of the first rotation point corresponding to each pixel point in the first image, r is the first distance value corresponding to each pixel point in the first image, and t is the preset angle.

[0065] Where t can be expressed in radians. In order to obtain the pixel point corresponding to a certain point on the object to be detected on 360 images, t can be varied from 0 to 2π, and the scanning angle can be used as an increment. For example, the t corresponding to the first image is 0, the t corresponding to the second image is (0+preset angle), and so on.

[0066] In some embodiments, the step of determining the coordinate information of each pixel in the first image after rotation according to the coordinate information of each pixel in the first image, the coordinate information of the first rotation point corresponding to each pixel in the first image, and the preset angle may include the following steps: (1) Determine a second distance value between each pixel point in the second image and a second rotation point corresponding to each pixel point in the second image.

[0067] (2) Determine the coordinate information of each pixel in the second image after rotation according to the coordinate information of the second rotation point corresponding to each pixel in the second image, the preset angle, and the second distance value corresponding to each pixel in the second image.

[0068] The second distance value may be the radius of the circle corresponding to each pixel in the second image, that is, the second distance value is the distance between each pixel in the second image and the center of the circle corresponding to it. For example, assuming that there is a pixel A in the second image, the center of the circle formed by the pixel A on the multiple images is the center A, and the second distance value corresponding to the pixel A is the distance between the coordinate information of the pixel A and the coordinate information of the center A.

[0069] Based on the coordinate information of the center of the circle corresponding to each pixel point in the second image, the radius of the circle corresponding to each pixel point in the second image, and the angle difference between the second image and the target image, the coordinate information of the pixel point corresponding to each pixel point in the second image in the target image can be determined, so as to correspond each pixel point in the second image with each pixel point on the target image.

[0070] In a specific embodiment, the step of determining the coordinate information of each pixel point in the second image after rotation based on the coordinate information of the second rotation point corresponding to each pixel point in the second image, a preset angle, and a second distance value corresponding to each pixel point in the second image may include: determining the coordinate information of each pixel point in the second image after rotation through a first set of equations.

[0071] Among them, the coordinate information (x(t), y(t)) is the coordinate information of each pixel point in the second image after rotation, the coordinate information (h, k) is the coordinate information of the second rotation point corresponding to each pixel point in the second image, r is the second distance value corresponding to each pixel point in the second image, and t is the preset angle.

[0072] By rotating the first image and the second image respectively about a preset rotation axis, the rotated first image and the second image corresponding to the target image are obtained, so that the pixel points on the first image are corresponded to the pixel points on the target image, and the pixel points on the second image are corresponded to the pixel points on the target image, so that the pixel value corresponding to the pixel point corresponding to the first image of a certain pixel point on the target image and the pixel value corresponding to the pixel point corresponding to the second image of a certain pixel point on the target image can be determined, and then according to the pixel value corresponding to the pixel point on the target image, the pixel value corresponding to the pixel point corresponding to the first image and the pixel value corresponding to the pixel point corresponding to the second image, it is determined whether the pixel point is affected by interference and other factors.

[0073] When it is determined that the pixel point is affected by interference and other factors and becomes inaccurate, the pixel value corresponding to the pixel point is corrected to reduce the impact of interference and other factors on the imaging accuracy of the computer tomography device. Specifically: In step 130, at least one abnormal pixel region in the target image is determined according to the rotated first image and the rotated second image.

[0074] The abnormal pixel area is a pixel value corresponding to a pixel point at the center of the abnormal pixel area that is different from or has a large difference with the pixel value corresponding to the pixel point corresponding to the pixel point on the first image, or the pixel value corresponding to the pixel point at the center of the abnormal pixel area is different from or has a large difference with the pixel value corresponding to the pixel point on the second image.

[0075] Specifically, in one embodiment, the step of determining at least one abnormal pixel region in the target image according to the rotated first image and the rotated second image may include the following steps: (1) When a pixel value of a pixel point on the target image is different from a pixel value of a corresponding pixel point on the rotated first image, the pixel point is determined to be a first abnormal pixel point on the target image.

[0076] (2) When a pixel value of a pixel point on the target image is different from a pixel value of a corresponding pixel point on the rotated second image, the pixel point is determined to be a second abnormal pixel point on the target image.

[0077] (3) Determine a final abnormal pixel in the target image according to the first abnormal pixel and the second abnormal pixel.

[0078] (4) Determine at least one abnormal pixel region based on each final abnormal pixel point and the pixels adjacent to each final abnormal pixel point.

[0079] The processing module determines the pixel point with the same coordinate information as the pixel point on the rotated first image based on the coordinate information of the pixel point on the target image, so as to determine the pixel value of the pixel point corresponding to the pixel point on the first image, thereby determining whether the pixel value corresponding to the pixel point on the target image is the same as the pixel value corresponding to the pixel point on the target image on the first image.

[0080] Exemplarily, the target image includes pixel point A, and pixel point B having the same coordinate information as pixel point A is determined on the rotated first image based on the coordinate information of pixel point A, that is, pixel point B is the pixel point corresponding to pixel point A on the first image. Thus, by comparing the pixel value corresponding to pixel point A with the pixel value corresponding to pixel point B, it can be determined whether pixel point A is inaccurate due to interference or other factors.

[0081] The processing module determines the pixel point with the same coordinate information as the pixel point on the rotated second image based on the coordinate information of the pixel point on the target image, so as to determine the pixel value of the pixel point corresponding to the pixel point on the second image, thereby determining whether the pixel value corresponding to the pixel point on the target image is the same as the pixel value of the pixel point corresponding to the pixel point on the target image on the second image.

[0082] Exemplarily, the target image includes pixel point A, and pixel point B having the same coordinate information as pixel point A is determined on the rotated second image based on the coordinate information of pixel point A, that is, pixel point B is the pixel point corresponding to pixel point A on the second image. Thus, by comparing the pixel value corresponding to pixel point A with the pixel value corresponding to pixel point B, it can be determined whether pixel point A is inaccurate due to interference or other factors.

[0083] The processing module determines the pixel values ​​of the corresponding pixels on the first image and the second image of the pixel points on the target image, so as to determine the inaccurate pixel points on the target image due to interference and other factors as final abnormal pixel points, and then determines at least one abnormal pixel area by combining each final abnormal pixel point and the pixel points adjacent to each final abnormal pixel point.

[0084] Exemplarily, the final abnormal pixel includes a final abnormal pixel A and a final abnormal pixel B, and the final abnormal pixel A and the pixels adjacent to the final abnormal pixel A are determined as an abnormal pixel region. The final abnormal pixel B and the pixels adjacent to the final abnormal pixel B are determined as an abnormal pixel region. Thus, multiple abnormal pixel regions in the target image are determined.

[0085] For example, please refer to Figure 3 , Figure 3 FIG. 4 shows a schematic diagram of a structure of an abnormal pixel region provided in an embodiment of the present application. Figure 3 As shown, the processing module determines the pixel points adjacent to the final abnormal pixel point A as an abnormal pixel region in the at least one abnormal pixel region.

[0086] In another embodiment, the step of determining at least one abnormal pixel region in the target image according to the rotated first image and the rotated second image may include the following steps: (1) When a difference between a pixel value of a pixel point on the target image and a pixel value of a corresponding pixel point on the rotated first image is greater than a first preset threshold, the pixel point is determined to be a third abnormal pixel point on the target image.

[0087] (2) When a difference between a pixel value of a pixel point on the target image and a pixel value of a corresponding pixel point on the rotated second image is greater than a second preset threshold, the pixel point is determined to be a fourth abnormal pixel point on the target image.

[0088] (3) Determine a final abnormal pixel in the target image according to the third abnormal pixel and the fourth abnormal pixel.

[0089] (4) Determine at least one abnormal pixel region based on each final abnormal pixel point and the pixels adjacent to each final abnormal pixel point.

[0090] The processing module determines the pixel point with the same coordinate information as the pixel point on the rotated first image based on the coordinate information of the pixel point on the target image, so as to determine the pixel value of the pixel point on the target image corresponding to the pixel point on the first image, thereby determining whether the difference between the pixel value corresponding to the pixel point on the target image and the pixel value corresponding to the pixel point on the target image on the first image is greater than the first preset threshold, thereby determining the third abnormal pixel point.

[0091] Exemplarily, the target image includes pixel point A, and pixel point B having the same coordinate information as pixel point A is determined on the rotated first image based on the coordinate information of pixel point A, that is, pixel point B is the pixel point corresponding to pixel point A on the first image. Thus, by comparing the difference between the pixel value corresponding to pixel point A and the pixel value corresponding to pixel point B with the first preset threshold, it can be determined whether pixel point A is inaccurate due to interference or other factors.

[0092] The processing module determines the pixel point with the same coordinate information as the pixel point on the rotated second image based on the coordinate information of the pixel point on the target image, so as to determine the pixel value of the pixel point on the target image corresponding to the pixel point on the second image, thereby determining whether the difference between the pixel value corresponding to the pixel point on the target image and the pixel value corresponding to the pixel point on the target image on the second image is greater than the second preset threshold, thereby determining the fourth abnormal pixel point.

[0093] Exemplarily, the target image includes pixel point A, and pixel point B having the same coordinate information as pixel point A is determined on the rotated second image based on the coordinate information of pixel point A, that is, pixel point B is the pixel point corresponding to pixel point A on the second image. Thus, by comparing the difference between the pixel value corresponding to pixel point A and the pixel value corresponding to pixel point B with the second preset threshold, it can be determined whether pixel point A is inaccurate due to interference or other factors.

[0094] The processing module determines the pixel values ​​of the corresponding pixels on the first image and the second image of the pixel points on the target image, so as to determine the inaccurate pixel points on the target image due to interference and other factors as final abnormal pixel points, and then determines at least one abnormal pixel area by combining each final abnormal pixel point and the pixel points adjacent to each final abnormal pixel point.

[0095] Among them, the specific values ​​of the first preset threshold and the second preset threshold can be flexibly set according to actual conditions. If the imaging quality requirements for the target image are high, the specific values ​​of the first preset threshold and the second preset threshold can be set to be smaller.

[0096] After determining at least one abnormal pixel region, the processing module determines the pixel value of the pixel point at the center position of the abnormal pixel region according to the pixel values ​​corresponding to the first abnormal pixel region corresponding to the at least one abnormal pixel region on the first image and the pixel points contained in the second abnormal pixel region corresponding to the at least one abnormal pixel region on the second image, so as to complete the correction of the pixels in the abnormal pixel region, thereby obtaining a more accurate target image. Specifically: In step 140, the pixel value of the center point of each abnormal pixel area is determined according to the pixel value of each pixel point in each abnormal pixel area, the first abnormal area corresponding to each abnormal pixel area on the rotated first image, and the second abnormal area corresponding to each abnormal pixel area on the rotated second image.

[0097] Exemplarily, at least one abnormal pixel area includes an abnormal pixel area A, a first abnormal area B corresponding to the abnormal pixel area A on the first image, and a second abnormal area C corresponding to the abnormal pixel area A on the second image. The pixel value of the center point of the abnormal pixel area A is determined based on the pixel values ​​corresponding to the pixel points contained in the abnormal pixel area A, the first abnormal area B, and the second abnormal area C, so as to complete the correction of the pixel value of the center point of the abnormal pixel area A and reduce the inaccuracy caused by the influence of interference and other factors on the center point of the abnormal pixel area A.

[0098] Through the above method, the pixel value of the center point of each abnormal pixel area is determined in turn to complete the correction of the pixel value of the center point of each abnormal pixel area, reduce the influence of interference and other factors on the target image and cause low imaging quality, so as to obtain a target image with higher accuracy.

[0099] Further, in some embodiments, the step of determining the pixel value of the center point of each abnormal pixel region according to the pixel value of each pixel point in each abnormal pixel region, the first abnormal region corresponding to each abnormal pixel region on the rotated first image, and the second abnormal region corresponding to each abnormal pixel region on the rotated second image may include the following steps: (1) Determine a pixel range corresponding to the center point of each abnormal pixel region according to the pixel value of each pixel point in each abnormal pixel region, the pixel value of each pixel point in the first abnormal region corresponding to each abnormal pixel region on the rotated first image, and the pixel value of each pixel point in the second abnormal region corresponding to each abnormal pixel region on the rotated second image.

[0100] (2) The median of the pixel range corresponding to the center point of each abnormal pixel area is determined as the pixel value of the center point of each abnormal pixel area.

[0101] The processing module can determine the pixel point with the same coordinate information as the center point on the rotated first image based on the coordinate information of the center point of each abnormal pixel area, thereby determining the pixel point corresponding to the center point on the first image, and then determine the first abnormal pixel area corresponding to the abnormal pixel area based on the pixel point corresponding to the center point on the first image and its adjacent pixel points.

[0102] The processing module can determine the pixel point with the same coordinate information as the center point on the rotated second image based on the coordinate information of the center point of each abnormal pixel area, thereby determining the pixel point corresponding to the center point on the second image, and then determine the second abnormal pixel area corresponding to the abnormal pixel area based on the pixel point corresponding to the center point on the second image and its adjacent pixel points.

[0103] The processing module then determines the pixel range of the center point of each abnormal pixel area according to each abnormal pixel area, the first abnormal pixel area corresponding to each abnormal pixel area, and the second abnormal pixel area corresponding to each abnormal pixel area.

[0104] Exemplarily, at least one abnormal pixel area includes an abnormal pixel area A, a first abnormal area B corresponding to the abnormal pixel area A on the first image, and a second abnormal area C corresponding to the abnormal pixel area A on the second image. The pixel range corresponding to the center point of the abnormal pixel area A is determined based on the pixel values ​​corresponding to the pixel points contained in the abnormal pixel area A, the first abnormal area B, and the second abnormal area C.

[0105] That is to say, the pixel range corresponding to a certain abnormal pixel area is a pixel interval composed of the pixel value corresponding to each pixel point in the abnormal pixel area, the pixel value corresponding to each pixel point in the first abnormal area corresponding to the abnormal pixel area on the first image, and the pixel value corresponding to each pixel point in the second abnormal area corresponding to the abnormal pixel area on the second image.

[0106] That is, the processing module determines the pixel value corresponding to each pixel point in the abnormal pixel area, the pixel value corresponding to each pixel point in the first abnormal area corresponding to the abnormal pixel area on the first image, and each pixel point in the second abnormal area corresponding to the abnormal pixel area on the second image, and determines the pixel range corresponding to the abnormal pixel area by the minimum pixel value and the maximum pixel value among these pixel values.

[0107] It is understandable that since a pixel point in an abnormal pixel area may be affected by interference and other factors and undergo a large change, the pixel point may be determined as the minimum pixel value or the maximum pixel value. In this case, the median of the pixel range corresponding to the abnormal pixel area is determined based on the minimum pixel value and the maximum pixel value to determine the pixel value corresponding to the center point of the abnormal pixel area. This situation will cause the pixel value corresponding to the corrected pixel point to differ greatly from the actual value.

[0108] Similarly, a pixel point in the first abnormal pixel area and the second abnormal pixel area may also be affected by interference and other factors and undergo significant changes. These abnormal pixel points may be determined as the minimum pixel value and the maximum pixel value, so that the pixel value corresponding to the corrected pixel point is significantly different from the actual value.

[0109] Based on the above situation, the step of determining the pixel value of the center point of each abnormal pixel area according to the pixel value of each abnormal pixel area, the first abnormal area corresponding to each abnormal pixel area on the rotated first image, and the second abnormal area corresponding to each abnormal pixel area on the rotated second image, may include the following steps: (1) Determine the median corresponding to each abnormal pixel region according to the pixel value of each pixel point in each abnormal pixel region, the pixel value of each pixel point in the first abnormal region corresponding to each abnormal pixel region on the rotated first image, and the pixel value of each pixel point in the second abnormal region corresponding to each abnormal pixel region on the rotated second image.

[0110] (2) Determine the pixel range corresponding to each abnormal pixel area based on the median and the third preset threshold.

[0111] (3) The median of the pixel range corresponding to the center point of each abnormal pixel area is determined as the pixel value of the center point of each abnormal pixel area.

[0112] The processing module determines the median of these pixel values ​​by determining the pixel value corresponding to each pixel point in a certain abnormal pixel area, the pixel value corresponding to each pixel point in a first abnormal area corresponding to the abnormal pixel area on the first image, and the pixel value corresponding to each pixel point in a second abnormal area corresponding to the abnormal pixel area on the second image.

[0113] The processing module then screens out pixel values ​​whose difference from the median is greater than a third preset threshold value to determine pixel values ​​that are closer to the median, thereby re-determining the pixel range corresponding to the abnormal pixel area based on the screened pixel values, and then determining the median of the pixel range as the pixel value of the center point of the abnormal pixel area, so as to eliminate the interference of the pixel points in the abnormal pixel area, the first abnormal pixel area corresponding to the abnormal pixel area, and the second abnormal pixel area corresponding to the abnormal pixel area, which are greatly different from the actual pixel points in confirming the pixel value of the pixel point of the center point of the abnormal pixel area, so as to obtain a more accurate pixel value of the pixel point of the center point of the abnormal pixel area.

[0114] It is understandable that the pixel values ​​of the pixel points at the center points of other abnormal pixel areas can also be determined one by one in the above manner, thereby correcting the pixel values ​​of the pixel points at the center points of each abnormal pixel area on the target image to obtain a target image that is more in line with reality.

[0115] In step 150, a final target image is determined according to the pixel value of the center point of each abnormal pixel region in at least one abnormal pixel region.

[0116] The processing module corrects the pixel values ​​of the pixels at the center of the abnormal pixel area one by one to obtain the final target image. In this way, the inaccurate pixels in the target image initially taken by the computer tomography device due to interference and other factors are screened out and corrected to obtain a target image closer to the actual one, thereby improving the imaging effect of the computer tomography device.

[0117] From the above description, it can be seen that by comparing the pixel values ​​corresponding to the pixel points on the target image with the pixel values ​​corresponding to the pixel points on the target image in the first image, and comparing the pixel values ​​corresponding to the pixel points on the target image with the pixel values ​​corresponding to the pixel points on the target image in the second image, the pixel points on the target image whose values ​​differ from the actual values ​​due to interference and other factors are determined.

[0118] Since there are many pixels on the target image, it is necessary to rotate the pixels on the first image and the pixels on the second image to determine the pixels on the first image and the corresponding pixels on the target image, as well as the pixels on the second image and the corresponding pixels on the target image.

[0119] Furthermore, the number of images acquired by the computer tomography device is relatively large, and each image acquired by the computer tomography device is corrected using the above method, which results in a large number of the above calculation processes of the computer tomography device, and occupies a large amount of computing resources.

[0120] Based on the above situation, the computed tomography device in the present application rotates about a preset rotation axis, and in the process of continuously acquiring multiple images of the object to be detected at different angles around the object to be detected, the scanning module acquires the image of the object to be detected twice every degree to acquire 720 images of the object to be detected.

[0121] That is to say, when the scanning module obtains an image of the object to be detected at a certain angle, the scanning module obtains two images of the object to be detected at the same angle.

[0122] The processing module determines any one of the two images of the object to be detected at a certain angle as the target image. Before the processing module corrects the corresponding pixel points in the target image by the above scheme, the processing module compares the pixel values ​​corresponding to the pixel points in the target image with the pixel values ​​corresponding to the pixel points in another image corresponding to the target image. If the pixel values ​​corresponding to the pixel points in the target image are significantly different from the pixel values ​​corresponding to the pixel points in the other image corresponding to the target image, the pixel points corresponding to the pixel points in the target image with the larger difference on the first image and the pixel points corresponding to the second image are determined, and then the pixel values ​​corresponding to the pixel points in the target image with the larger difference are corrected by the above method.

[0123] Therefore, there is no need to rotate all the pixels on the first image, and there is no need to rotate all the pixels on the second image. It is only necessary to rotate the pixels in the target image corresponding to the larger gap to the angles corresponding to the first image and the second image, so as to determine the pixels in the target image corresponding to the larger gap corresponding to the pixels on the first image and the pixels on the second image, so as to determine whether the pixels in the target image corresponding to the larger gap are inconsistent with the actual situation, and whether to perform the subsequent correction step of the pixel values ​​corresponding to the pixels in the target image corresponding to the larger gap. This effectively reduces the calculation pressure of the computed tomography equipment.

[0124] See also Figure 4 , Figure 4 A schematic diagram of the structure of an image processing device provided in an embodiment of the present application is shown, which is applied to a computer tomography device, and the computer tomography device is used to rotate about a preset rotation axis to continuously acquire multiple images of the object to be detected at different angles around the object to be detected. The image processing device 200 includes: a first determination module 210, a rotation module 220, a second determination module 230, a first execution module 240 and a second execution module 250. Specifically: A first determination module 210 is used to determine a first image and a second image that are adjacent to a target image among multiple images; the target image is any one of the multiple images; A rotation module 220, configured to rotate the first image and the second image respectively about a preset rotation axis to obtain the rotated first image and the second image corresponding to the target image; A second determination module 230, configured to determine at least one abnormal pixel region in the target image according to the rotated first image and the rotated second image; A first execution module 240 is used to determine the pixel value of the center point of each abnormal pixel area according to the pixel value of each abnormal pixel area, the first abnormal area corresponding to each abnormal pixel area on the rotated first image, and the second abnormal area corresponding to each abnormal pixel area on the rotated second image; The second execution module 250 is used to determine a final target image according to the pixel value of the center point of each abnormal pixel area in at least one abnormal pixel area.

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0126] In several embodiments provided in the present application, the coupling or direct coupling or communication connection between the modules shown or discussed may be an indirect coupling or communication connection through some interfaces, devices or modules, which may be electrical, mechanical or other forms.

[0127] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software functional modules.

[0128] See also Figure 5 , Figure 5 A structural schematic diagram of a computer tomography device provided in an embodiment of the present application is shown. The computer tomography device 300 in the present application may include one or more of the following components: a processor 310, a memory 320 and one or more application programs, wherein the one or more application programs may be stored in the memory 320 and configured to be executed by one or more processors 310, and the one or more programs are configured to execute the image processing method described in the aforementioned method embodiment.

[0129] The processor 310 may include one or more processing cores. The processor 310 uses various interfaces and lines to connect various parts of the entire terminal device 300, and executes various functions and processes data of the terminal device 300 by running or executing instructions, programs, code sets or instruction sets stored in the memory 320, and calling data stored in the memory 320. Optionally, the processor 310 can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 310 can integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 310, but may be implemented separately through a communication chip.

[0130] The memory 320 may include a random access memory (RAM) or a read-only memory (ROM). The memory 320 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 320 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the following various method embodiments, etc. The data storage area may also store data created by the terminal device 300 during use.

[0131] See also Figure 6 , Figure 6 A schematic diagram of the structure of a computer-readable storage medium provided in an embodiment of the present application is shown. The computer-readable medium 400 stores program code, and the program code can be called by a processor to execute the image processing method described in the above method embodiment.

[0132] The computer-readable storage medium 400 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. Optionally, the computer-readable storage medium 300 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 400 has storage space for program codes 410 for executing any method steps in the above method. These program codes may be read from or written to one or more computer program devices. The program code 410 may be compressed, for example, in a suitable form.

[0133] The technical solution provided in the present application is applied to a computer tomography device, which is used to rotate about a preset rotation axis to continuously obtain multiple images of the object to be detected at different angles around the object to be detected. The image processing method includes: determining a first image and a second image adjacent to a target image in the multiple images; the target image is any one of the multiple images; rotating the first image and the second image about the preset rotation axis respectively to obtain a rotated first image and a second image corresponding to the target image; determining at least one abnormal pixel area in the target image according to the rotated first image and the rotated second image; determining the pixel value of the center point of each abnormal pixel area according to the pixel value of each pixel point in each abnormal pixel area, the first abnormal area corresponding to each abnormal pixel area on the rotated first image, and the second abnormal area corresponding to each abnormal pixel area on the rotated second image; and determining the final target image according to the pixel value of the center point of each abnormal pixel area in at least one abnormal pixel area. Thus, according to the first image and the second image adjacent to the target image, the pixel values ​​of the pixels on the target image that are inconsistent with the actual situation are corrected to make the pixel values ​​of the pixels on the target image closer to the actual situation, thereby obtaining a target image with higher accuracy, so as to improve the imaging accuracy of industrial computed tomography technology.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An image processing method, characterized in that: Applied to a computed tomography (CT) device, the CT device is used to rotate around a preset rotation axis and continuously acquire multiple images of a to-be-detected object at different angles around the to-be-detected object. The method includes: Determine a first image and a second image adjacent to a target image among the multiple images; the target image is any one of the multiple images; Perform rotation operations on the first image and the second image respectively around the preset rotation axis to obtain a rotated first image and a rotated second image corresponding to the target image; Determine at least one abnormal pixel region in the target image according to the rotated first image and the rotated second image; Determine the pixel value of the center point of each abnormal pixel region according to the pixel value of each pixel point in each abnormal pixel region, the first abnormal region corresponding to each abnormal pixel region on the rotated first image, and the second abnormal region corresponding to each abnormal pixel region on the rotated second image; Determine the final target image according to the pixel value of the center point of each abnormal pixel region in the at least one abnormal pixel region; 2. The image processing method according to claim 1, characterized in that: The CT device is specifically used to take the X-axis of the CT device as the rotation axis and acquire an image of the to-be-detected object around the to-be-detected object at preset intervals to obtain the multiple images; The performing rotation operations on the first image and the second image respectively around the preset rotation axis to obtain a rotated first image and a rotated second image corresponding to the target image includes: Determine a first rotation point corresponding to each pixel point in the first image on the X-axis respectively; Determine the rotated coordinate information of each pixel point in the first image according to the coordinate information of each pixel point in the first image, the coordinate information of the first rotation point corresponding to each pixel point in the first image, and the preset angle to determine the rotated first image; Determine a second rotation point corresponding to each pixel point in the second image on the X-axis respectively; Determine the rotated coordinate information of each pixel point in the second image according to the coordinate information of each pixel point in the second image, the coordinate information of the second rotation point corresponding to each pixel point in the second image, and the preset angle to determine the rotated second image; 3. The image processing method according to claim 2, characterized in that: The determining the rotated coordinate information of each pixel point in the first image according to the coordinate information of each pixel point in the first image, the coordinate information of the first rotation point corresponding to each pixel point in the first image, and the preset angle includes: Determine a first distance value between each pixel point in the first image and the first rotation point corresponding to each pixel point in the first image; Determine the rotated coordinate information of each pixel point in the first image according to the coordinate information of the first rotation point corresponding to each pixel point in the first image, the preset angle, and the first distance value corresponding to each pixel point in the first image; And / or, determining the coordinate information of each pixel in the second image after rotation according to the coordinate information of each pixel in the second image, the coordinate information of the second rotation point corresponding to each pixel in the second image, and the preset angle, includes: Determine a second distance value between each pixel point in the second image and the second rotation point corresponding to each pixel point in the second image; Determine the coordinate information of each pixel in the second image after rotation according to the coordinate information of the second rotation point corresponding to each pixel in the second image, the preset angle, and the second distance value corresponding to each pixel in the second image.

4. The image processing method according to claim 2, characterized in that: The determining, according to the coordinate information of the first rotation point corresponding to each pixel in the first image, the preset angle, and the first distance value corresponding to each pixel in the first image, coordinate information of each pixel in the first image after rotation includes: The coordinate information of each pixel point in the first image after rotation is determined by a first set of equations, and the expression of the first set of equations is: Wherein, the coordinate information (x(t), y(t)) is the coordinate information of each pixel point in the first image after rotation, the coordinate information (h, k) is the coordinate information of the first rotation point corresponding to each pixel point in the first image, r is the first distance value corresponding to each pixel point in the first image, and t is the preset angle; The determining, according to the coordinate information of the second rotation point corresponding to each pixel in the second image, the preset angle, and the second distance value corresponding to each pixel in the second image, the coordinate information of each pixel in the second image after rotation includes: The coordinate information of each pixel point in the second image after rotation is determined by the first set of equations.

5. The image processing method according to claim 1, characterized in that: The determining at least one abnormal pixel region in the target image according to the rotated first image and the rotated second image comprises: When a pixel value of a pixel point on the target image is different from a pixel value of a corresponding pixel point of the pixel point on the rotated first image, determining the pixel point as a first abnormal pixel point on the target image; When a pixel value of a pixel point on the target image is different from a pixel value of a corresponding pixel point of the pixel point on the rotated second image, determining the pixel point as a second abnormal pixel point on the target image; Determine a final abnormal pixel in the target image according to the first abnormal pixel and the second abnormal pixel; The at least one abnormal pixel region is determined according to each of the final abnormal pixel points and the pixel points adjacent to each of the final abnormal pixel points.

6. The image processing method according to claim 1, characterized in that: The determining at least one abnormal pixel region in the target image according to the rotated first image and the rotated second image comprises: When a difference between a pixel value of a pixel point on the target image and a pixel value of a corresponding pixel point of the pixel point on the rotated first image is greater than a first preset threshold, determining that the pixel point is a third abnormal pixel point on the target image; When a difference between a pixel value of a pixel point on the target image and a pixel value of a corresponding pixel point of the pixel point on the rotated second image is greater than a second preset threshold, determining that the pixel point is a fourth abnormal pixel point on the target image; Determine a final abnormal pixel in the target image according to the third abnormal pixel and the fourth abnormal pixel; The at least one abnormal pixel region is determined according to each of the final abnormal pixel points and the pixel points adjacent to each of the final abnormal pixel points.

7. The image processing method according to claim 1, characterized in that: Determining the pixel value of the center point of each abnormal pixel region according to the pixel value of each abnormal pixel region, the first abnormal region corresponding to each abnormal pixel region on the rotated first image, and the second abnormal region corresponding to each abnormal pixel region on the rotated second image, comprises: Determine a pixel range corresponding to the center point of each abnormal pixel region according to the pixel value of each pixel point in each abnormal pixel region, the pixel value of each pixel point in the first abnormal region corresponding to each abnormal pixel region on the rotated first image, and the pixel value of each pixel point in the second abnormal region corresponding to each abnormal pixel region on the rotated second image; The median of the pixel range corresponding to the center point of each abnormal pixel area is determined as the pixel value of the center point of each abnormal pixel area.

8. An image processing device, characterized in that: Applied to a computer tomography device, the computer tomography device is used to rotate about a preset rotation axis to continuously acquire multiple images of the object to be detected at different angles around the object to be detected, and the device includes: A first determining module, used to determine a first image and a second image adjacent to a target image among the multiple images; the target image is any one of the multiple images; A rotation module, used to rotate the first image and the second image respectively about the preset rotation axis to obtain the rotated first image and the second image corresponding to the target image; A second determination module, configured to determine at least one abnormal pixel region in the target image according to the rotated first image and the rotated second image; A first execution module, used to determine the pixel value of the center point of each abnormal pixel area according to the pixel value of each pixel point in each abnormal pixel area, the first abnormal area corresponding to each abnormal pixel area on the rotated first image, and the second abnormal area corresponding to each abnormal pixel area on the rotated second image; The second execution module is used to determine the final target image according to the pixel value of the center point of each abnormal pixel area in the at least one abnormal pixel area.

9. A computer tomography device, characterized in that: include: one or more processors; Memory; One or more application programs, wherein the one or more application programs are stored in the memory and are configured to be executed by the one or more processors according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, and the program codes can be called by a processor to execute the image processing method according to any one of claims 1 to 7.

Images