CN113240762A - Image processing method and device and electronic equipment - Google Patents

Image processing method and device and electronic equipment Download PDF

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CN113240762A
CN113240762A CN202110369034.8A CN202110369034A CN113240762A CN 113240762 A CN113240762 A CN 113240762A CN 202110369034 A CN202110369034 A CN 202110369034A CN 113240762 A CN113240762 A CN 113240762A
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程景烨
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Neusoft Medical Systems Co Ltd
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Shenyang Advanced Medical Equipment Technology Incubation Center Co ltd
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Abstract

The embodiment of the invention provides an image processing method and device and electronic equipment. The method comprises the steps of conducting guiding filtering on a second image by taking a first image as a guiding image to obtain a filtering image, wherein the first image and the second image are two images obtained by reconstructing the same spiral cone beam CT scanning data by using the same reconstruction algorithm and different reconstruction parameter values; the cone angle artifact of the second image is lighter than that of the first image, and a denoising error image which contains the cone angle artifact and is free of noise is obtained according to the first image and the filtering image; and obtaining a target image according to the first image and the denoising error image, so that cone angle artifacts in the spiral cone beam CT image can be quickly and effectively removed, and the image quality of the spiral cone beam CT image is improved.

Description

Image processing method and device and electronic equipment
Technical Field
The present invention relates to the field of medical image processing technologies, and in particular, to an image processing method and apparatus, and an electronic device.
Background
Helical cone-beam CT (Computed Tomography) is an important imaging modality. For helical cone beam CT scan data, reconstruction is typically performed using an approximately analytical reconstruction algorithm. However, the use of an approximate analytical reconstruction algorithm can cause severe artifacts. As the cone angle increases, the artifact becomes more severe, and thus such artifact is referred to as a cone angle artifact.
The quality of the helical cone beam CT image is seriously affected by the existence of the cone angle artifact, so that the cone angle artifact needs to be removed to improve the quality of the helical cone beam CT image
Disclosure of Invention
In order to overcome the problems in the related art, the invention provides an image processing method, an image processing device and electronic equipment, which can quickly remove cone angle artifacts in a helical cone beam CT image and improve the image quality.
According to a first aspect of embodiments of the present invention, there is provided an image processing method including:
taking the first image as a guide image, and conducting guide filtering on the second image to obtain a filtering image; the first image and the second image are two images obtained by reconstructing the same spiral cone beam CT scanning data by using the same reconstruction algorithm and different reconstruction parameter values; the cone angle artifact of the second image is lighter than the cone angle artifact of the first image;
obtaining a denoising error image which contains cone angle artifacts and is free of noise according to the first image and the filtering image;
and obtaining a target image according to the first image and the denoising error image.
Illustratively, obtaining a noise-removed error image including cone angle artifacts and removing noise from the first image and the filtered image includes:
subtracting the filtered image from the first image to obtain an error image;
and denoising the error image to obtain a denoised error image.
Illustratively, obtaining a target image from the first image and the denoising error image includes:
obtaining a correction error image according to the correction coefficient value and the denoising error image;
and subtracting the corrected error image from the first image to obtain a target image.
Illustratively, the manner of obtaining the correction coefficient value includes:
receiving a correction coefficient value input by a user;
or reading the default correction coefficient value of the system.
For example, before the first image is used as a guide image and the second image is subjected to guide filtering to obtain a filtered image, the method further includes:
reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a first reconstruction parameter value to obtain a first image;
reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a second reconstruction parameter value to obtain a second image;
and the parameters corresponding to the first reconstruction parameter value and the second reconstruction parameter value are cone angle weight parameters in a weighted filtering back projection algorithm.
According to a second aspect of the embodiments of the present invention, there is provided an image processing apparatus including:
the guide filtering module is used for performing guide filtering on the second image by taking the first image as a guide image to obtain a filtering image; the first image and the second image are two images obtained by reconstructing the same spiral cone beam CT scanning data by using the same reconstruction algorithm and different reconstruction parameter values; the cone angle artifact of the second image is lighter than the cone angle artifact of the first image;
the denoising error image acquisition module is used for acquiring a denoising error image which contains cone angle artifacts and is used for removing noise according to the first image and the filtering image;
and the target image acquisition module is used for acquiring a target image according to the first image and the denoising error image.
Illustratively, the denoising error image obtaining module may be specifically configured to:
subtracting the filtered image from the first image to obtain an error image;
and denoising the error image to obtain a denoised error image.
Illustratively, the target image acquisition module is specifically configured to:
obtaining a correction error image according to the correction coefficient value and the denoising error image;
and subtracting the corrected error image from the first image to obtain a target image.
Illustratively, the manner of obtaining the correction coefficient value includes:
receiving a correction coefficient value input by a user;
or reading the default correction coefficient value of the system.
Exemplarily, the method further comprises the following steps:
the first reconstruction module is used for reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a first reconstruction parameter value to obtain a first image;
the second reconstruction module is used for reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a second reconstruction parameter value to obtain a second image;
and the parameters corresponding to the first reconstruction parameter value and the second reconstruction parameter value are cone angle weight parameters in a weighted filtering back projection algorithm.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
according to the method, a first image is used as a guide image, a second image is subjected to guide filtering to obtain a filtering image, and the first image and the second image are two images obtained by reconstructing the same spiral cone beam CT scanning data by using the same reconstruction algorithm and different reconstruction parameter values; the cone angle artifact of the second image is lighter than that of the first image, and a denoising error image which contains the cone angle artifact and is free of noise is obtained according to the first image and the filtering image; and obtaining a target image according to the first image and the denoising error image, so that cone angle artifacts in the spiral cone beam CT image can be quickly and effectively removed, and the image quality of the spiral cone beam CT image is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the specification.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present specification and together with the description, serve to explain the principles of the specification.
Fig. 1 is a flowchart illustrating an image processing method according to an embodiment of the present invention.
Fig. 2 is a functional block diagram of an image processing apparatus according to an embodiment of the present invention.
Fig. 3 is a hardware structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of embodiments of the invention, as detailed in the following claims.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of embodiments of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used to describe various information in embodiments of the present invention, the information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, the first information may also be referred to as second information, and similarly, the second information may also be referred to as first information, without departing from the scope of embodiments of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
In the related art, an algorithm based on sinusoidal domain data processing is used to suppress or reduce cone angle artifacts. The principle of this algorithm is:
(1) reconstructing raw data (namely helical cone beam CT scanning data) by using an approximate analytical reconstruction algorithm to obtain an original image;
(2) calculating to obtain a sine-domain simulation projection by using a Forward projection (Forward Project)) for the reconstructed original image;
(3) subtracting the sinusoidal domain generated data projection from the sinusoidal domain simulated projection to obtain a sinusoidal domain error projection;
(4) reconstructing the sinusoidal domain error projection to obtain an error image;
(5) the error image is subtracted from the original image to obtain a modified image with reduced cone angle artifacts.
And (5) replacing the original image with the corrected image, and iteratively executing the steps (2) to (5).
Calculating the forward projection is a relatively time-consuming process, and is slow because the related art needs to calculate the forward projection for the same number of iterations.
The embodiment of the invention provides an image processing method capable of quickly removing cone angle artifacts.
The image processing method of the present invention will be described in detail below with reference to examples.
Fig. 1 is a flowchart illustrating an image processing method according to an embodiment of the present invention. As shown in fig. 1, in this embodiment, the image processing method may include:
s101, taking the first image as a guide image, and conducting guide filtering on the second image to obtain a filtering image; the first image and the second image are two images obtained by reconstructing the same spiral cone beam CT scanning data by using the same reconstruction algorithm and different reconstruction parameter values; the cone angle artifacts of the second image are lighter than the cone angle artifacts of the first image.
S102, obtaining a denoising error image which contains cone angle artifacts and is free of noise according to the first image and the filtering image.
S103, obtaining a target image according to the first image and the denoising error image.
By the guide filtering, the image can be smoothed, and meanwhile edge information of the image can be well maintained. The obtained filtered image well retains useful information of the image, provides a basis for obtaining an accurate denoising error image in the subsequent step S102, and simultaneously, the target image obtained in the step S103 has higher quality.
It should be noted that the first image and the second image are obtained by reconstructing the same helical cone beam CT scan data, and the same reconstruction algorithm is used in the reconstruction process, and the same reconstruction parameters are used, but the values of the reconstruction parameters are different.
Wherein the cone angle artifacts of the second image are lighter than the cone angle artifacts of the first image, but the signal-to-noise ratio of the first image is higher than the signal-to-noise ratio of the second image.
In an exemplary implementation process, before step S101, the method may further include:
reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a first reconstruction parameter value to obtain a first image;
reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a second reconstruction parameter value to obtain a second image;
and the parameters corresponding to the first reconstruction parameter value and the second reconstruction parameter value are cone angle weight parameters in a weighted filtering back projection algorithm.
In this embodiment, the weighted filtering back projection algorithm is one of approximate analytical reconstruction algorithms.
The weighted filtered back-projection algorithm needs to weight the sine-domain projection before the filtered back-projection, the weighting direction is the detector layer direction to suppress the cone angle artifact introduced during the back-projection, and the weighting function can be in the following formula (1), but is not limited thereto.
Figure BDA0003008572030000061
In equation (1), q represents the detector layer index parameter, Pitch represents the Pitch, SliceNum represents the number of detector layers, and MidSlice represents the detector middle layer.
In equation (1), the parameter Q ∈ (0, 1), referred to herein as the cone angle weight parameter in the weighted filtered backprojection algorithm, the larger Q, the higher the signal-to-noise ratio of the reconstructed image, but the larger the cone angle artifact, and the smaller Q, the lower the signal-to-noise ratio of the reconstructed image, but the smaller the cone angle artifact.
Thus, the first image can be reconstructed separately (with f) by adjusting the parameter QgRepresented) and a second image (with f)nRepresentation). Wherein the first image fgThe corresponding parameter Q has a value greater than the second image fnThe value of the corresponding parameter Q.
In practical applications, the first image f may be set by a usergCorresponding values of the parameter Q and the second image fnThe value of the corresponding parameter Q. In another way, the first image f may also be determined empiricallygCorresponding values of the parameter Q and the second image fnThe corresponding parameter Q is stored in the system as a default value, and the default value in the system is directly adopted to determine the first image f when in usegCorresponding values of the parameter Q and the second image fnThe value of the corresponding parameter Q.
It should be noted that, although the weighted filtered back-projection algorithm is used to illustrate how to obtain the first image and the second image, those skilled in the art will understand that the approximate analytic reconstruction algorithm is not limited to the weighted filtered back-projection algorithm, and other approximate analytic reconstruction algorithms meeting the condition may also be used, where the condition is: after the same helical cone beam CT scanning data are reconstructed by adopting the same reconstruction algorithm and different reconstruction parameter values, a first image with high signal-to-noise ratio and serious cone angle artifact and a second image with low signal-to-noise ratio and little cone angle artifact or no cone angle are obtained.
In an exemplary implementation, obtaining a denoised error image containing cone angle artifacts and removing noise according to the first image and the filtered image may include:
subtracting the filtered image from the first image to obtain an error image;
and denoising the error image to obtain a denoised error image.
For example, with the aforementioned first image fgFor the guide image, the second image fnObtaining a filtered image after the guiding filtering
Figure BDA0003008572030000071
Then, according to the formula
Figure BDA0003008572030000072
Calculating to obtain an error image fe. Since the guided filtering is a local linear model, the image is filtered
Figure BDA0003008572030000073
And a first image fgWith similar gradients, so the error image feHas very small edge errors, approximately containing only noise and cone angle artifacts.
Since our purpose is to derive from the first image fgIn order to remove cone angle artifacts, it is desirable to obtain an image that contains only cone angle artifacts. However, the error image feNot only cone angle artifacts but also noise. Therefore, it is necessary to extract the error image f from the error imageeRemoving the noise. Since the cone angle artifacts are low frequency, the error image f can be correctedeDenoising error image f obtained after low-pass filtering denoising is carried oute *Only cone angle artifacts are included.
In an exemplary implementation process, in step S103, obtaining a target image according to the first image and the denoising error image may include:
obtaining a correction error image according to the correction coefficient value and the denoising error image;
and subtracting the corrected error image from the first image to obtain a target image.
Wherein the correction factor is denoted herein by α.
The de-noised error image obtained by the method
Figure BDA0003008572030000081
Only cone angle artifact is included, and a de-noised error image is obtained
Figure BDA0003008572030000082
Then, the error image is denoised through the correction coefficient alpha
Figure BDA0003008572030000083
Multiplying to obtain a corrected error image
Figure BDA0003008572030000084
Then by the formula
Figure BDA0003008572030000085
Obtaining the target image with cone angle artifact removed
Figure BDA0003008572030000086
In practical applications, values of the first reconstruction parameter value and the second reconstruction parameter value may be not optimal, so that the obtained denoising error image has too many or too few cone angle artifacts. The cone angle artifacts in the denoised error image are excessive, namely the cone angle artifacts contained in the denoised error image are more than the cone angle artifacts actually contained in the first image; too few cone angle artifacts in the denoised error image means that the denoised error image contains fewer cone angle artifacts than actually contained in the first image. In either case, the effect of the de-cone angle artifact of the first image is reduced. To improve the effect of the de-cone angle artifact of the first image, the present embodiment refers to a correction coefficient α. And correcting the de-noising error image by using the correction coefficient alpha, so that the cone angle artifact contained in the corrected error image obtained after correction is approximate to the cone angle artifact contained in the first image, and the effect of removing the cone angle artifact of the first image is improved.
For example, when de-noising an error image
Figure BDA0003008572030000087
Cone angle artifacts contained therein are larger than the first image fgWhen the cone angle artifact is much, the correction coefficient alpha can be less than 1 to reduce the de-noising error image
Figure BDA0003008572030000088
Cone angle artifacts contained in, so that the de-noised error image
Figure BDA0003008572030000089
Cone angle artifacts and first image f contained thereingThe cone angle artifacts actually contained in (a) are more similar. Similarly, when denoising the error image
Figure BDA00030085720300000810
Cone angle artifacts contained therein are larger than the first image fgWhen the cone angle artifact actually contained in the image is less, the correction coefficient alpha can be a value larger than 1 so as to increase the de-noising error image
Figure BDA00030085720300000811
Cone angle artifacts contained therein, also in order to de-noise the error image
Figure BDA00030085720300000812
Cone angle artifacts and first image f contained thereingThe cone angle artifacts actually contained in (a) are more similar. Thus, the correction coefficient alpha is used for denoising the error image
Figure BDA00030085720300000813
After the correction, the cone angle artifact and the first image f can be obtainedgThe cone angle artifact actually contained in the image is closer to the corrected error image, so that the target image obtained by subtracting the corrected error image from the first image has a good cone angle artifact removing effect.
In an exemplary implementation, the obtaining of the correction coefficient value includes:
receiving a correction coefficient value input by a user;
or reading the default correction coefficient value of the system.
In practical applications, different scenes have different requirements on image quality, the value of the correction coefficient α cannot be specified by a uniform standard, and at this time, the value of the correction coefficient α can be determined by a user according to needs, so that the value of the correction coefficient α can be acquired by receiving a correction coefficient value input by the user. Alternatively, values of a plurality of correction coefficients α may be input, the cone angle artifact removal effects corresponding to the values of the correction coefficients α may be compared, and the most effective value may be selected as the finally input correction coefficient value.
In an exemplary implementation, when the first reconstruction parameter value and the second reconstruction parameter value adopt default values of a system, a correction coefficient value capable of achieving a better cone angle artifact removal effect may be empirically determined and stored as a default correction coefficient value of the system, and the default correction coefficient value is directly adopted as the correction coefficient value when the correction coefficient value is used.
As can be seen from the foregoing embodiments of the present invention, the image processing method of this embodiment completes the removal of the cone angle artifact in the image domain, and does not need to perform forward projection in the sine domain, and each step in the image processing method of this embodiment is performed only once, and does not need to be performed iteratively, so that the image processing method of this embodiment has a faster processing speed than the foregoing related art.
According to the image processing method provided by the embodiment of the invention, a first image is taken as a guide image, and guide filtering is carried out on a second image to obtain a filtering image, wherein the first image and the second image are two images obtained by reconstructing the same spiral cone beam CT scanning data by using the same reconstruction algorithm and different reconstruction parameter values; the cone angle artifact of the second image is lighter than that of the first image, and a denoising error image which contains the cone angle artifact and is free of noise is obtained according to the first image and the filtering image; and obtaining a target image according to the first image and the denoising error image, so that cone angle artifacts in the spiral cone beam CT image can be quickly and effectively removed, and the image quality of the spiral cone beam CT image is improved.
Based on the above method embodiment, the embodiment of the present invention further provides corresponding apparatus, device, and storage medium embodiments.
Fig. 2 is a functional block diagram of an image processing apparatus according to an embodiment of the present invention. As shown in fig. 2, in the present embodiment, the image processing apparatus may include:
the guiding filtering module 210 is configured to guide and filter the second image by using the first image as a guiding image to obtain a filtered image; the first image and the second image are two images obtained by reconstructing the same spiral cone beam CT scanning data by using the same reconstruction algorithm and different reconstruction parameter values; the cone angle artifact of the second image is lighter than the cone angle artifact of the first image;
a denoising error image obtaining module 220, configured to obtain a denoising error image including a cone angle artifact and having noise removed according to the first image and the filtered image;
and a target image obtaining module 230, configured to obtain a target image according to the first image and the denoising error image.
In an exemplary implementation, the denoising error image obtaining module 220 may be specifically configured to:
subtracting the filtered image from the first image to obtain an error image;
and denoising the error image to obtain a denoised error image.
In an exemplary implementation, the target image acquisition module 230 may be specifically configured to:
obtaining a correction error image according to the correction coefficient value and the denoising error image;
and subtracting the corrected error image from the first image to obtain a target image.
In an exemplary implementation, the obtaining of the correction coefficient value includes:
receiving a correction coefficient value input by a user;
or reading the default correction coefficient value of the system.
In an exemplary implementation, the method further includes:
the first reconstruction module is used for reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a first reconstruction parameter value to obtain a first image;
the second reconstruction module is used for reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a second reconstruction parameter value to obtain a second image;
and the parameters corresponding to the first reconstruction parameter value and the second reconstruction parameter value are cone angle weight parameters in a weighted filtering back projection algorithm.
The embodiment of the invention also provides the electronic equipment. Fig. 3 is a hardware structure diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 3, the electronic apparatus includes: an internal bus 301, and a memory 302, a processor 303, and an external interface 304 connected by the internal bus, wherein:
the memory 302 is used for storing machine readable instructions corresponding to the image processing logic;
the processor 303 is configured to read the machine-readable instructions in the memory 302 and execute the instructions to implement the following operations:
taking the first image as a guide image, and conducting guide filtering on the second image to obtain a filtering image; the first image and the second image are two images obtained by reconstructing the same spiral cone beam CT scanning data by using the same reconstruction algorithm and different reconstruction parameter values; the cone angle artifact of the second image is lighter than the cone angle artifact of the first image;
obtaining a denoising error image which contains cone angle artifacts and is free of noise according to the first image and the filtering image;
and obtaining a target image according to the first image and the denoising error image.
In one exemplary implementation, obtaining a denoised error image containing cone angle artifacts and removing noise according to the first image and the filtered image includes:
subtracting the filtered image from the first image to obtain an error image;
and denoising the error image to obtain a denoised error image.
In an exemplary implementation, obtaining a target image according to the first image and the denoised error image includes:
obtaining a correction error image according to the correction coefficient value and the denoising error image;
and subtracting the corrected error image from the first image to obtain a target image.
In an exemplary implementation, the obtaining of the correction coefficient value includes:
receiving a correction coefficient value input by a user;
or reading the default correction coefficient value of the system.
In an exemplary implementation process, before the step of taking the first image as a guide image and performing guide filtering on the second image to obtain a filtered image, the method further includes:
reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a first reconstruction parameter value to obtain a first image;
reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a second reconstruction parameter value to obtain a second image;
and the parameters corresponding to the first reconstruction parameter value and the second reconstruction parameter value are cone angle weight parameters in a weighted filtering back projection algorithm.
The electronic device in this embodiment may be a console device in a CT system.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the program, when executed by a processor, implements the following operations:
taking the first image as a guide image, and conducting guide filtering on the second image to obtain a filtering image; the first image and the second image are two images obtained by reconstructing the same spiral cone beam CT scanning data by using the same reconstruction algorithm and different reconstruction parameter values; the cone angle artifact of the second image is lighter than the cone angle artifact of the first image;
obtaining a denoising error image which contains cone angle artifacts and is free of noise according to the first image and the filtering image;
and obtaining a target image according to the first image and the denoising error image.
In one exemplary implementation, obtaining a denoised error image containing cone angle artifacts and removing noise according to the first image and the filtered image includes:
subtracting the filtered image from the first image to obtain an error image;
and denoising the error image to obtain a denoised error image.
In an exemplary implementation, obtaining a target image according to the first image and the denoised error image includes:
obtaining a correction error image according to the correction coefficient value and the denoising error image;
and subtracting the corrected error image from the first image to obtain a target image.
In an exemplary implementation, the obtaining of the correction coefficient value includes:
receiving a correction coefficient value input by a user;
or reading the default correction coefficient value of the system.
In an exemplary implementation process, before the step of taking the first image as a guide image and performing guide filtering on the second image to obtain a filtered image, the method further includes:
reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a first reconstruction parameter value to obtain a first image;
reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a second reconstruction parameter value to obtain a second image;
and the parameters corresponding to the first reconstruction parameter value and the second reconstruction parameter value are cone angle weight parameters in a weighted filtering back projection algorithm.
For the device and apparatus embodiments, as they correspond substantially to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, wherein the modules described as separate parts may or may not be physically separate, and the parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in the specification. One of ordinary skill in the art can understand and implement it without inventive effort.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Other embodiments of the present description will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This specification is intended to cover any variations, uses, or adaptations of the specification following, in general, the principles of the specification and including such departures from the present disclosure as come within known or customary practice within the art to which the specification pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the specification being indicated by the following claims.
It will be understood that the present description is not limited to the precise arrangements described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present description is limited only by the appended claims.
The above description is only a preferred embodiment of the present disclosure, and should not be taken as limiting the present disclosure, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.

Claims (10)

1. An image processing method, comprising:
taking the first image as a guide image, and conducting guide filtering on the second image to obtain a filtering image; the first image and the second image are two images obtained by reconstructing the same spiral cone beam CT scanning data by using the same reconstruction algorithm and different reconstruction parameter values; the cone angle artifact of the second image is lighter than the cone angle artifact of the first image;
obtaining a denoising error image which contains cone angle artifacts and is free of noise according to the first image and the filtering image;
and obtaining a target image according to the first image and the denoising error image.
2. The method of claim 1, wherein obtaining a denoised error image containing cone angle artifacts and removing noise from the first image and the filtered image comprises:
subtracting the filtered image from the first image to obtain an error image;
and denoising the error image to obtain a denoised error image.
3. The method of claim 1, wherein deriving a target image from the first image and the denoised error image comprises:
obtaining a correction error image according to the correction coefficient value and the denoising error image;
and subtracting the corrected error image from the first image to obtain a target image.
4. The method of claim 3, wherein the correction coefficient values are obtained by:
receiving a correction coefficient value input by a user;
or reading the default correction coefficient value of the system.
5. The method of claim 1, wherein before the step of performing the guided filtering on the second image by using the first image as the guide image to obtain the filtered image, the method further comprises:
reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a first reconstruction parameter value to obtain a first image;
reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a second reconstruction parameter value to obtain a second image;
and the parameters corresponding to the first reconstruction parameter value and the second reconstruction parameter value are cone angle weight parameters in a weighted filtering back projection algorithm.
6. An image processing apparatus characterized by comprising:
the guide filtering module is used for performing guide filtering on the second image by taking the first image as a guide image to obtain a filtering image; the first image and the second image are two images obtained by reconstructing the same spiral cone beam CT scanning data by using the same reconstruction algorithm and different reconstruction parameter values; the cone angle artifact of the second image is lighter than the cone angle artifact of the first image;
the denoising error image acquisition module is used for acquiring a denoising error image which contains cone angle artifacts and is used for removing noise according to the first image and the filtering image;
and the target image acquisition module is used for acquiring a target image according to the first image and the denoising error image.
7. The apparatus of claim 6, wherein the de-noising error image acquisition module is specifically configured to:
subtracting the filtered image from the first image to obtain an error image;
and denoising the error image to obtain a denoised error image.
8. The apparatus of claim 6, wherein the target image acquisition module is specifically configured to:
obtaining a correction error image according to the correction coefficient value and the denoising error image;
and subtracting the corrected error image from the first image to obtain a target image.
9. The apparatus of claim 8, wherein the correction coefficient values are obtained by:
receiving a correction coefficient value input by a user;
or reading the default correction coefficient value of the system.
10. The apparatus of claim 6, further comprising:
the first reconstruction module is used for reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a first reconstruction parameter value to obtain a first image;
the second reconstruction module is used for reconstructing the helical cone beam CT scanning data by using a weighted filtering back projection algorithm and a second reconstruction parameter value to obtain a second image;
and the parameters corresponding to the first reconstruction parameter value and the second reconstruction parameter value are cone angle weight parameters in a weighted filtering back projection algorithm.
CN202110369034.8A 2021-04-06 2021-04-06 Image processing method and device and electronic equipment Pending CN113240762A (en)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111192228A (en) * 2020-01-02 2020-05-22 沈阳先进医疗设备技术孵化中心有限公司 Image processing method and device, CT (computed tomography) equipment and CT system
CN111798391A (en) * 2020-06-29 2020-10-20 东软医疗系统股份有限公司 Image processing method and device, medical imaging equipment and system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111192228A (en) * 2020-01-02 2020-05-22 沈阳先进医疗设备技术孵化中心有限公司 Image processing method and device, CT (computed tomography) equipment and CT system
CN111798391A (en) * 2020-06-29 2020-10-20 东软医疗系统股份有限公司 Image processing method and device, medical imaging equipment and system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
佟丽霞 等: "一种基于引导滤波的CT锥角伪影去除方法研究", 《中国医学装备》, vol. 17, no. 7, pages 21 - 24 *

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