CN111798391A - Image processing method and device, medical imaging equipment and system - Google Patents
Image processing method and device, medical imaging equipment and system Download PDFInfo
- Publication number
- CN111798391A CN111798391A CN202010610155.2A CN202010610155A CN111798391A CN 111798391 A CN111798391 A CN 111798391A CN 202010610155 A CN202010610155 A CN 202010610155A CN 111798391 A CN111798391 A CN 111798391A
- Authority
- CN
- China
- Prior art keywords
- image
- filtering
- difference
- original
- filtered
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000002059 diagnostic imaging Methods 0.000 title claims abstract description 28
- 238000003672 processing method Methods 0.000 title claims abstract description 17
- 238000001914 filtration Methods 0.000 claims abstract description 198
- 238000000034 method Methods 0.000 claims description 32
- 238000002591 computed tomography Methods 0.000 claims description 26
- 230000008569 process Effects 0.000 claims description 19
- 230000002146 bilateral effect Effects 0.000 claims description 16
- 238000002600 positron emission tomography Methods 0.000 claims description 12
- 210000001519 tissue Anatomy 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 230000008520 organization Effects 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 3
- 210000000988 bone and bone Anatomy 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 230000000717 retained effect Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 210000004351 coronary vessel Anatomy 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000003325 tomography Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
- G06T2207/20028—Bilateral filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
The embodiment of the invention provides an image processing method and device, medical imaging equipment and a medical imaging system. According to the embodiment of the invention, the original image is subjected to first filtering to obtain a filtered image, the original image and the filtered image are subjected to difference to obtain a difference image, the difference image is subjected to second filtering to obtain a filtered difference image, the difference image and the filtered difference image are subjected to difference to obtain a high-frequency image, the high-frequency image and the filtered image are superposed to obtain a recombined image corresponding to the original image, a result image is obtained based on the recombined image sequence formed by the recombined images corresponding to all the original images in the target image sequence, and the condition that the uniformity of each image in the target image sequence in the Z direction is inconsistent is improved by utilizing filtering combination, so that strip artifacts in the result image obtained based on the image sequence can be reduced or even eliminated, and the image quality is improved.
Description
Technical Field
The present invention relates to the field of medical image processing technologies, and in particular, to an image processing method and apparatus, a medical imaging device, and a medical imaging system.
Background
Currently, medical imaging technology is widely applied, and medical imaging has become an important clinical auxiliary means. The types of medical images are also many, and mainly include CT (Computed Tomography), PET (Positron Emission Tomography), MR (Magnetic resonance), and the like.
In the resulting images obtained based on the medical image sequence, streak artifacts often exist, which seriously affect the image quality. For example, due to the influence of factors such as cone angle artifacts and slice occlusion at the edge layer of a tomography, streak artifacts may be generated at positions such as the edge layer of the slice or bone edges in a CT-MPR (Multi-planar reconstruction) image, resulting in degradation of image quality.
Disclosure of Invention
In order to overcome the problems in the related art, the invention provides an image processing method and device, medical imaging equipment and a system, which are used for improving the image quality of a heart coronary vessel reconstruction image.
According to a first aspect of embodiments of the present invention, there is provided an image processing method including:
aiming at each original image in a target image sequence, carrying out first filtering on the original image to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image;
the original image and the filtering image are subjected to difference to obtain a difference image;
performing second filtering on the difference image to obtain a filtering difference image, wherein information contained in the filtering difference image is a low-frequency information part of the difference image;
the difference image and the filtering difference image are subjected to difference to obtain a high-frequency image;
superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image;
and obtaining a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
According to a second aspect of the embodiments of the present invention, there is provided an image processing apparatus including:
the first filtering module is used for carrying out first filtering on each original image in a target image sequence to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image;
the first subtraction module is used for subtracting the original image from the filtered image to obtain a difference image;
the second filtering module is used for carrying out second filtering on the difference image to obtain a filtering difference image, and information contained in the filtering difference image is a low-frequency information part of the difference image;
the second subtraction module is used for subtracting the difference image from the filtering difference image to obtain a high-frequency image;
the superposition module is used for superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image;
and the result acquisition module is used for acquiring a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
According to a third aspect of embodiments of the present invention, there is provided a medical imaging apparatus including: the system comprises an internal bus, a memory, a processor and an external interface which are connected through the internal bus; the external interface is connected with an image data acquisition device of the medical image system;
the memory is used for storing machine readable instructions corresponding to the image processing logic;
the processor is configured to read the machine-readable instructions on the memory and perform the following operations:
aiming at each original image in a target image sequence, carrying out first filtering on the original image to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image;
the original image and the filtering image are subjected to difference to obtain a difference image;
performing second filtering on the difference image to obtain a filtering difference image, wherein information contained in the filtering difference image is a low-frequency information part of the difference image;
the difference image and the filtering difference image are subjected to difference to obtain a high-frequency image;
superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image;
and obtaining a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
According to a fourth aspect of the embodiments of the present invention, there is provided a medical imaging system, including an image data acquisition device, a scanning bed and a medical imaging apparatus; wherein:
the image data acquisition device is used for acquiring the scanning data of a scanning object in the scanning process of the medical imaging system;
the medical imaging device is used for:
aiming at each original image in a target image sequence, carrying out first filtering on the original image to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image;
the original image and the filtering image are subjected to difference to obtain a difference image;
performing second filtering on the difference image to obtain a filtering difference image, wherein information contained in the filtering difference image is a low-frequency information part of the difference image;
the difference image and the filtering difference image are subjected to difference to obtain a high-frequency image;
superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image;
and obtaining a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, each original image in a target image sequence is subjected to first filtering to obtain a filtering image, the original image and the filtering image are subjected to difference to obtain a difference image, the difference image is subjected to second filtering to obtain a filtering difference image, the difference image and the filtering difference image are subjected to difference to obtain a high-frequency image, the high-frequency image and the filtering image are superposed to obtain a recombined image corresponding to the original image, a result image is obtained based on a recombined image sequence formed by the recombined images corresponding to all the original images in the target image sequence, the condition that the uniformity of each image in the target image sequence in the Z direction is inconsistent is improved by utilizing filtering combination, and therefore, the strip artifacts in the result image obtained based on the image sequence can be reduced or even eliminated, the image quality 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.
Drawings
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 schematic diagram of a comparison between an MPR coronal image obtained in a conventional manner and an MPR coronal image obtained by using the image processing method provided by the embodiment of the present invention.
Fig. 3 is a functional block diagram of an image processing apparatus according to an embodiment of the present invention.
Fig. 4 is a hardware structure diagram of a medical imaging apparatus 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.
The nature of streak artifacts in medical images is the Z-directional uniformity inconsistency caused by the differences of the individual images in the sequence of medical images used to obtain the medical image. According to the embodiment of the invention, the strip artifacts in the image are removed by removing the difference (the artifacts are either lightened or darkened integrally) in the cross-sectional image causing the strip artifacts.
The image processing method is explained in detail below by way of 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, aiming at each original image in a target image sequence, carrying out first filtering on the original image to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image.
And S102, carrying out difference on the original image and the filtered image to obtain a difference image.
And S103, performing second filtering on the difference image to obtain a filtering difference image, wherein information contained in the filtering difference image is a low-frequency information part of the difference image.
S104, subtracting the difference image from the filtering difference image to obtain a high-frequency image;
and S105, superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image.
And S106, obtaining a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
In this embodiment, the target image sequence may be a CT image sequence, a PET image sequence, an MR image sequence, or the like.
In this embodiment, by performing the first filtering on the original image, an image corresponding to the low-frequency information portion of the original image can be separated, that is, the filtered image is an image in which the low-frequency information of the original image is retained and the medium-high frequency information of the original image is removed.
By subtracting the original image from the filtered image, an image corresponding to the middle-high frequency information portion of the original image can be obtained, i.e., the difference image is an image in which the middle-high frequency information of the original image is retained and the low frequency information of the original image is removed.
The medium-high frequency information of the original image includes organization information, artifact information, image noise information, and the like. The artifact information therein is a factor causing streak artifacts. Therefore, in this embodiment, the difference image is further subjected to the second filtering to extract artifact information (the artifact information is a low-frequency portion in the difference image), and a filtered difference image representing the artifact information is obtained.
And then, the filtering difference image is differenced with the difference image to obtain a high-frequency image which does not contain artifact information and contains detailed organization information and high-frequency noise information.
Since the high-frequency image does not include artifact information, a reconstructed image obtained by superimposing the high-frequency image and the filtered image does not include artifact information. In this way, all images in the recombined image sequence do not contain artifacts, so that streak artifacts in the resulting images obtained on the basis of the recombined image sequence can be reduced or eliminated.
In an exemplary implementation process, in step S101, performing a first filtering on the original image to obtain a filtered image, which may include:
and carrying out one-dimensional bilateral filtering in the Z direction on the original image to obtain a filtered image.
In this embodiment, by using one-dimensional bilateral filtering, the edge characteristics of the image can be better maintained, the image boundary blurring is prevented, and the algorithm complexity is reduced (the algorithm complexity of the one-dimensional bilateral filtering is o (n)).
It should be noted that, in this embodiment, the filtered image is not limited to be obtained by using one-dimensional bilateral filtering in the Z direction, and may be obtained by other methods, such as mean filtering, gaussian filtering, and the like.
In an exemplary implementation process, performing one-dimensional bilateral filtering on the original image in the Z direction to obtain a filtered image may include:
determining a one-dimensional filtering convolution kernel according to the CT value and the Z-direction position information of each Z pixel point in the convolution window;
and convolving the original image with the one-dimensional filtering convolution kernel to obtain a filtering image.
Take the target image sequence as the CT image sequence and the result image as the MPR image as an example.
The process of obtaining a filtered image by one-dimensional bilateral filtering of an original image can be represented by the following formula (1).
In formula (1), g represents an original image, gLowRepresenting the filtered image, gLowIs the low-frequency part of the original image g,
representing a convolution operation using a one-dimensional filtered convolution kernel of window width l, omegaiIs a one-dimensional convolution kernel with a window width of l.
The filtering convolution kernel weight can be calculated by the following formula (2).
In equation (2), i is the coordinate in the corresponding convolution window, x is the center coordinate of the convolution window, g (x) is the CT value of the corresponding pixel, σsIs the distance variance weight, σrAnd the variance weight of the CT value, i and x are coordinates in a convolution kernel in the Z direction, and omega (i) is the ith numerical value in the convolution kernel in the Z direction.
By applying the parameter sigma in the formula (2)s、σrAdjusting, only basic tissue and bone medium information in the processed image is reserved, detail tissue, high-frequency noise and artifact abnormal information are smoothed, and the smoothed image g isLowThe information in (1) includes basic tissue information and bone medium information.
In order to solve the problem of image inconsistency caused by the loss of organization information and the loss of high-frequency information in the image, the image (namely g) after the Z-direction filtering is carried outLow) And (4) making difference with the original image so as to obtain the filtered and smoothed intermediate frequency artifact, high frequency detail organization and noise. G in formula (3) is described as formula (3) belowHighIs the medium-high frequency part of the original image obtained.
gHigh＝g-gLow(3)
In an exemplary implementation, the second filtering the difference image to obtain a filtered difference image may include:
and performing guiding filtering on the difference image to obtain a filtering difference image.
For example, the aforementioned gHighFor difference image, for gHighAnd performing guiding filtering to obtain a filtering difference image.
The experimental analysis proves that the CT value deviation of the artifact information is generally higher than that of the detail tissue, the artifact information does not have an in-plane organization structure, and the artifact information belongs to low-frequency information compared with the detail texture tissue and noise, so that the artifact information in the difference image can be extracted through further filtering processing in the image plane.
In this embodiment, a guided filtering method is used for processing (the method is not limited to the medium-high frequency extraction method), artifact information in the difference image is separated, the filtered difference image only includes abnormal information (referred to as artifact information herein) causing the artifact, and a high-frequency image including detail organization information and high-frequency noise information, which does not include the artifact, can be obtained by performing a difference between the filtered difference image and the difference image.
The invention considers the filtering operation of the real-time performance of the algorithm in the image plane and adopts the guide filtering with O (1) complexity, and in order to restore more tissue details, the normalized difference image is used as the guide image of the guide filtering.
Obtaining a filtered difference image using the guided filtering can be expressed by equation (4) and equation (5) as follows:
in equations (4) and (5), q represents a filtered output image of the pilot filtering (e.g., the aforementioned filtered difference image), I represents a pilot image, ωkRepresenting neighbourhoods of radius k centred on pixel (i, j), each neighbourhood having a fixed akAnd bk。
In an exemplary implementation, the performing guided filtering on the difference image to obtain a filtered difference image may include:
normalizing the difference image to obtain a normalized difference image;
and adopting the normalized difference image as a guide image, and performing guide filtering on the difference image to obtain a filtering difference image.
In this embodiment, the guide image is a difference image after normalization, and the purpose is to perform "low-frequency matting" on the difference image and extract a low-frequency portion (i.e., an artifact portion) in the difference image. The embodiment can better restore the tissue details by adopting the normalized difference image as the guide image.
In other embodiments, the above artifact removal manner can be used to improve the overall brightness or overall darkening of each image in the image sequence, so that the Z-direction uniformity tends to be consistent.
In the formula (4), akAnd bkThe selection of (c) can be obtained by optimizing the following formula (6).
In formula (6), I is a normalized difference image (guide image) and is a regularization term (the larger the guide filter, the smoother the filtered output image, and the smaller the guide image, the smaller the effect of the guide image.
The normalization mode of the guide image can adopt a min-max (minimum-maximum) mode, and data are mapped to a [0,1] interval.
The principle of normalizing the difference image can be expressed by the following formula (7).
In the formula (7), the first and second groups,
for the maximum CT value in the difference image,
is the minimum CT value in the difference image,
is the CT value of pixel (i, j) in the difference image.
And guiding the filtered image q to only contain artifact abnormal information, and subtracting the filtered image q from the pre-filtered image to obtain a high-frequency image containing detailed tissue information and high-frequency noise information.
In other embodiments, other images may be used as the guide image, for example, an original image and a Z-filtered low-frequency image may be used as the guide image.
In an exemplary implementation, obtaining a result image based on a recombined image sequence composed of recombined images corresponding to all original images in the target image sequence may include:
and performing multi-plane reconstruction on the reconstructed image sequence consisting of the reconstructed images corresponding to all the original images in the target image sequence to obtain a multi-plane reconstructed MPR image.
The image processing method provided by the embodiment of the invention obtains a filtered image by performing first filtering on each original image in a target image sequence, obtains a difference image by performing difference between the original image and the filtered image, obtains a filtered difference image by performing second filtering on the difference image, obtains a high-frequency image by performing difference between the difference image and the filtered difference image, superposes the high-frequency image and the filtered image to obtain a recombined image corresponding to the original image, obtains a result image based on a recombined image sequence composed of recombined images corresponding to all the original images in the target image sequence, improves the condition that the uniformity of each image in the target image sequence in the Z direction is inconsistent by utilizing filtering combination, thereby reducing or even eliminating the strip-shaped artifacts in the result image obtained based on the image sequence, the image quality is improved.
Taking the MPR image as an example, a processing effect of the image processing method provided by the embodiment of the invention on the streak artifact is shown below.
Fig. 2 is a schematic diagram of a comparison between an MPR coronal image obtained in a conventional manner and an MPR coronal image obtained by using the image processing method provided by the embodiment of the present invention. In fig. 2, the left image is an MPR coronal image obtained by the conventional method, and the right image is an MPR coronal image obtained by the image processing method provided by the embodiment of the present invention.
In addition, the image processing method provided by the embodiment of the invention can effectively reduce or eliminate cone angle artifacts in the image, and further improve the image quality.
Based on the above method embodiment, the embodiment of the present invention further provides corresponding apparatus, device, and storage medium embodiments.
Fig. 3 is a functional block diagram of an image processing apparatus according to an embodiment of the present invention. As shown in fig. 3, in the present embodiment, the image processing apparatus may include:
a first filtering module 310, configured to perform first filtering on each original image in a target image sequence to obtain a filtered image, where information included in the filtered image is a low-frequency information portion of the original image;
a first subtraction module 320, configured to subtract the original image from the filtered image to obtain a difference image;
a second filtering module 330, configured to perform second filtering on the difference image to obtain a filtered difference image, where information included in the filtered difference image is a low-frequency information portion of the difference image;
a second subtraction module 340, configured to subtract the difference image from the filtered difference image to obtain a high-frequency image,
the superposition module 350 is configured to superpose the high-frequency image and the filtered image to obtain a recombined image corresponding to the original image;
and a result obtaining module 360, configured to obtain a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
In an exemplary implementation, the first filtering module 310 may be specifically configured to:
and carrying out one-dimensional bilateral filtering in the Z direction on the original image to obtain a filtered image.
In an exemplary implementation process, when the first filtering module 310 is configured to perform one-dimensional bilateral filtering on the original image in the Z direction to obtain a filtered image, the first filtering module may be specifically configured to:
determining a one-dimensional filtering convolution kernel according to the CT value and the Z-direction position information of each Z pixel point in the convolution window;
and convolving the original image with the one-dimensional filtering convolution kernel to obtain a filtering image.
In an exemplary implementation, the second filtering module 330 may be specifically configured to:
and performing guiding filtering on the difference image to obtain a filtering difference image.
In an exemplary implementation process, the second filtering module 330, when configured to perform guided filtering on the difference image to obtain a filtered difference image, may specifically be configured to:
normalizing the difference image to obtain a normalized difference image;
and adopting the normalized difference image as a guide image, and performing guide filtering on the difference image to obtain a filtering difference image.
In an exemplary implementation process, the result obtaining module 360 may be specifically configured to:
and performing multi-plane reconstruction on the reconstructed image sequence consisting of the reconstructed images corresponding to all the original images in the target image sequence to obtain a multi-plane reconstructed MPR image.
In an exemplary implementation, the target image sequence is an electron computed tomography CT image sequence, a positron emission tomography PET image sequence, or a magnetic resonance MR image sequence.
The embodiment of the invention also provides medical imaging equipment. Fig. 4 is a hardware structure diagram of a medical imaging apparatus according to an embodiment of the present invention. As shown in fig. 4, the medical imaging apparatus includes: an internal bus 401, and a memory 402, a processor 403 and an external interface 404 connected through the internal bus 401, wherein the external interface 404 is connected with an image data acquisition device of the medical imaging system;
the memory 402 is used for storing machine readable instructions corresponding to the image processing logic;
the processor 403 is configured to read the machine-readable instructions in the memory 402 and execute the instructions to implement the following operations:
aiming at each original image in a target image sequence, carrying out first filtering on the original image to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image;
the original image and the filtering image are subjected to difference to obtain a difference image;
performing second filtering on the difference image to obtain a filtering difference image, wherein information contained in the filtering difference image is a low-frequency information part of the difference image;
the difference image and the filtering difference image are subjected to difference to obtain a high-frequency image;
superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image;
and obtaining a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
In an exemplary implementation process, performing a first filtering on the original image to obtain a filtered image includes:
and carrying out one-dimensional bilateral filtering in the Z direction on the original image to obtain a filtered image.
In an exemplary implementation process, performing one-dimensional bilateral filtering on the original image in the Z direction to obtain a filtered image, including:
determining a one-dimensional filtering convolution kernel according to the CT value and the Z-direction position information of each Z pixel point in the convolution window;
and convolving the original image with the one-dimensional filtering convolution kernel to obtain a filtering image.
In an exemplary implementation, performing a second filtering on the difference image to obtain a filtered difference image includes:
and performing guiding filtering on the difference image to obtain a filtering difference image.
In an exemplary implementation, the performing guided filtering on the difference image to obtain a filtered difference image includes:
normalizing the difference image to obtain a normalized difference image;
and adopting the normalized difference image as a guide image, and performing guide filtering on the difference image to obtain a filtering difference image.
In an exemplary implementation process, obtaining a result image based on a recombined image sequence composed of recombined images corresponding to all original images in the target image sequence includes:
and performing multi-plane reconstruction on the reconstructed image sequence consisting of the reconstructed images corresponding to all the original images in the target image sequence to obtain a multi-plane reconstructed MPR image.
In an exemplary implementation, the target image sequence is an electron computed tomography CT image sequence, a positron emission tomography PET image sequence, or a magnetic resonance MR image sequence.
The embodiment of the invention also provides a medical imaging system, which comprises an image data acquisition device, a scanning bed and medical imaging equipment; wherein:
the image data acquisition device is used for acquiring the scanning data of a scanning object in the scanning process of the medical imaging system;
the medical imaging device is used for:
aiming at each original image in a target image sequence, carrying out first filtering on the original image to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image;
the original image and the filtering image are subjected to difference to obtain a difference image;
performing second filtering on the difference image to obtain a filtering difference image, wherein information contained in the filtering difference image is a low-frequency information part of the difference image;
the difference image and the filtering difference image are subjected to difference to obtain a high-frequency image;
superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image;
and obtaining a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
In an exemplary implementation process, performing a first filtering on the original image to obtain a filtered image includes:
and carrying out one-dimensional bilateral filtering in the Z direction on the original image to obtain a filtered image.
In an exemplary implementation process, performing one-dimensional bilateral filtering on the original image in the Z direction to obtain a filtered image, including:
determining a one-dimensional filtering convolution kernel according to the CT value and the Z-direction position information of each Z pixel point in the convolution window;
and convolving the original image with the one-dimensional filtering convolution kernel to obtain a filtering image.
In an exemplary implementation, performing a second filtering on the difference image to obtain a filtered difference image includes:
and performing guiding filtering on the difference image to obtain a filtering difference image.
In an exemplary implementation, the performing guided filtering on the difference image to obtain a filtered difference image includes:
normalizing the difference image to obtain a normalized difference image;
and adopting the normalized difference image as a guide image, and performing guide filtering on the difference image to obtain a filtering difference image.
In an exemplary implementation process, obtaining a result image based on a recombined image sequence composed of recombined images corresponding to all original images in the target image sequence includes:
and performing multi-plane reconstruction on the reconstructed image sequence consisting of the reconstructed images corresponding to all the original images in the target image sequence to obtain a multi-plane reconstructed MPR image.
In an exemplary implementation, the target image sequence is an electron computed tomography CT image sequence, a positron emission tomography PET image sequence, or a magnetic resonance MR image sequence.
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:
aiming at each original image in a target image sequence, carrying out first filtering on the original image to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image;
the original image and the filtering image are subjected to difference to obtain a difference image;
performing second filtering on the difference image to obtain a filtering difference image, wherein information contained in the filtering difference image is a low-frequency information part of the difference image;
the difference image and the filtering difference image are subjected to difference to obtain a high-frequency image;
superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image;
and obtaining a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
In an exemplary implementation process, performing a first filtering on the original image to obtain a filtered image includes:
and carrying out one-dimensional bilateral filtering in the Z direction on the original image to obtain a filtered image.
In an exemplary implementation process, performing one-dimensional bilateral filtering on the original image in the Z direction to obtain a filtered image, including:
determining a one-dimensional filtering convolution kernel according to the CT value and the Z-direction position information of each Z pixel point in the convolution window;
and convolving the original image with the one-dimensional filtering convolution kernel to obtain a filtering image.
In an exemplary implementation, performing a second filtering on the difference image to obtain a filtered difference image includes:
and performing guiding filtering on the difference image to obtain a filtering difference image.
In an exemplary implementation, the performing guided filtering on the difference image to obtain a filtered difference image includes:
normalizing the difference image to obtain a normalized difference image;
and adopting the normalized difference image as a guide image, and performing guide filtering on the difference image to obtain a filtering difference image.
In an exemplary implementation process, obtaining a result image based on a recombined image sequence composed of recombined images corresponding to all original images in the target image sequence includes:
and performing multi-plane reconstruction on the reconstructed image sequence consisting of the reconstructed images corresponding to all the original images in the target image sequence to obtain a multi-plane reconstructed MPR image.
In an exemplary implementation, the target image sequence is an electron computed tomography CT image sequence, a positron emission tomography PET image sequence, or a magnetic resonance MR image sequence.
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:
aiming at each original image in a target image sequence, carrying out first filtering on the original image to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image;
the original image and the filtering image are subjected to difference to obtain a difference image;
performing second filtering on the difference image to obtain a filtering difference image, wherein information contained in the filtering difference image is a low-frequency information part of the difference image;
the difference image and the filtering difference image are subjected to difference to obtain a high-frequency image;
superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image;
and obtaining a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
2. The method of claim 1, wherein first filtering the original image to obtain a filtered image comprises:
and carrying out one-dimensional bilateral filtering in the Z direction on the original image to obtain a filtered image.
3. The method of claim 2, wherein performing Z-direction one-dimensional bilateral filtering on the original image to obtain a filtered image comprises:
determining a one-dimensional filtering convolution kernel according to the CT value and the Z-direction position information of each Z pixel point in the convolution window;
and convolving the original image with the one-dimensional filtering convolution kernel to obtain a filtering image.
4. The method of claim 1, wherein second filtering the difference image to obtain a filtered difference image comprises:
and performing guiding filtering on the difference image to obtain a filtering difference image.
5. The method of claim 4, wherein the pilot filtering the difference image to obtain a filtered difference image comprises:
normalizing the difference image to obtain a normalized difference image;
and adopting the normalized difference image as a guide image, and performing guide filtering on the difference image to obtain a filtering difference image.
6. The method according to claim 1, wherein obtaining a result image based on a recombined image sequence composed of recombined images corresponding to all original images in the target image sequence comprises:
and performing multi-plane reconstruction on the reconstructed image sequence consisting of the reconstructed images corresponding to all the original images in the target image sequence to obtain a multi-plane reconstructed MPR image.
7. The method of claim 1, wherein the target image sequence is an electron Computed Tomography (CT) image sequence, a Positron Emission Tomography (PET) image sequence, or a Magnetic Resonance (MR) image sequence.
8. An image processing apparatus characterized by comprising:
the first filtering module is used for carrying out first filtering on each original image in a target image sequence to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image;
the first subtraction module is used for subtracting the original image from the filtered image to obtain a difference image;
the second filtering module is used for carrying out second filtering on the difference image to obtain a filtering difference image, and information contained in the filtering difference image is a low-frequency information part of the difference image;
the second subtraction module is used for subtracting the difference image from the filtering difference image to obtain a high-frequency image;
the superposition module is used for superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image;
and the result acquisition module is used for acquiring a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
9. A medical imaging apparatus, comprising: the system comprises an internal bus, a memory, a processor and an external interface which are connected through the internal bus; the external interface is connected with an image data acquisition device of the medical image system;
the memory is used for storing machine readable instructions corresponding to the image processing logic;
the processor is configured to read the machine-readable instructions on the memory and perform the following operations:
aiming at each original image in a target image sequence, carrying out first filtering on the original image to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image;
the original image and the filtering image are subjected to difference to obtain a difference image;
performing second filtering on the difference image to obtain a filtering difference image, wherein information contained in the filtering difference image is a low-frequency information part of the difference image;
the difference image and the filtering difference image are subjected to difference to obtain a high-frequency image;
superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image;
and obtaining a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
10. A medical imaging system is characterized by comprising an image data acquisition device, a scanning bed and medical imaging equipment; wherein:
the image data acquisition device is used for acquiring the scanning data of a scanning object in the scanning process of the medical imaging system;
the medical imaging device is used for:
aiming at each original image in a target image sequence, carrying out first filtering on the original image to obtain a filtered image, wherein information contained in the filtered image is a low-frequency information part of the original image;
the original image and the filtering image are subjected to difference to obtain a difference image;
performing second filtering on the difference image to obtain a filtering difference image, wherein information contained in the filtering difference image is a low-frequency information part of the difference image;
the difference image and the filtering difference image are subjected to difference to obtain a high-frequency image;
superposing the high-frequency image and the filtering image to obtain a recombined image corresponding to the original image;
and obtaining a result image based on a recombined image sequence formed by recombined images corresponding to all original images in the target image sequence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010610155.2A CN111798391A (en) | 2020-06-29 | 2020-06-29 | Image processing method and device, medical imaging equipment and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010610155.2A CN111798391A (en) | 2020-06-29 | 2020-06-29 | Image processing method and device, medical imaging equipment and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111798391A true CN111798391A (en) | 2020-10-20 |
Family
ID=72809720
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010610155.2A Pending CN111798391A (en) | 2020-06-29 | 2020-06-29 | Image processing method and device, medical imaging equipment and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111798391A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112489155A (en) * | 2020-12-07 | 2021-03-12 | 中国科学院深圳先进技术研究院 | Image reconstruction method and apparatus, electronic device, and machine-readable storage medium |
| CN113240762A (en) * | 2021-04-06 | 2021-08-10 | 沈阳先进医疗设备技术孵化中心有限公司 | Image processing method and device and electronic equipment |
| CN114022374A (en) * | 2021-11-02 | 2022-02-08 | 海南大学 | Image ring artifact correction method, system and equipment |
| CN114494027A (en) * | 2020-10-27 | 2022-05-13 | 上海联影医疗科技股份有限公司 | Image noise reduction method and device, computer equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160364856A1 (en) * | 2015-06-11 | 2016-12-15 | Shenyang Neusoft Medical Systems Co. Ltd. | Process for computed tomography image |
| CN110070589A (en) * | 2019-04-28 | 2019-07-30 | 东软医疗系统股份有限公司 | Bearing calibration, device, storage medium and the electronic equipment of CT image |
| CN111047659A (en) * | 2019-11-08 | 2020-04-21 | 湖北科技学院 | CT ring artifact correction method combined with filtering method |
-
2020
- 2020-06-29 CN CN202010610155.2A patent/CN111798391A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160364856A1 (en) * | 2015-06-11 | 2016-12-15 | Shenyang Neusoft Medical Systems Co. Ltd. | Process for computed tomography image |
| CN110070589A (en) * | 2019-04-28 | 2019-07-30 | 东软医疗系统股份有限公司 | Bearing calibration, device, storage medium and the electronic equipment of CT image |
| CN111047659A (en) * | 2019-11-08 | 2020-04-21 | 湖北科技学院 | CT ring artifact correction method combined with filtering method |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114494027A (en) * | 2020-10-27 | 2022-05-13 | 上海联影医疗科技股份有限公司 | Image noise reduction method and device, computer equipment and storage medium |
| CN112489155A (en) * | 2020-12-07 | 2021-03-12 | 中国科学院深圳先进技术研究院 | Image reconstruction method and apparatus, electronic device, and machine-readable storage medium |
| WO2022120899A1 (en) * | 2020-12-07 | 2022-06-16 | 中国科学院深圳先进技术研究院 | Image reconstruction method and apparatus, electronic device and machine-readable storage medium |
| CN112489155B (en) * | 2020-12-07 | 2024-01-26 | 中国科学院深圳先进技术研究院 | Image reconstruction method and device, electronic equipment and machine-readable storage medium |
| CN113240762A (en) * | 2021-04-06 | 2021-08-10 | 沈阳先进医疗设备技术孵化中心有限公司 | Image processing method and device and electronic equipment |
| CN114022374A (en) * | 2021-11-02 | 2022-02-08 | 海南大学 | Image ring artifact correction method, system and equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111798391A (en) | Image processing method and device, medical imaging equipment and system | |
| EP2380132B1 (en) | Denoising medical images | |
| US5832134A (en) | Data visualization enhancement through removal of dominating structures | |
| CN109431531B (en) | Blood vessel segmentation method and device based on perfusion imaging and computer device | |
| KR101258814B1 (en) | Nonrigid registration method and system with density correction of each tissue and rigidity constraint of tumor in dynamic contrast-enhanced breast mr images | |
| CN111815735B (en) | Human tissue self-adaptive CT reconstruction method and reconstruction system | |
| WO2018037024A1 (en) | Feature-based image processing using feature images extracted from different iterations | |
| CN113592745A (en) | Unsupervised MRI image restoration method based on antagonism domain self-adaptation | |
| CN110400359B (en) | Method, device and equipment for eliminating image artifacts and storage medium | |
| CN115100093A (en) | Medical image fusion method based on gradient filtering | |
| CN114511670A (en) | Blood vessel reconstruction method, device, equipment and medium based on dynamic perfusion image | |
| CN116342444A (en) | Dual-channel multi-mode image fusion method and fusion imaging terminal | |
| CN107845081B (en) | Magnetic resonance image denoising method | |
| CN112862850A (en) | Digital silhouette angiography image processing method and device | |
| US11308660B2 (en) | Motion compensated cardiac valve reconstruction | |
| CN112785520B (en) | CT image artifact removing method | |
| Selvathi et al. | Segmentation of tissues in MR images using modified spatial fuzzy C means algorithm | |
| Draganov et al. | 3D SUSAN filtering of CT images with adaptive selection of the brightness threshold | |
| CN111489409A (en) | CT image processing method and device, CT equipment and CT system | |
| CN111932475B (en) | Filtering method, filtering device, CT equipment and CT system | |
| Madabhushi et al. | Generalized scale: theory, algorithms, and application to image inhomogeneity correction | |
| CN116681715B (en) | Blood vessel segmentation method, device, equipment and storage medium based on pixel value change | |
| CN113724158B (en) | Noise reduction method for dynamic contrast enhanced magnetic resonance imaging | |
| CN117808718B (en) | Method and system for improving medical image data quality based on Internet | |
| CN111612719A (en) | CT image processing method and device, CT equipment and CT system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201020 |
|
| RJ01 | Rejection of invention patent application after publication |