CT image denoising method and device based on adaptive filtering and computer equipment

Abstract

The invention relates to a CT image denoising method and device based on adaptive filtering and computer equipment, and the method comprises the steps: constructing a corresponding weight adjustment function through noise information received by a detector based on different doses of X-rays; adjusting parameters of a target filtering algorithm according to the weight adjustment function; obtaining a plurality of first projection images corresponding to the target image, and filtering the first projection images according to the adjusted parameters of the target filtering algorithm to obtain corresponding target projection images; according to the image denoising method and device, the problem that the filtering algorithm parameters cannot be dynamically adjusted is solved, the filtering algorithm parameters are dynamically adjusted, and the image denoising effect is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a CT image denoising method, device and computer equipment based on adaptive filtering. Background Art

[0002] Computed Tomography (CT) uses an X-ray beam to scan a layer of a certain thickness of the human body. The detector receives the X-rays that pass through the layer and processes the received X-rays to generate a corresponding CT image to obtain information about the internal structure of the human body. During the image generation process, due to factors such as X-ray dose, tube voltage, and scanning layer thickness, noise is usually generated along with the CT image, resulting in reduced CT image quality. Therefore, it is necessary to effectively suppress image noise to improve image quality, thereby providing a reliable reference for medical diagnosis.

[0003] The current image denoising method obtains the corresponding image projection data according to the detection results of the detector, performs one-dimensional convolution filtering on the image projection data, and reconstructs the image according to the filtering results to obtain the corresponding target image, thereby reducing image noise. However, in the above method, the filtering algorithm parameters cannot be adjusted dynamically, and it is easy to have insufficient filtering strength or excessive filtering, resulting in poor image denoising effect.

[0004] With regard to the problem in the related art that the parameters of the filtering algorithm cannot be adjusted dynamically, no effective solution has been proposed yet. Summary of the invention

[0005] In this embodiment, a CT image denoising method, apparatus and computer device based on adaptive filtering are provided to solve the problem in the related art that the filtering algorithm parameters cannot be adjusted dynamically.

[0006] In a first aspect, a CT image denoising method based on adaptive filtering is provided in this embodiment, and the method comprises:

[0007] Based on the noise information received by the detector under different doses of X-rays, a corresponding weight adjustment function is constructed;

[0008] According to the weight adjustment function, adjusting the parameters of the target filtering algorithm;

[0009] Acquire a plurality of first projection images corresponding to the target image, and perform filtering processing on the first projection images according to the adjusted parameters of the target filtering algorithm to obtain corresponding target projection images;

[0010] The target projection image is reconstructed to obtain the target image.

[0011] In some embodiments, the constructing of a corresponding weight adjustment function based on the noise information received by the detector under different doses of X-rays includes:

[0012] Based on the noise information received by the detector under different doses of X-rays, determining the received grayscale value and signal variance value corresponding to each dose;

[0013] Performing curve fitting on the received grayscale value and the signal variance value to obtain a corresponding fitting curve;

[0014] The weight adjustment function is determined according to the fitting curve.

[0015] In some embodiments, adjusting the parameters of the target filtering algorithm according to the weight adjustment function includes:

[0016] Obtaining a scanning positioning piece corresponding to the target image;

[0017] Determine the average gray value of each pixel in the scanning positioning film;

[0018] The average gray value is calculated by the weight adjustment function to obtain a corresponding adjustment weight;

[0019] According to the adjustment weight, the parameters of the target filtering algorithm are adjusted.

[0020] In some embodiments, determining the average grayscale value of each pixel in the scanning positioning film includes:

[0021] Counting the grayscale value of each pixel in the scanning positioning film to obtain a grayscale histogram corresponding to the scanning positioning film;

[0022] A trimmed mean operation is performed on the grayscale value of each pixel according to the grayscale histogram to obtain an average grayscale value of each pixel.

[0023] In some of the embodiments, the target filtering algorithm includes a trimmed mean filtering algorithm and a curvature filtering algorithm.

[0024] In some embodiments, filtering the first projection image according to the adjusted parameters of the target filtering algorithm to obtain the corresponding target projection image includes:

[0025] According to the adjusted parameters of the trimmed mean filtering algorithm, filtering the first projection image to obtain a corresponding second projection image;

[0026] The second projection image is filtered according to the adjusted parameters of the curvature filtering algorithm to obtain the target projection image.

[0027] In some embodiments, reconstructing the target projection image to obtain the target image includes:

[0028] The target projection image is reconstructed by a filtered back-projection algorithm to obtain the target image.

[0029] In a second aspect, a CT image denoising device based on adaptive filtering is provided in this embodiment, the device comprising: a construction module, an update module, a filtering module and a reconstruction module;

[0030] The construction module is used to construct a corresponding weight adjustment function based on the noise information received by the detector under different doses of X-rays;

[0031] The updating module is used to adjust the parameters of the target filtering algorithm according to the weight adjustment function;

[0032] The filtering module is used to obtain a plurality of first projection images corresponding to the target image, and filter the first projection images according to the adjusted parameters of the target filtering algorithm to obtain the corresponding target projection images;

[0033] The reconstruction module is used to reconstruct the target projection image to obtain the target image.

[0034] In a third aspect, a computer device is provided in this embodiment, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the CT image denoising method based on adaptive filtering described in the first aspect is implemented.

[0035] In a fourth aspect, in this embodiment, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the CT image denoising method based on adaptive filtering described in the first aspect is implemented.

[0036] Compared with the related art, the CT image denoising method, device and computer equipment based on adaptive filtering provided in this embodiment construct a corresponding weight adjustment function based on the noise information received by the detector under different doses of X-rays; adjust the parameters of the target filtering algorithm according to the weight adjustment function; obtain multiple first projection images corresponding to the target image, and filter the first projection images according to the adjusted parameters of the target filtering algorithm to obtain the corresponding target projection image; reconstruct the target projection image to obtain the target image, which solves the problem that the filtering algorithm parameters cannot be adjusted dynamically, realizes the dynamic adjustment of the filtering algorithm parameters, and effectively improves the image denoising effect.

[0037] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0039] Figure 1 It is a hardware structure block diagram of a CT image denoising method based on adaptive filtering provided in an embodiment of the present application;

[0040] Figure 2 is a flow chart of a CT image denoising method based on adaptive filtering provided in one embodiment of the present application;

[0041] Figure 3 It is a flowchart of a CT image de-interpretation method based on adaptive filtering provided in one embodiment of the present application;

[0042] Figure 4 It is a flow chart of a CT image denoising method based on adaptive filtering provided in a preferred embodiment of the present application;

[0043] Figure 5 It is a structural block diagram of a CT image denoising device based on adaptive filtering provided in one embodiment of the present application.

[0044] In the figure: 102, processor; 104, memory; 106, transmission device; 108, input and output device; 10, construction module; 20, update module; 30, filtering module; 40, reconstruction module. DETAILED DESCRIPTION

[0045] In order to more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0046] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the general meaning understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the", "these" and the like in this application do not represent quantitative restrictions, and they can be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusions; for example, a process, method and system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether directly or indirectly. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. Usually, the character " / " indicates that the objects associated with each other are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0047] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 FIG. 4 is a hardware structure block diagram of a terminal of the CT image denoising method based on adaptive filtering in this embodiment. Figure 1 As shown, the terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 and memory 104 for storing data, wherein processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0048] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the CT image denoising method based on adaptive filtering in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the terminal via a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0049] The transmission device 106 is used to receive or send data via a network. The above network includes a wireless network provided by the communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0050] In this embodiment, a CT image denoising method based on adaptive filtering is provided. Figure 2 is a flow chart of the CT image denoising method based on adaptive filtering in this embodiment. Figure 2 As shown, the process includes the following steps:

[0051] Step S210: construct a corresponding weight adjustment function based on the noise information received by the detector under different doses of X-rays.

[0052] Specifically, in the current CT system, the X-ray incident dose is controlled to increase at uniform intervals, multiple scans are performed based on different incident dose values, and the received grayscale value and signal variance value corresponding to each dose are determined based on the noise information received by the detector each time. Among them, the main source of noise information is the statistical fluctuation of X-ray photons, that is, the transient distribution of photons in space, which is quantum noise. In addition, it also includes non-quantum noise such as electronic noise in the detector photodiode, and the specific noise situation can be reflected by the signal variance value.

[0053] Furthermore, a fitting curve of the received grayscale value and the signal variance value is constructed. According to the expression corresponding to the fitting curve, the minimum received grayscale value is calculated to obtain the corresponding signal variance value, and the expression corresponding to the fitting curve is adjusted according to the signal variance value to obtain the final weight adjustment function.

[0054] Step S220, adjusting the parameters of the target filtering algorithm according to the weight adjustment function.

[0055] It should be noted that the weight adjustment function in this embodiment is used to calculate the adjustment weight corresponding to each actual scanning process. Based on the adjustment weight, the adjustment weight of each target filtering algorithm is calculated respectively to adaptively adjust the parameters of each target filtering algorithm.

[0056] Step S230 , obtaining a plurality of first projection images corresponding to the target image, and filtering the first projection images according to the adjusted parameters of the target filtering algorithm to obtain corresponding target projection images.

[0057] Specifically, a plurality of first projection images are acquired during the actual scanning process, and the first projection images are filtered in sequence by a target filtering algorithm after parameter adjustment to obtain corresponding target projection images for reconstructing the target image.

[0058] Step S240: reconstruct the target projection image to obtain a target image.

[0059] Specifically, based on different CT systems, the target projection image can be reconstructed by a corresponding CT image reconstruction algorithm to obtain a target image, wherein the CT image reconstruction algorithm includes but is not limited to a direct Fourier reconstruction algorithm, a filtered back projection reconstruction algorithm, and a direct back projection reconstruction algorithm.

[0060] The current image denoising method obtains the corresponding image projection data according to the detection results of the detector, performs a one-dimensional convolution filter on the image projection data, and reconstructs the image according to the filtering results to obtain the corresponding target image, thereby reducing image noise. However, in the above method, it is impossible to dynamically adjust the parameters of the filtering algorithm, which may lead to insufficient filtering strength or excessive filtering, resulting in poor image denoising effect. On the basis of the prior art, the present application adaptively adjusts the parameters of the filtering algorithm according to the weight adjustment function, determines the optimal filtering parameters of the algorithm, and filters the projection image through different filtering algorithms after parameter adjustment. While retaining the image details, it effectively removes various noises in the projection image and significantly improves the image quality, so that an accurate target image can be reconstructed based on the processed projection image.

[0061] Specifically, a corresponding weight adjustment function is constructed based on the noise information received by the detector under different doses of X-rays; the parameters of the target filtering algorithm are adjusted according to the weight adjustment function; multiple first projection images corresponding to the target image are obtained, and the first projection images are filtered according to the adjusted parameters of the target filtering algorithm to obtain the corresponding target projection image; the target projection image is reconstructed to obtain the target image, which solves the problem that the filtering algorithm parameters cannot be adjusted dynamically, realizes the dynamic adjustment of the filtering algorithm parameters, and effectively improves the image denoising effect.

[0062] In some of the embodiments, based on the noise information received by the detector under different doses of X-rays, a corresponding weight adjustment function is constructed, including:

[0063] Step S211, based on the noise information received by the detector under different doses of X-rays, determining the received grayscale value and signal variance value corresponding to each dose;

[0064] Step S212, performing curve fitting on the received grayscale value and the signal variance value to obtain a corresponding fitting curve;

[0065] Step S213: determining a weight adjustment function according to the fitting curve.

[0066] Specifically, under a fixed X-ray tube voltage, the X-ray tube current is increased according to a preset current interval, that is, the detector incident dose is increased, and scanning is performed based on incident doses of different values ​​each time. During each scanning process, the noise information received by the detector is obtained.

[0067] It should be noted that, based on the above noise information, the received grayscale value and signal variance value corresponding to each incident dose can be determined. The received grayscale value is the grayscale value of the digitized image generated on the detector side, and the received grayscale value is usually linearly related to the incident dose.

[0068] Furthermore, the detector incident dose is converted into the corresponding receiving grayscale value, and the receiving grayscale value is used as the horizontal coordinate and the signal variance value is used as the vertical coordinate to construct the corresponding fitting curve. The specific expression is as follows:

[0069] y=a*x 3 +b*x 2 +c;

[0070] Where x is the received grayscale value, y is the signal variance value, and a, b, and c are all fitting curve coefficients, which can be determined according to the actual application. After obtaining the fitting relationship between the received grayscale value and the signal variance value, the minimum received grayscale value x min Input into the above fitting curve to get the corresponding signal variance value y max, and then determine the weight adjustment function ω according to the fitting curve to adjust the parameters of the target filtering algorithm. The specific expression is as follows:

[0071] ω=(a*x 3 +b*x 2 +c) / y max .

[0072] Through this embodiment, based on the noise information received by the detector under different doses of X-rays, the received grayscale value and signal variance value corresponding to each dose are determined, and curve fitting is performed on the received grayscale value and signal variance value to obtain the corresponding fitting curve, and the weight adjustment function is determined according to the fitting curve, so that the parameters of the target filtering algorithm can be adaptively adjusted through the weight adjustment function.

[0073] In some of the embodiments, adjusting the parameters of the target filtering algorithm according to the weight adjustment function includes:

[0074] Step S221, obtaining a scanning positioning slice corresponding to the target image;

[0075] Step S222, determining the average gray value of each pixel in the scanned positioning film;

[0076] Step S223, calculating the average gray value through a weight adjustment function to obtain a corresponding adjustment weight;

[0077] Step S224, adjusting the parameters of the target filtering algorithm according to the adjustment weights.

[0078] Specifically, obtain the scanning localizer during the actual patient scanning process, and calculate the average gray value x corresponding to each pixel point in the scanning localizer. 1 . The average gray value x is adjusted by the weight adjustment function 1 Perform calculations to obtain the corresponding adjustment weight ω 1 =(a*x 1 3 +b*x 1 2 +c) / y max .

[0079] Further, according to the adjustment weight ω 1 , determine the adjustment weights of different target filtering algorithms. For example, the adjustment weight of the parameters in the tail-cut mean filtering algorithm is ω 1 , and the adjustment weight of the parameters in the curvature filtering algorithm is 1-ω 1 , and according to the final adjustment weights, adjust the parameters of its own filtering algorithm to determine the optimal filtering parameters of the algorithm.

[0080] Through this embodiment, the average grayscale value of the patient's scan positioning film is obtained, the average grayscale value is calculated through the weight adjustment function to obtain the corresponding adjustment weight, and according to the adjustment weight, the parameters of the target filtering algorithm are adjusted to obtain the optimal filtering parameters, so that during the image filtering process, the noise in the projection image can be effectively removed, thereby greatly improving the image quality.

[0081] In some embodiments, determining the average gray value of each pixel in the scanning positioning film includes:

[0082] The grayscale value of each pixel in the scanning positioning film is counted to obtain a grayscale histogram corresponding to the scanning positioning film;

[0083] According to the grayscale histogram, the grayscale value of each pixel is trimmed and averaged to obtain the average grayscale value of each pixel.

[0084] Specifically, the grayscale value of each pixel in the scanning positioning film is counted, and a grayscale histogram corresponding to the scanning positioning film is generated according to the statistical results. According to the grayscale histogram, the grayscale value of each pixel is subjected to tail-cutting mean operation, that is, the extreme values ​​distributed at both ends of the grayscale histogram are removed, and the grayscale values ​​of the reserved area are averaged to obtain the average grayscale value corresponding to each pixel.

[0085] It is important to know that the grayscale histogram is a function of grayscale distribution, which describes the number of pixels of each grayscale level in the image and clearly reflects the frequency of occurrence of each grayscale. Therefore, in the grayscale histogram of the scanning positioning film, the extreme values ​​at both ends of the distribution can be intuitively determined, and the number of occurrences of each grayscale value in the reserved area can be obtained to facilitate the calculation of the average grayscale value.

[0086] Through this embodiment, the grayscale value of each pixel in the scanned positioning film is statistically analyzed to obtain a grayscale histogram corresponding to the scanned positioning film, and the grayscale value of each pixel is subjected to a trimmed mean operation according to the grayscale histogram to obtain the average grayscale value of each pixel, thereby avoiding the influence of extreme values ​​on the average value and improving the accuracy of the average grayscale value.

[0087] In some of the embodiments, the target filtering algorithm includes a trimmed mean filtering algorithm and a curvature filtering algorithm.

[0088] It should be noted that the trimmed mean filtering algorithm can remove Poisson noise under low signal intensity, and its algorithm parameters include the maximum window width parameter β and the gray threshold parameter λ; among which, the preferred value of β is 15, and the preferred value of λ is 1125; while the curvature filtering algorithm can effectively remove Gaussian noise in the image, and its algorithm parameters include the number of iterations I and the step size control parameter α; among which, the preferred value of I is 6, and the preferred value of α is 0.8.

[0089] Specifically, when processing an image through the trimmed mean filtering algorithm, the trimmed mean of the neighborhood pixels of each pixel is calculated to replace the original pixel value of the pixel, where the filter window used to determine the range of the neighborhood pixels is affected by the algorithm parameters; and when using the curvature filtering algorithm, it is necessary to iteratively update the average curvature of the image by calculating the second-order partial derivative of the pixel, and the final average curvature is affected by the algorithm parameters. Therefore, when the filtering algorithm parameters are fixed, it is easy for some image data to have insufficient filtering strength or excessive filtering. In this case, it is necessary to adjust the filtering algorithm parameters according to the actual scanning situation to avoid poor denoising effect or excessive damage to the details of the image.

[0090] Through this embodiment, the tail-cut mean filtering algorithm and the curvature filtering algorithm are used to filter the projection image in sequence, thereby effectively removing Poisson noise and Gaussian noise while retaining image details, thereby significantly improving image quality.

[0091] In some of the embodiments, filtering the first projection image according to the adjusted parameters of the target filtering algorithm to obtain the corresponding target projection image includes:

[0092] Step S231, filtering the first projection image according to the adjusted parameters of the trimmed mean filtering algorithm to obtain a corresponding second projection image;

[0093] Step S232: filtering the second projection image according to the adjusted parameters of the curvature filtering algorithm to obtain a target projection image.

[0094] Specifically, according to the calculated adjustment weight ω 1 , determine the adjustment weights of different target filtering algorithms. Among them, the adjustment weight of the parameters in the tail-cut mean filtering algorithm is ω 1 , the adjustment weight of the parameters in the curvature filtering algorithm is 1-ω 1 .

[0095] Furthermore, the parameters of the tail-cut mean filtering algorithm are adjusted to obtain the optimal filtering parameter β of the algorithm. 1 =β*ω 1 and λ 1 =λ*ω 1 , based on the adjusted parameters, the first projection image p i, Perform filtering to obtain the corresponding second projection image p i, ′, that is, p i, ′=Filter atmf (p i, , β 1 ,λ 1 ); And adjust the parameters of the curvature filtering algorithm to obtain the optimal filtering parameters of the curvature filtering algorithm I 1=I*(1-ω 1 ) and α 1 =α*(1-ω 1 ), based on the adjusted parameters, the second projection image p i, 'Perform curvature filtering to obtain the target projection image p i, ″, that is, p i, ″=Filter cf (p i, ′,I 1 , α 1 ).

[0096] Through this embodiment, the projection image is processed based on the tail-cut mean filtering algorithm with adjusted parameters, which effectively removes Poisson noise streak artifacts and avoids insufficient filtering intensity or excessive filtering. At the same time, the curvature filtering algorithm is applied to better retain the high-frequency details of the image and remove Gaussian noise in the image, thereby significantly improving the image quality.

[0097] In some embodiments, reconstructing the target projection image to obtain the target image includes:

[0098] Step S241, reconstructing the target projection image by using a filtered back projection algorithm to obtain a target image.

[0099] It should be noted that after the filtering process of the projection image is completed, the target projection image can be reconstructed by the filtered back projection algorithm to obtain the target image. For cone beam CT, the FDK (Feldkamp-Davis-Kress) reconstruction algorithm can be used to achieve the reconstruction of the target image.

[0100] Specifically, the target projection image is preliminarily subjected to distance weighted correction, and the weighted corrected projection data is subjected to ramp filtering to obtain corresponding filtered projection data, so that a back-projection operation can be performed on the filtered projection data to reconstruct the target image.

[0101] Through this embodiment, the target projection image is reconstructed using the filtered back projection algorithm, so that an accurate target image is reconstructed and generated based on the projection image after filtering.

[0102] Figure 3 FIG. 1 is a flow chart of the CT image denoising method based on adaptive filtering in this embodiment. Figure 3 As shown in the figure, the CT image denoising method based on adaptive filtering has the following specific process:

[0103] In the current CT system, based on the noise information received by the detector under different doses of X-rays, the received grayscale value and signal variance value corresponding to each dose are determined, and curve fitting is performed on the received grayscale value and signal variance value to obtain the corresponding fitting curve S310, and the weight adjustment function S320 is determined based on the fitting curve; the scanning positioning film in the actual patient scanning process is obtained, and the average grayscale value corresponding to each pixel point in the scanning positioning film is calculated S330, so that the average grayscale value is calculated through the weight adjustment function to obtain the corresponding adjustment weight.

[0104] Based on the above adjustment weights, the adjustment weights of the trimmed mean filtering algorithm and the curvature filtering algorithm are determined S340, and the corresponding algorithm parameters S350 are determined according to their respective adjustment weights; further, according to the adjusted parameters of the trimmed mean filtering algorithm, the first projection image is filtered to obtain the corresponding second projection image S360, and according to the adjusted parameters of the curvature filtering algorithm, the second projection image is filtered to obtain the target projection image S370, and finally FDK reconstruction is performed on the target projection image to obtain the target image S380.

[0105] The present embodiment is described and illustrated below through preferred embodiments.

[0106] Figure 4 is a flow chart of the CT image denoising method based on adaptive filtering of the preferred embodiment, such as Figure 4 As shown, the CT image denoising method based on adaptive filtering includes the following steps:

[0107] Step S410, based on the noise information received by the detector under different doses of X-rays, determining the received grayscale value and signal variance value corresponding to each dose;

[0108] Step S420, performing curve fitting on the received grayscale value and the signal variance value to obtain a corresponding fitting curve, and determining a weight adjustment function according to the fitting curve;

[0109] Step S430, obtaining a scanning localizer corresponding to the target image, and determining an average gray value of each pixel in the scanning localizer;

[0110] Step S440, calculating the average gray value through a weight adjustment function to obtain a corresponding adjustment weight;

[0111] Step S450, adjusting the parameters of the trimmed mean filter algorithm and the curvature filter algorithm according to the adjustment weight;

[0112] Step S460, filtering the first projection image according to the adjusted parameters of the trimmed mean filtering algorithm to obtain a corresponding second projection image;

[0113] Step S470, filtering the second projection image according to the adjusted parameters of the curvature filtering algorithm to obtain a target projection image;

[0114] Step S480: reconstruct the target projection image to obtain the target image.

[0115] Through this embodiment, based on the noise information received by the detector under different doses of X-rays, the received grayscale value and signal variance value corresponding to each dose are determined, and curve fitting is performed on the received grayscale value and signal variance value to obtain the corresponding fitting curve, so that the weight adjustment function can be determined according to the fitting curve; in the actual scanning process, the patient's scanning positioning film is obtained, the average grayscale value of each pixel point in the scanning positioning film is determined, and the average grayscale value is calculated through the weight adjustment function to obtain the corresponding adjustment weight, based on the adjustment weight, the adjustment weights of the trimmed mean filtering algorithm and the curvature filtering algorithm can be calculated respectively, so as to realize the parameter optimization of the two filtering algorithms, obtain the respective optimal filtering parameters, and avoid the subsequent occurrence of insufficient filtering intensity or excessive filtering.

[0116] According to the tail-cut mean filtering algorithm under the optimal filtering parameters, the first projection image is filtered to obtain the corresponding second projection image, which effectively removes the Poisson noise stripe artifacts; further, the second projection image is subjected to curvature filtering based on the adjusted algorithm parameters to obtain the target projection image, thereby retaining the high-frequency details of the image and removing the Gaussian noise in the image; based on this, an accurate target image is finally reconstructed.

[0117] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0118] In this embodiment, a CT image denoising device based on adaptive filtering is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made are not repeated here. The terms "module", "unit", "subunit", etc. used below can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0119] Figure 5 is a structural block diagram of the CT image denoising device based on adaptive filtering in this embodiment, as shown in Figure 5 As shown, the device includes: a construction module 10, an update module 20, a filtering module 30 and a reconstruction module 40;

[0120] A construction module 10 is used to construct a corresponding weight adjustment function based on the noise information received by the detector under different doses of X-rays;

[0121] An updating module 20, used to adjust the parameters of the target filtering algorithm according to the weight adjustment function;

[0122] A filtering module 30 is used to obtain a plurality of first projection images corresponding to the target image, and filter the first projection images according to the adjusted parameters of the target filtering algorithm to obtain the corresponding target projection images;

[0123] The reconstruction module 40 is used to reconstruct the target projection image to obtain the target image.

[0124] Through the device provided in this embodiment, a corresponding weight adjustment function is constructed based on the noise information received by the detector under different doses of X-rays; according to the weight adjustment function, the parameters of the target filtering algorithm are adjusted; multiple first projection images corresponding to the target image are obtained, and according to the adjusted parameters of the target filtering algorithm, the first projection images are filtered to obtain the corresponding target projection images; the target projection images are reconstructed to obtain the target image, which solves the problem that the filtering algorithm parameters cannot be adjusted dynamically, realizes the dynamic adjustment of the filtering algorithm parameters, and effectively improves the image denoising effect.

[0125] In some of these embodiments, Figure 5 On the basis of, the device also includes a fitting module, which is used to determine the received grayscale value and signal variance value corresponding to each dose based on the noise information received by the detector under different doses of X-rays; perform curve fitting on the received grayscale value and signal variance value to obtain the corresponding fitting curve; and determine the weight adjustment function according to the fitting curve.

[0126] In some of these embodiments, Figure 5 On the basis of, the device also includes an adjustment module, which is used to obtain a scanning positioning piece corresponding to the target image; determine the average grayscale value of each pixel in the scanning positioning piece; calculate the average grayscale value through a weight adjustment function to obtain a corresponding adjustment weight; and adjust the parameters of the target filtering algorithm according to the adjustment weight.

[0127] In some of these embodiments, Figure 5 On the basis of, the device also includes a calculation module, which is used to count the grayscale values ​​of each pixel in the scanning positioning film to obtain a grayscale histogram corresponding to the scanning positioning film; according to the grayscale histogram, a trimmed mean operation is performed on the grayscale value of each pixel to obtain an average grayscale value of each pixel.

[0128] In some of these embodiments, Figure 5On the basis of, the device also includes a processing module, which is used to filter the first projection image according to the adjusted parameters of the tail-cut mean filtering algorithm to obtain the corresponding second projection image; according to the adjusted parameters of the curvature filtering algorithm, the second projection image is filtered to obtain the target projection image.

[0129] In some of these embodiments, Figure 5 On the basis of, the device also includes a generation module, which is used to reconstruct the target projection image through a filtered back projection algorithm to obtain a target image.

[0130] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0131] In this embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0132] Optionally, the computer device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0133] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and will not be repeated in this embodiment.

[0134] In addition, in combination with the CT image denoising method based on adaptive filtering provided in the above embodiments, a storage medium may also be provided in this embodiment to implement the method. The storage medium stores a computer program; when the computer program is executed by a processor, any one of the CT image denoising methods based on adaptive filtering in the above embodiments is implemented.

[0135] It should be understood that the specific embodiments described herein are only used to explain the application, rather than to limit it. Based on the embodiments provided in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of this application.

[0136] Obviously, the drawings are only some examples or embodiments of the present application. For ordinary technicians in the field, the present application can also be applied to other similar situations based on these drawings without creative work. In addition, it is understandable that although the work done in this development process may be complicated and lengthy, for ordinary technicians in the field, certain changes in design, manufacturing or production based on the technical content disclosed in this application are only conventional technical means and should not be regarded as insufficient content disclosed in this application.

[0137] The term "embodiment" in this application refers to a specific feature, structure or characteristic described in conjunction with the embodiment that can be included in at least one embodiment of the present application. The appearance of this phrase in various locations in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. It is clearly or implicitly understood by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.

[0138] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of patent protection. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the attached claims.

Claims

1. A CT image denoising method based on adaptive filtering, characterized in that: The method comprises: Based on the noise information received by the detector under different doses of X-rays, a corresponding weight adjustment function is constructed; According to the weight adjustment function, adjusting the parameters of the target filtering algorithm; Acquire a plurality of first projection images corresponding to the target image, and perform filtering processing on the first projection images according to the adjusted parameters of the target filtering algorithm to obtain corresponding target projection images; The target projection image is reconstructed to obtain the target image.

2. The CT image denoising method based on adaptive filtering according to claim 1, characterized in that: The method of constructing a corresponding weight adjustment function based on the noise information received by the detector under different doses of X-rays includes: Based on the noise information received by the detector under different doses of X-rays, determining the received grayscale value and signal variance value corresponding to each dose; Performing curve fitting on the received grayscale value and the signal variance value to obtain a corresponding fitting curve; The weight adjustment function is determined according to the fitting curve.

3. The CT image denoising method based on adaptive filtering according to claim 1, characterized in that: The step of adjusting the parameters of the target filtering algorithm according to the weight adjustment function includes: Obtaining a scanning positioning piece corresponding to the target image; Determine the average gray value of each pixel in the scanning positioning film; The average gray value is calculated by the weight adjustment function to obtain a corresponding adjustment weight; According to the adjustment weight, the parameters of the target filtering algorithm are adjusted.

4. The CT image denoising method based on adaptive filtering according to claim 3, characterized in that: Determining the average grayscale value of each pixel in the scanning positioning film includes: Counting the grayscale value of each pixel in the scanning positioning film to obtain a grayscale histogram corresponding to the scanning positioning film; A trimmed mean operation is performed on the grayscale value of each pixel according to the grayscale histogram to obtain an average grayscale value of each pixel.

5. The CT image denoising method based on adaptive filtering according to claim 3, characterized in that: The target filtering algorithm includes a trimmed mean filtering algorithm and a curvature filtering algorithm.

6. The CT image denoising method based on adaptive filtering according to claim 5, characterized in that: The filtering process is performed on the first projection image according to the adjusted parameters of the target filtering algorithm to obtain a corresponding target projection image, including: According to the adjusted parameters of the trimmed mean filtering algorithm, filtering the first projection image to obtain a corresponding second projection image; The second projection image is filtered according to the adjusted parameters of the curvature filtering algorithm to obtain the target projection image.

7. The CT image denoising method based on adaptive filtering according to claim 1, characterized in that: The step of reconstructing the target projection image to obtain the target image includes: The target projection image is reconstructed by a filtered back-projection algorithm to obtain the target image.

8. A CT image denoising device based on adaptive filtering, characterized in that: The device comprises: a construction module, an update module, a filtering module and a reconstruction module; The construction module is used to construct a corresponding weight adjustment function based on the noise information received by the detector under different doses of X-rays; The updating module is used to adjust the parameters of the target filtering algorithm according to the weight adjustment function; The filtering module is used to obtain a plurality of first projection images corresponding to the target image, and filter the first projection images according to the adjusted parameters of the target filtering algorithm to obtain the corresponding target projection images; The reconstruction module is used to reconstruct the target projection image to obtain the target image.

9. A computer device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps of the CT image denoising method based on adaptive filtering according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the CT image denoising method based on adaptive filtering described in any one of claims 1 to 7 are implemented.

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