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WO2018126434A1 - Ct图像阴影校正方法、装置及电子设备 - Google Patents

Ct图像阴影校正方法、装置及电子设备 Download PDF

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Publication number
WO2018126434A1
WO2018126434A1 PCT/CN2017/070410 CN2017070410W WO2018126434A1 WO 2018126434 A1 WO2018126434 A1 WO 2018126434A1 CN 2017070410 W CN2017070410 W CN 2017070410W WO 2018126434 A1 WO2018126434 A1 WO 2018126434A1
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Prior art keywords
image
original
reconstructed
template
shading correction
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French (fr)
Inventor
梁晓坤
张志诚
谢耀钦
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Shenzhen Institute of Advanced Technology of CAS
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Shenzhen Institute of Advanced Technology of CAS
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Priority to PCT/CN2017/070410 priority Critical patent/WO2018126434A1/zh
Priority to CN201710015541.5A priority patent/CN106780397B/zh
Publication of WO2018126434A1 publication Critical patent/WO2018126434A1/zh
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography

Definitions

  • the present invention relates to the field of image shading correction technology, and in particular, to a CT image shading correction method, apparatus and electronic device.
  • CT image shading correction is one of the most important issues to improve CBCT image quality.
  • the currently known methods of shadow correction for CT images can be mainly divided into two categories: pre-processing and post-processing methods.
  • the pre-processing method corrects the shadow of the CT image mainly by attaching hardware devices to prevent the scattered photons from reaching the detector, so that there is no scattered signal.
  • the following are two typical methods for preprocessing shadow correction.
  • the first is to increase the air gap between the object and the detector, and the second is to use an anti-scatter grid. As the air gap widens, the detected rate of diffused scattered photons decreases, while the source signal is unaffected.
  • the first method is limited by the physical space of the CBCT device itself.
  • the spatial distance cannot be increased wirelessly, and the geometric blurring of the image is increased while increasing the X-ray dose to compensate for the increase in distance.
  • the anti-scatter grid uses a grid of lead grids that are focused on the source to block scattered light at unfocused angles of incidence.
  • the second method also has a defect that the attenuation efficiency of the scattered light is not high.
  • the commercial grid can only provide about 3 times the SPR reduction rate, and cannot guarantee the CBCT image quality in a high scattering environment.
  • it also needs to increase the patient's exposure dose to compensate for the attenuation of the source ray intensity, and the clinical application value is not high.
  • Post-processing refers to the estimation of the scattering distribution after the X-ray projection is acquired according to the original method, and the estimated scattering distribution is subtracted from the source projection, and the shading correction can be performed.
  • Post-processing methods include: analytical modeling, Monte Carlo simulation, source modulation, measurement, based on prior data correction, polar coordinate based correction and adaptive iterative shadow correction.
  • the analytical modeling method considers that the scattered signal is the sound of the source signal after passing through the scattering core. Should be, the calculation speed is very fast, but the corresponding scattering estimation accuracy is limited and cumbersome adjustment parameters are required for complex objects.
  • the source modulation method is to add a high frequency modulator between the x-ray source and the object, and separate them in the frequency domain according to different response characteristics of the scattering and source signals, but the method requires high precision in manufacturing the modulation board, The clinical application effect is limited by the actual physical factors; the measurement method is to insert a source ray blocker (usually a lead strip) between the x-ray source and the object, so that a shadow area containing only the scatter signal is formed on the detector, but The method is to change the hardware settings of the system, which is difficult to operate.
  • the polar coordinate-based correction method estimates the shadow distribution of the CT image by the distribution characteristics of the shadow of the CT image in polar coordinates, but this method requires polar transformation and interpolation of the image, which takes a long time.
  • the adaptive iterative shading correction method does not require a priori image information, but the method requires repeated pre-projection operations on the reconstructed image, and the calculation efficiency is low.
  • Embodiments of the present invention provide a CT image shading correction method, apparatus, and electronic device to reduce loss of image spatial resolution and quickly correct an original CT reconstructed image.
  • An embodiment of the present invention provides a CT image shadow correction method, including:
  • Shadow correction is performed based on the smoothed image and the template image.
  • the image texture removal operation is performed on the original CT reconstructed image to obtain a smooth image, including:
  • the original CT reconstructed image is edge-protected and the image texture is removed by using an L0 norm smoothing algorithm to obtain the smoothed image.
  • the structural components of the original CT reconstructed image are segmented according to human tissue, and the template image is constructed, including:
  • the original CT reconstructed image is segmented into a plurality of human tissue regions
  • the CT values of the corresponding tissue corresponding to the X-ray tube voltage are respectively filled in different human tissue regions to obtain the template image.
  • the shading correction is performed according to the smoothed image and the template image, including:
  • the original CT reconstructed image is subjected to compensation processing using the CT image shadow distribution to obtain a corrected CT image.
  • the low-pass filtering process is performed on the structural error of the residual image, including:
  • the tissue error of the residual image is low-pass filtered by a Savitzky-Golay local low-pass filter.
  • performing image texture removal on the original CT reconstructed image by using an L0 norm smoothing algorithm including:
  • the textureless smooth image is calculated by the objective function, which is as follows:
  • S p is the p-th pixel index of the texture-free smooth image S
  • I p is the p-th pixel index of the original CT reconstructed image I
  • C(S) is The number of pixel indices p
  • ⁇ is the smoothing factor
  • An embodiment of the present invention further provides a CT image shading correction apparatus, including:
  • a smooth image generating unit configured to perform an image texture removing operation on the original CT reconstructed image to obtain a smooth image
  • a template image construction unit configured to perform segmentation processing on a structural component of the original CT reconstructed image according to human body tissue, and construct a template image
  • a correction unit configured to perform shading correction according to the smoothed image and the template image.
  • the smooth image generating unit is specifically configured to:
  • the original CT reconstructed image is edge-protected and the image texture is removed by using an L0 norm smoothing algorithm to obtain the smoothed image.
  • the template image construction unit includes:
  • a segmentation module configured to segment the original CT reconstructed image into a plurality of human tissue regions by using an image segmentation method
  • the stencil building module is configured to respectively fill the CT values of the corresponding tissue corresponding X-ray tube voltages in different human tissue regions to obtain the stencil image.
  • the correction unit comprises:
  • a residual image construction module configured to perform a difference image with the template image to obtain a residual image, where the residual image includes image shadows and organizational structure errors;
  • a low-pass filtering module configured to perform low-pass filtering processing on the structural error of the residual image to obtain a shadow distribution of the CT image
  • the compensation processing module is configured to perform compensation processing on the original CT reconstructed image by using the CT image shadow distribution to obtain a corrected CT image.
  • the low pass filter module is specifically configured to:
  • the tissue error of the residual image is low-pass filtered by a Savitzky-Golay local low-pass filter.
  • An embodiment of the present invention further provides an electronic device, where the electronic device includes:
  • a memory comprising computer readable instructions that, when executed, cause the processor to:
  • Shadow correction is performed based on the smoothed image and the template image.
  • the instruction causes the processor to perform edge protection and image texture removal on the original CT reconstructed image by using an L0 norm smoothing algorithm to obtain the smoothed image.
  • the instructions cause the processor to perform the following operations:
  • the original CT reconstructed image is segmented into a plurality of human tissue regions
  • the CT values of the corresponding tissue corresponding to the X-ray tube voltage are respectively filled in different human tissue regions to obtain the template image.
  • the instructions cause the processor to perform the following operations:
  • the original CT reconstructed image is subjected to compensation processing using the CT image shadow distribution to obtain a corrected CT image.
  • the instructions cause the processor to perform a low pass filtering process on the tissue structure error of the residual image using a Savitzky-Golay local low pass filter.
  • the invention utilizes the edge-protected L0 norm smoothing algorithm to decompose the image into a structural image and a texture image, eliminates the texture information and performs subsequent processing without loss of image resolution. Segmentation of human tissue using image segmentation algorithms, An accurate reference image can be generated. Through the L0 norm smoothing algorithm, the detailed information of the image can be protected to the utmost, and the image spatial resolution loss is small.
  • the present invention eliminates the need for a priori CT information, eliminates the need for front projection operations and polar coordinate conversion, and therefore has a faster calculation speed; it is fully compatible with the image-guided CBCT system of modern radiotherapy accelerators without changing other hardware and scanning protocols.
  • FIG. 1 is a flowchart of a CT image shading correction method according to an embodiment of the present invention
  • FIG. 2 is a schematic view showing the operation of an image shading correction method according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of smoothing and correcting images under different smoothing intensity factors ⁇ according to an embodiment of the present invention
  • FIG. 4 is a schematic structural diagram of a CT image shading correction apparatus according to an embodiment of the present invention.
  • FIG. 5 is another schematic structural diagram of a CT image shading correction apparatus according to an embodiment of the present invention.
  • FIG. 6 is a schematic block diagram of a system configuration of an electronic device 600 according to an embodiment of the present invention.
  • FIG. 1 is a flowchart of a CT image shading correction method according to an embodiment of the present invention.
  • the CT image shading correction method includes:
  • S102 Perform segmentation processing on the structural components of the original CT reconstructed image according to human tissue, and construct a template image;
  • S103 Perform shading correction according to the smoothed image and the template image.
  • the execution body of the CT image shading correction method shown in FIG. 1 may be an electronic device, and the electronic device may be a desktop computer, a tablet computer, or the like, but the present invention is not limited thereto.
  • the present invention first performs an image texture removal operation on the original CT reconstructed image, and then performs a segmentation process on the original CT reconstructed image, and finally performs a shadow according to the smoothed image obtained by the de-texturing operation and the segmented template image. Correction can reduce the loss of image spatial resolution and quickly correct the original CT reconstructed image.
  • the texture of the image is a high frequency signal relative to the CT image shadow signal.
  • the original CT reconstructed image may be decomposed into textures and structures.
  • the original CT reconstructed image may be edge-protected by using the L0 norm smoothing algorithm.
  • the image texture is removed to obtain the smoothed image.
  • the L0 norm smoothing algorithm with edge protection is as follows:
  • I be the original CT reconstructed image of the input, and S be the smooth image with no texture output.
  • Equation 1 For a pixel index p, calculate the sum of the gradient differences in both the x and y directions as its gradient value, as shown in Equation 1:
  • the untextured smooth image S can be obtained by the following objective function, see Equation 2:
  • Equation 1 and Equation 2 ⁇ (SI) 2 is the image structure similarity constraint, S p is the p-th pixel index of the texture-free smooth image S, and I p is the p-th pixel index of the original CT reconstructed image I, ⁇ is the smoothing factor, and The partial derivative of the x and y in the two coordinate directions of the image is indexed for the pixel.
  • the present invention divides the algorithm into two sub-problems to solve separately, and adopts a special alternating optimization strategy of semi-quadratic splitting to obtain an approximate optimal solution.
  • CT values of the same type of human tissue in CBCT images without artifacts should be basically the same, and therefore, the method of image segmentation can be utilized.
  • the structural components in CT images can be divided into air, bone, soft tissue (such as muscle and fat), etc.
  • Image segmentation method can be used to segment the original CT reconstructed image into multiple human tissue regions, that is, to segment different tissues of the human body. , fill the corresponding tissue with the corresponding tissue area in the corresponding X-ray
  • the standard CT value at the line tube voltage produces a stencil image that can be used as a reference image for the shading correction process.
  • the present invention may employ a multi-threshold image segmentation algorithm that separates the bone and soft tissue of the patient's head and assigns standard CT values.
  • the present invention needs to perform shading correction using a smooth image and a stencil image. Therefore, it is necessary to make the smooth image and the stencil image to be inferior to obtain a residual image, and the residual image includes image shading and a small amount of tissue structure error. Since the image shadow is mainly a low frequency signal, and the tissue structure is mainly a high frequency signal, a low pass filter can be used to eliminate the structural error and obtain a shadow distribution of the CT image.
  • the present invention uses a Savitzky-Golay local low pass filter to filter the residual image on the image domain.
  • the low pass filter preserves the contour features and avoids the contrast resolution of the corrected loss image. .
  • the CT reconstructed image is compensated by the shadow distribution of the CT image, and the corrected CT image can be obtained.
  • FIG. 2 is a schematic diagram of the operation of the image shading correction method according to the embodiment of the present invention. The specific operation flow is described below with reference to FIG. 2 .
  • the L0 norm smoothing process and the image segmentation are respectively performed on the pre-correction image (original CT reconstructed image), and the L0 norm smoothing process is performed to obtain a textureless image (smooth image), and the image is segmented to obtain a template image.
  • the residual image can be obtained by smoothing the image and the template image, and the residual image can be low-pass filtered to obtain a shadow distribution image.
  • the obtained shadow distribution image is superimposed with the pre-correction image (compensation processing) to obtain a corrected image, and image correction is completed.
  • L0 norm smoothing algorithm affects the degree of image texture removal and the edge protection of tissue structure. Full image texture removal helps to preserve the detail retention of the corrected image without compromising the image resolution. Good edge protection helps to estimate the accuracy of image shadows and improve the contrast between the corrected image tissue. Therefore, in the L0 norm smoothing process, the selection of the smoothing intensity factor ⁇ should be balanced between the two. Different ⁇ selection affects the image. As shown in Fig. 3, the first line image of Fig. 3 is respectively an L0 norm smooth image under three different sizes ⁇ , and the second line image of Fig. 3 is a corrected image under the corresponding ⁇ . When ⁇ is taken too small, as shown in the first column image of FIG.
  • the invention combines L0 norm smoothing with image segmentation to perform CT image shading correction, effectively eliminating image shading.
  • the method utilizes the edge-protected L0 norm smoothing algorithm to decompose the image into a structural image and a texture image, and eliminates the texture information and then performs subsequent processing without loss of image resolution.
  • the invention successfully segmentes the human tissue using an accurate image segmentation algorithm and generates an accurate reference image.
  • the CT image shading correction technology based on L0 norm smoothing and image segmentation has the following advantages in addition to the image shading correction effect:
  • the L0 norm smoothing maximizes the protection of the image details, and the image spatial resolution loss is small
  • the invention does not need a priori CT information, does not require a front projection operation, does not require polar coordinate conversion, and has a faster calculation speed;
  • Embodiment 2 of the present invention provides a CT image shading correction apparatus.
  • the apparatus can be applied to the electronic apparatus in Embodiment 1. Since the principle of solving the problem is similar to the method of Embodiment 1, the specific implementation may refer to the implementation of the method of Embodiment 1, and the repeated description is not repeated.
  • the CT image shading correction apparatus includes a smooth image generation unit 401, a template image construction unit 402, and a correction unit 403.
  • the smooth image generating unit 401 is configured to perform an image texture removing operation on the original CT reconstructed image to obtain a smoothed image
  • the template image construction unit 402 is configured to perform segmentation processing on the structural components of the original CT reconstructed image according to human body tissue to construct a template image;
  • the correcting unit 403 is configured to perform shading correction according to the smoothed image and the template image.
  • the smooth image generating unit 401 may be configured to perform edge protection and image texture removal on the original CT reconstructed image by using an L0 norm smoothing algorithm to obtain the smoothed image.
  • FIG. 5 is another schematic structural diagram of a CT image shading correction apparatus according to an embodiment of the present invention.
  • the template image construction unit 402 includes a segmentation module 501 and a template construction module 502.
  • the correcting unit 403 includes a residual image constructing module 503, a low pass filtering module 504, and a compensation processing module 505.
  • the segmentation module 501 is configured to segment the original CT reconstructed image into a plurality of human tissue regions by using an image segmentation method
  • the stencil building module 502 is configured to respectively fill the CT values of the corresponding tissue corresponding X-ray tube voltages in different human tissue regions to obtain the stencil image.
  • the residual image construction module 503 is configured to perform the difference between the smoothed image and the template image to obtain a residual image, where the residual image includes image shadows and organizational structure errors;
  • the low-pass filtering module 503 is configured to perform low-pass filtering processing on the structural error of the residual image to obtain a CT image shadow distribution; the low-pass filtering module 503 may use the Savitzky-Golay local low-pass filter to perform the residual The organization error of the image is subjected to low-pass filtering.
  • the compensation processing module 504 is configured to perform compensation processing on the original CT reconstructed image by using the CT image shadow distribution to obtain a corrected CT image.
  • the apparatus of the embodiment uses the edge-protected L0 norm smoothing algorithm to decompose the image into a structural image and a texture image, and eliminates the texture information and performs subsequent processing without loss of image resolution.
  • the image segmentation algorithm is used to segment the human tissue to generate an accurate reference image.
  • the L0 norm smoothing algorithm Through the L0 norm smoothing algorithm, the detailed information of the image can be protected to the utmost, and the image spatial resolution loss is small.
  • the present invention eliminates the need for a priori CT information, eliminates the need for front projection operations and polar coordinate conversion, and therefore has a faster calculation speed; it is fully compatible with the image-guided CBCT system of modern radiotherapy accelerators without changing other hardware and scanning protocols.
  • the embodiment 3 provides an electronic device, which may be a desktop computer, a tablet computer, a mobile terminal, etc., and the embodiment is not limited thereto.
  • the electronic device may refer to the implementation of the method of Embodiment 1 and the apparatus described in Embodiment 2, and the content thereof is incorporated herein, and the details are not described again.
  • FIG. 6 is a schematic block diagram of a system configuration of an electronic device 600 according to an embodiment of the present invention.
  • the electronic device 600 can include a central processing unit 100 and a memory 140; the memory 140 is coupled to the central processing unit 100.
  • the figure is exemplary; other types of structures may be used in addition to or in place of the structure to implement telecommunications functions or other functions.
  • the CT image shading correction function can be integrated into the central processing unit 100.
  • the central processing unit 100 may be configured to perform the following operations: performing an image texture removal operation on the original CT reconstructed image to obtain a smooth image; and performing segmentation processing on the structural components of the original CT reconstructed image according to human body tissue to construct a template image; Shadow correction is performed based on the smoothed image and the template image.
  • the image texture removal operation is performed on the original CT reconstructed image to obtain a smooth image, including: performing edge protection and image texture removal on the original CT reconstructed image by using a L0 norm smoothing algorithm to obtain the smoothed image.
  • the method for segmenting the structural components of the original CT reconstructed image according to the human body tissue, and constructing the template image comprises: dividing the original CT reconstructed image into a plurality of human tissue regions by using an image segmentation method; respectively, in different human bodies The tissue area is filled with the CT value of the corresponding tissue corresponding to the X-ray tube voltage to obtain the template image.
  • Performing shading correction according to the smoothed image and the template image includes: performing difference between the smoothed image and the template image to obtain a residual image, where the residual image includes image shading and organizational structure error;
  • the structural error of the difference image is subjected to low-pass filtering processing to obtain a shadow distribution of the CT image; the original CT reconstructed image is compensated by the shadow distribution of the CT image to obtain a corrected CT image.
  • the low-pass filtering process is performed on the structural error of the residual image, including: performing a low-pass filtering process on the structural error of the residual image by using a Savitzky-Golay local low-pass filter.
  • the image texture removal is performed on the original CT reconstructed image by using an L0 norm smoothing algorithm, including:
  • the textureless smooth image is calculated by the objective function, which is as follows:
  • S p is the p-th pixel index of the texture-free smooth image S
  • I p is the p-th pixel index of the original CT reconstructed image I
  • the number of pixel indices p, ⁇ is the smoothing factor, and The partial derivative of the pixel index in both the x and y directions.
  • the CT image shading correction device may be configured separately from the central processing unit 100.
  • the CT image shading correction device may be configured as a chip connected to the central processing unit 100, and the CT image is implemented by the control of the central processing unit. Shadow correction function.
  • the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processing unit 130, a display 160, and a power source 170. It should be noted that the electronic device 600 does not have to include all the components shown in FIG. 6; in addition, the electronic device 600 may further include components not shown in FIG. 6, and reference may be made to the prior art.
  • central processor 100 also sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device that receives input and controls various components of electronic device 600. The operation of the part.
  • the memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable device.
  • the above-mentioned information related to the failure can be stored, and a program for executing the related information can be stored.
  • the central processing unit 100 can execute the program stored by the memory 140 to implement information storage or processing and the like.
  • Input unit 120 provides input to central processor 100.
  • the input unit 120 is, for example, a button or a touch input device.
  • the power source 170 is used to provide power to the electronic device 600.
  • the display 160 is used to display a display object such as an image or a character.
  • the display may be, for example, an LCD display, but is not limited thereto.
  • the memory 140 can be a solid state memory such as a read only memory (ROM), a random access memory (RAM), a SIM card, or the like. It is also possible to store a memory that can be selectively erased and provided with more data even when the power is turned off, and an example of the memory is sometimes referred to as an EPROM or the like. Memory 140 can also be some other type of device. Memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142 for storing an application and a function program or a flow for executing an operation of the electronic device 600 by the central processing unit 100.
  • ROM read only memory
  • RAM random access memory
  • SIM card or the like. It is also possible to store a memory that can be selectively erased and provided with more data even when the power is turned off, and an example of the memory is sometimes referred to as an EPROM or the like. Memory 140 can also be some other type of device. Memory 140 includes a buffer memory 141 (sometimes referred to as
  • the memory 140 may also include a data storage portion 143 for storing data such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device.
  • the driver storage portion 144 of the memory 140 may include various drivers for the communication function of the electronic device and/or for performing other functions of the electronic device such as a messaging application, an address book application, and the like.
  • the communication module 110 is a transmitter/receiver 110 that transmits and receives signals via the antenna 111.
  • a communication module (transmitter/receiver) 110 is coupled to the central processing unit 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
  • a plurality of communication modules 110 such as a cellular network module, a Bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device.
  • the communication module (transmitter/receiver) 110 is also coupled to the speaker 131 and microphone 132 via the audio processor 130 to provide an audio output via the speaker 131 and to receive audio input from the microphone 132 to achieve the usual telecommunications functions.
  • Audio processor 130 may include any suitable buffer, decoder, amplifier, or the like.
  • the audio processor 130 is also coupled to the central processing unit 100 such that recording can be performed on the local unit through the microphone 132, and the sound stored on the local unit can be played through the speaker 131.
  • An embodiment of the present invention further provides a computer readable program, wherein when the program is executed in an electronic device, the program causes a computer to perform a CT image shading correction method as described in Embodiment 1 above in the electronic device .
  • the embodiment of the present invention further provides a storage medium storing a computer readable program, wherein the computer readable program causes the computer to perform the CT image shading correction described in Embodiment 1 above in the electronic device.
  • embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

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Abstract

一种CT图像阴影校正方法、装置及电子设备(600），该CT图像阴影校正方法包括：对原始CT重建图像进行图像纹理去除操作，得到平滑图像(S101)；根据人体组织对原始CT重建图像的结构成分进行分割处理，构建模版图像(S102)；根据平滑图像及模版图像进行阴影校正(S103)。该方法可以减小图像空间分辨率的损失，快速的对原始CT重建图像进行校正。

Description

CT图像阴影校正方法、装置及电子设备 技术领域
本发明涉及图像阴影校正技术领域，特别涉及一种CT图像阴影校正方法、装置及电子设备。
背景技术
在锥形束CT(cone-beam CT，简称CBCT)扫描中，由于散射信号和射束硬化效应的影响，重建图像中出现低频的CT图像阴影，CT图像阴影严重影响了图像CT值的准确性和图像的空间均匀度。在无阴影校正的CBCT系统中，CT图像阴影导致重建图像的CT值误差可超过350HU，给影像引导治疗的定位精度和影像的诊断带来误差，因而限制了CBCT在临床中的广泛应用。目前CBCT主要还是用于初步定位和放疗摆位，在介入和放疗中的进一步应用受到限制，因此CT图像阴影校正是提升CBCT图像质量首要解决的重要问题之一。
目前已知的CT图像阴影校正的方法主要可以分为两大类：预处理和后处理方法。预处理方法校正CT图像阴影主要是通过附加硬件装置，阻止散射光子到达探测器，这样也就不存在散射信号。下面是预处理进行阴影校正的两个典型方法，第一是增加物体与探测器之间的空气隙，第二是使用抗散射线栅。随着空气隙加宽，扩散开的散射光子的探测率会降低，而源信号则不受影响。但第一种方法受CBCT设备本身物理空间的限制，空间距离不能无线增大，而且增大的同时加大了影像的几何模糊，同时还需要增加X线剂量来弥补距离的增加，在临床实际中并不实用。抗散射线栅使用聚焦于射线源的铅栅网格，能阻挡非聚焦入射角的散射光。第二种方法也存在对散射光的衰减效率不高的缺陷。目前商用线栅只能提供约3倍的SPR降低率，无法保证高散射环境下的CBCT图像质量。此外，它还需要增加病人的受照剂量来补偿被衰减掉的源射线强度，临床应用价值不高。
虽然预处理方法能直接阻止散射光子到达探测器，但其局限性更加突出，后处理方法更实用。后处理指的是按照原来的方法采集X射线投影后，再通过后处理估计散射分布，通过源投影减去估计出的散射分布，即可对其进行阴影校正。后处理方法包括：解析建模法、蒙特卡罗模拟法、源调制法、测量法，基于先验数据校正法，基于极坐标的校正法和自适应迭代阴影校正法。解析建模法认为散射信号是源信号通过散射核后的响 应，计算速度很快，但是相应的散射估计精度有限且对复杂物体需要繁琐的调整参数。蒙特卡罗模拟法是散射估计的“金标准”，但该方法计算量极大，十分耗时。源调制法是在x射线源和物体之间加入高频调制器，根据散射和源信号不同的响应特性，在频率域上把它们分离开，但该方法对调制板的制造精度要求高，其临床应用效果受到实际物理因素的限制；测量法是在x射线源和受照物之间插入源射线阻挡器(通常是铅条)，这样探测器上形成仅包含散射信号的阴影区，但是该方法要改变系统的硬件设置，操作难度较大。基于先验数据校正法虽能较好的得到校正图像，但是该方法需要借助放射治疗中额外的先验病患信息，因此无法作为大体积CT成像系统阴影校正的通用解决方案。基于极坐标的校正法通过CT图像阴影在极坐标下的分布特征，估计出CT图像阴影分布，但是该方法需要对图像进行极坐标转化和插值操作，耗时较长。自适应迭代阴影校正法无需先验图像信息，但是该方法需要对重建图像进行反复前投影操作，计算效率低。
发明内容
本发明实施例提供了CT图像阴影校正方法、装置及电子设备，以减小图像空间分辨率的损失，快速的对原始CT重建图像进行校正。
本发明实施例提供了一种CT图像阴影校正方法，包括：
对原始CT重建图像进行图像纹理去除操作，得到平滑图像；
根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像；
根据所述平滑图像及模版图像进行阴影校正。
一实施例中，对原始CT重建图像进行图像纹理去除操作，得到平滑图像，包括：
利用L0范数平滑算法对所述原始CT重建图像进行边缘保护同时及图像纹理去除，得到所述平滑图像。
一实施例中，根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像，包括：
采用图像分割方法，将所述原始CT重建图像分割出多个人体组织区域；
分别在不同的人体组织区域填充相应组织对应X射线球管电压下的CT值，得到所述模版图像。
一实施例中，根据所述平滑图像及模版图像进行阴影校正，包括：
将所述平滑图像与模版图像做差，得到残差图像，所述残差图像中包括图像阴影及组织结构误差；
对所述残差图像的组织结构误差进行低通滤波处理，得到CT图像阴影分布；
利用所述CT图像阴影分布对所述原始CT重建图像进行补偿处理，得到修正后的CT图像。
一实施例中，对所述残差图像的组织结构误差进行低通滤波处理，包括：
利用Savitzky-Golay局部低通滤波器对所述残差图像的组织结构误差进行低通滤波处理。
一实施例中，利用L0范数平滑算法对所述原始CT重建图像进行图像纹理去除，包括：
通过目标函数计算无纹理平滑图像，所述目标函数如下：
Figure PCTCN2017070410-appb-000001
其中，
Figure PCTCN2017070410-appb-000002
Sp为无纹理平滑图像S的第p个像素索引，Ip为原始CT重建图像I的第p个像素索引，C(S)为
Figure PCTCN2017070410-appb-000003
像素索引p的个数，λ为平滑因子，
Figure PCTCN2017070410-appb-000004
及
Figure PCTCN2017070410-appb-000005
为像素索引在x及y两个方向的偏导数。
本发明实施例还提供了一种CT图像阴影校正装置，包括：
平滑图像生成单元，用于对原始CT重建图像进行图像纹理去除操作，得到平滑图像；
模版图像构建单元，用于根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像；
校正单元，用于根据所述平滑图像及模版图像进行阴影校正。
一实施例中，所述平滑图像生成单元具体用于：
利用L0范数平滑算法对所述原始CT重建图像进行边缘保护同时及图像纹理去除，得到所述平滑图像。
一实施例中，所述模版图像构建单元包括：
分割模块，用于采用图像分割方法，将所述原始CT重建图像分割出多个人体组织区域；
模版构建模块，用于分别在不同的人体组织区域填充相应组织对应X射线球管电压下的CT值，得到所述模版图像。
一实施例中，所述校正单元包括：
残差图像构建模块，用于将所述平滑图像与模版图像做差，得到残差图像，所述残差图像中包括图像阴影及组织结构误差；
低通滤波模块，用于对所述残差图像的组织结构误差进行低通滤波处理，得到CT图像阴影分布；
补偿处理模块，用于利用所述CT图像阴影分布对所述原始CT重建图像进行补偿处理，得到修正后的CT图像。
一实施例中，所述低通滤波器模块具体用于：
利用Savitzky-Golay局部低通滤波器对所述残差图像的组织结构误差进行低通滤波处理。
本发明实施例还提供了一种电子设备，该电子设备包括：
处理器；和
包括计算机可读指令的存储器，所述计算机可读指令在被执行时使所述处理器执行以下操作：
对原始CT重建图像进行图像纹理去除操作，得到平滑图像；
根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像；
根据所述平滑图像及模版图像进行阴影校正。
一实施例中，所述指令使所述处理器利用L0范数平滑算法对所述原始CT重建图像进行边缘保护同时及图像纹理去除，得到所述平滑图像。
一实施例中，所述指令使所述处理器执行以下操作：
采用图像分割方法，将所述原始CT重建图像分割出多个人体组织区域；
分别在不同的人体组织区域填充相应组织对应X射线球管电压下的CT值，得到所述模版图像。
一实施例中，所述指令使所述处理器执行以下操作：
将所述平滑图像与模版图像做差，得到残差图像，所述残差图像中包括图像阴影及组织结构误差；
对所述残差图像的组织结构误差进行低通滤波处理，得到CT图像阴影分布；
利用所述CT图像阴影分布对所述原始CT重建图像进行补偿处理，得到修正后的CT图像。
一实施例中，所述指令使所述处理器利用Savitzky-Golay局部低通滤波器对所述残差图像的组织结构误差进行低通滤波处理。
本发明利用边缘保护的L0范数平滑算法把图像分解为结构图像和纹理图像，消除纹理信息消再做后续处理，不损失图像分辨率。采用图像分割算法地将人体组织分割， 可以生成精确的参考图像。通过L0范数平滑算法，可以最大限度地保护图像的细节信息，图像空间分辨率损失小。另外，本发明无需先验的CT信息，无需前投影操作及极坐标转换，因此计算速度较快；完全兼容现代放疗加速器的图像引导CBCT系统，不用改变其它硬件和扫描协议。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本发明实施例的CT图像阴影校正方法流程图；
图2为本发明实施例的图像阴影校正方法工作示意图；
图3为本发明实施例的不同平滑强度因子λ下的平滑和校正图像示意图；
图4为本发明实施例的CT图像阴影校正装置的一结构示意图；
图5为本发明实施例的CT图像阴影校正装置的另一结构示意图；
图6为本发明实施例的电子设备600的系统构成的示意框图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
实施例1
本发明实施例提供了一种CT图像阴影校正方法，图1为本发明实施例的CT图像阴影校正方法流程图。如图1所示，该CT图像阴影校正方法包括：
S101：对原始CT重建图像进行图像纹理去除操作，得到平滑图像；
S102：根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像；
S103：根据所述平滑图像及模版图像进行阴影校正。
图1所示的CT图像阴影校正方法的执行主体可以为电子设备，该电子设备可以为台式计算机、平板电脑等，但本发明不限于此。
由图1所示的流程可知，本发明首先对原始CT重建图像进行图像纹理去除操作，然后对原始CT重建图像进行分割处理，最后根据去纹理操作得到的平滑图像及分割得到的模版图像进行阴影校正，可以减小图像空间分辨率的损失，快速的对原始CT重建图像进行校正。
相对于CT图像阴影信号而言，图像的纹理属于高频信号。进行图像阴影校正时，为了保护图像细节，不损失图像空间分辨率，可以将原始CT重建图像分解为纹理和结构，例如可以利用L0范数平滑算法对所述原始CT重建图像进行边缘保护同时及图像纹理去除，得到所述平滑图像。
具有边缘保护功能的L0范数平滑算法过程如下：
设I为输入的原始CT重建图像，S为输出无纹理的平滑图像。
Figure PCTCN2017070410-appb-000006
为图像的梯度，其中，p为图像的像素索引，x和y分别为图像的横纵两个方向的坐标。
对于一个像素索引p，计算其在x和y两个方向的梯度差之和作为它的梯度值，如公式1所示：
Figure PCTCN2017070410-appb-000007
上式像素索引p的个数。
根据上述定义，无纹理的平滑图像S可以通过以下目标函数求得，见公式2：
Figure PCTCN2017070410-appb-000009
公式1及公式2中，∑(S-I)2为图像结构相似性约束项，Sp为无纹理平滑图像S的第p个像素索引，Ip为原始CT重建图像I的第p个像素索引，λ为平滑因子，
Figure PCTCN2017070410-appb-000010
及
Figure PCTCN2017070410-appb-000011
为像素索引在图像的两个坐标方向x及y两个的偏导数。
因L0范数的不可解性，所以本发明将算法分为两个子问题进行分别求解，采用半二次分裂的特殊交替优化策略得到近似的最优解。
根据解剖常识，同种类型的人体组织在没有伪影的CBCT图像中的CT值应该基本一致，因此，可以利用图像分割的方法。CT图像中的结构成分可分为空气、骨头、软组织(如肌肉和脂肪)区域等，采用图像分割方法，可以将原始CT重建图像分割出多个人体组织区域，即把人体不同的组织分割出来，用对应的组织区域填充相应组织在对应X射 线球管电压下的标准CT值，生成一幅模版图像，该模版图像可以作为阴影校正过程的参考图像。
一实施例中，本发明可以采用多阈值的图像分割算法，分离病人头部的骨骼和软组织并赋予标准的CT值。
本发明需要利用平滑图像及模版图像进行阴影校正，因此需要将所述平滑图像与模版图像做差，得到残差图像，残差图像中包括图像阴影及少量的组织结构误差。由于图像阴影主要为低频信号，而组织结构则主要是高频信号，因此可利用低通滤波器消除组织结构误差，得到CT图像阴影分布。
普通的低通滤波可以消除组织结构的误差，但是解剖结构的边界轮廓也会被滤除，导致图像对比度的严重丢失。在一较佳实施例中，本发明采用Savitzky-Golay局部低通滤波器在图像域上对残差图像进行滤波，该低通滤波器可以保持轮廓特征，避免了校正后损失图像的对比度分辨率。
在得到CT图像阴影分布之后，利用CT图像阴影分布对所述CT重建图像进行补偿处理，就可以得到修正后的CT图像。
图2为本发明实施例的图像阴影校正方法工作示意图，下面结合图2说明具体地操作流程。如图2所示，对修正前图像(原始CT重建图像)分别进行L0范数平滑处理及图像分割出来，L0范数平滑处理得到无纹理的图像(平滑图像)，图像分割出来处理得到模版图像。利用平滑图像及模版图像可以得到残差图像，对残差图像进行低通滤波，就可以得到阴影分布图像。最后，将得到的阴影分布图像与修正前图像进行叠加(补偿处理)，得到修正后的图像，完成图像校正。
纹理去除和边缘保护是一对矛盾，L0范数平滑算法的参数选择影响图像纹理去除程度与组织结构边缘保护的效果。充分的图像纹理去除，有助于校正后图像的细节保留，不破坏图像分辨率。良好的边缘保护，有助于估计图像阴影的准确性，提高校正后图像组织间的对比度。因此，在L0范数平滑处理过程中，平滑强度因子λ的选择应在两者间取得平衡。不同λ选择对图像造成影响如图3所示，图3第一行图像分别为3个大小不同λ下的L0范数平滑图像，图3第二行图像为在相应λ下的校正图像。当λ取得过小时，如图3第一列图像，纹理去除不充分导致估计出的阴影信号中包含图像细节，阴影信号补偿至原始图像得到的校正图像损失图像细节。当λ取得过大时，如图3第三列图像，因平滑程度过强导致组织结构边缘模糊，估计出的阴影信息中包含结构信息，因此校正后图像组织结 构之间对比度降低。当λ取0.01时，在纹理去除和边缘保护之间取得了平衡，在保护图像细节的同时，不降低组织间对比度。
本发明将L0范数平滑与图像分割相结合进行CT图像阴影校正，有效地消除了图像阴影。与现有方法不同的是，本方法利用边缘保护的L0范数平滑算法，把图像分解为结构图像和纹理图像，把纹理信息消除后再做后续处理，不损失图像分辨率。本发明采用精确的图像分割算法成功地将人体组织分割，生成了精确的参考图像。基于L0范数平滑和图像分割的CT图像阴影校正技术除了图像阴影校正效果明显外，还具有以下优点：
1.通过L0范数平滑最大限度地保护了图像的细节信息，图像空间分辨率损失小；
2.与现有的校正算法相比，本发明无需先验的CT信息，无需前投影操作，无需极坐标转换，计算速度较快；
3.完全兼容现代放疗加速器的图像引导CBCT系统，不用改变其它硬件和扫描协议。
实施例2
本发明实施例2提供了一种CT图像阴影校正装置该装置可以应用于实施例1中的电子设备。由于该装置解决问题的原理与实施例1的方法类似，因此其具体的实施可以参照实施例1的方法的实施，重复之处不再赘述。
图4为本发明实施例的CT图像阴影校正装置的一结构示意图，如图4所示，该CT图像阴影校正装置包括：平滑图像生成单元401，模版图像构建单元402及校正单元403。
平滑图像生成单元401用于对原始CT重建图像进行图像纹理去除操作，得到平滑图像；
模版图像构建单元402用于根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像；
校正单元403用于根据所述平滑图像及模版图像进行阴影校正。
本实施例中，平滑图像生成单元401可以用于：利用L0范数平滑算法对所述原始CT重建图像进行边缘保护同时及图像纹理去除，得到所述平滑图像。
图5为本发明实施例的CT图像阴影校正装置的另一结构示意图，模版图像构建单元402包括：分割模块501及模版构建模块502。校正单元403包括：残差图像构建模块503、低通滤波模块504及补偿处理模块505。
分割模块501用于采用图像分割方法，将所述原始CT重建图像分割出多个人体组织区域；
模版构建模块502用于分别在不同的人体组织区域填充相应组织对应X射线球管电压下的CT值，得到所述模版图像。
残差图像构建模块503用于将所述平滑图像与模版图像做差，得到残差图像，所述残差图像中包括图像阴影及组织结构误差；
低通滤波模块503用于对所述残差图像的组织结构误差进行低通滤波处理，得到CT图像阴影分布；该低通滤波模块503可以利用Savitzky-Golay局部低通滤波器对所述残差图像的组织结构误差进行低通滤波处理。
补偿处理模块504用于利用所述CT图像阴影分布对所述原始CT重建图像进行补偿处理，得到修正后的CT图像。
本实施例的装置利用边缘保护的L0范数平滑算法把图像分解为结构图像和纹理图像，消除纹理信息消再做后续处理，不损失图像分辨率。采用图像分割算法地将人体组织分割，可以生成精确的参考图像。通过L0范数平滑算法，可以最大限度地保护图像的细节信息，图像空间分辨率损失小。另外，本发明无需先验的CT信息，无需前投影操作及极坐标转换，因此计算速度较快；完全兼容现代放疗加速器的图像引导CBCT系统，不用改变其它硬件和扫描协议。
实施例3
本实施例3提供一种电子设备，该电子设备可以是台式计算机、平板电脑及移动终端等，本实施例不限于此。在本实施例中，该电子设备可以参照实施例1的方法的实施及实施例2所述的装置，其内容被合并于此，重复之处不再赘述。
图6为本发明实施例的电子设备600的系统构成的示意框图。如图6所示，该电子设备600可以包括中央处理器100和存储器140；存储器140耦合到中央处理器100。值得注意的是，该图是示例性的；还可以使用其他类型的结构，来补充或代替该结构，以实现电信功能或其他功能。
一实施例中，CT图像阴影校正功能可以被集成到中央处理器100中。其中，中央处理器100可以被配置为进行如下控制：对原始CT重建图像进行图像纹理去除操作，得到平滑图像；根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像；根据所述平滑图像及模版图像进行阴影校正。
其中，对原始CT重建图像进行图像纹理去除操作，得到平滑图像，包括：利用L0范数平滑算法对所述原始CT重建图像进行边缘保护同时及图像纹理去除，得到所述平滑图像。
其中，根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像，包括：采用图像分割方法，将所述原始CT重建图像分割出多个人体组织区域；分别在不同的人体组织区域填充相应组织对应X射线球管电压下的CT值，得到所述模版图像。
其中，根据所述平滑图像及模版图像进行阴影校正，包括：将所述平滑图像与模版图像做差，得到残差图像，所述残差图像中包括图像阴影及组织结构误差；对所述残差图像的组织结构误差进行低通滤波处理，得到CT图像阴影分布；利用所述CT图像阴影分布对所述原始CT重建图像进行补偿处理，得到修正后的CT图像。
其中，对所述残差图像的组织结构误差进行低通滤波处理，包括：利用Savitzky-Golay局部低通滤波器对所述残差图像的组织结构误差进行低通滤波处理。
其中，利用L0范数平滑算法对所述原始CT重建图像进行图像纹理去除，包括：
通过目标函数计算无纹理平滑图像，所述目标函数如下：
Figure PCTCN2017070410-appb-000012
其中，
Figure PCTCN2017070410-appb-000013
Sp为无纹理平滑图像S的第p个像素索引，Ip为原始CT重建图像I的第p个像素索引，
Figure PCTCN2017070410-appb-000014
像素索引p的个数，λ为平滑因子，
Figure PCTCN2017070410-appb-000015
及
Figure PCTCN2017070410-appb-000016
为像素索引在x及y两个方向的偏导数。
在另一个实施方式中，CT图像阴影校正装置可以与中央处理器100分开配置，例如可以将CT图像阴影校正装置配置为与中央处理器100连接的芯片，通过中央处理器的控制来实现CT图像阴影校正功能。
如图6所示，该电子设备600还可以包括：通信模块110、输入单元120、音频处理单元130、显示器160、电源170。值得注意的是，电子设备600也并不是必须要包括图6中所示的所有部件；此外，电子设备600还可以包括图6中没有示出的部件，可以参考现有技术。
如图6所示，中央处理器100有时也称为控制器或操作控件，可以包括微处理器或其他处理器装置和/或逻辑装置，该中央处理器100接收输入并控制电子设备600的各个部件的操作。
其中，存储器140，例如可以是缓存器、闪存、硬驱、可移动介质、易失性存储器、非易失性存储器或其它合适装置中的一种或更多种。可储存上述与失败有关的信息，此外还可存储执行有关信息的程序。并且中央处理器100可执行该存储器140存储的该程序，以实现信息存储或处理等。
输入单元120向中央处理器100提供输入。该输入单元120例如为按键或触摸输入装置。电源170用于向电子设备600提供电力。显示器160用于进行图像和文字等显示对象的显示。该显示器例如可为LCD显示器，但并不限于此。
该存储器140可以是固态存储器，例如，只读存储器(ROM)、随机存取存储器(RAM)、SIM卡等。还可以是这样的存储器，其即使在断电时也保存信息，可被选择性地擦除且设有更多数据，该存储器的示例有时被称为EPROM等。存储器140还可以是某种其它类型的装置。存储器140包括缓冲存储器141(有时被称为缓冲器)。存储器140可以包括应用/功能存储部142，该应用/功能存储部142用于存储应用程序和功能程序或用于通过中央处理器100执行电子设备600的操作的流程。
存储器140还可以包括数据存储部143，该数据存储部143用于存储数据，例如联系人、数字数据、图片、声音和/或任何其他由电子设备使用的数据。存储器140的驱动程序存储部144可以包括电子设备的用于通信功能和/或用于执行电子设备的其他功能(如消息传送应用、通讯录应用等)的各种驱动程序。
通信模块110即为经由天线111发送和接收信号的发送机/接收机110。通信模块(发送机/接收机)110耦合到中央处理器100，以提供输入信号和接收输出信号，这可以和常规移动通信终端的情况相同。
基于不同的通信技术，在同一电子设备中，可以设置有多个通信模块110，如蜂窝网络模块、蓝牙模块和/或无线局域网模块等。通信模块(发送机/接收机)110还经由音频处理器130耦合到扬声器131和麦克风132，以经由扬声器131提供音频输出，并接收来自麦克风132的音频输入，从而实现通常的电信功能。音频处理器130可以包括任何合适的缓冲器、解码器、放大器等。另外，音频处理器130还耦合到中央处理器100，从而使得可以通过麦克风132能够在本机上录音，且使得可以通过扬声器131来播放本机上存储的声音。
本发明实施例还提供一种计算机可读程序，其中当在电子设备中执行所述程序时，所述程序使得计算机在所述电子设备中执行如上面实施例1所述的CT图像阴影校正方法。
本发明实施例还提供一种存储有计算机可读程序的存储介质，其中所述计算机可读程序使得计算机在电子设备中执行上面实施例1所述的CT图像阴影校正。
以上参照附图描述了本发明的优选实施方式。这些实施方式的许多特征和优点根据该详细的说明书是清楚的，因此所附权利要求旨在覆盖这些实施方式的落入其真实精神和范围内的所有这些特征和优点。此外，由于本领域的技术人员容易想到很多修改和改变，因此不是要将本发明的实施方式限于所例示和描述的精确结构和操作，而是可以涵盖落入其范围内的所有合适修改和等同物。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
本发明中应用了具体实施例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本发明的限制。

Claims (16)

  1. 一种CT图像阴影校正方法，其特征在于，包括：
    对原始CT重建图像进行图像纹理去除操作，得到平滑图像；
    根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像；
    根据所述平滑图像及模版图像进行阴影校正。
  2. 根据权利要求1所述的CT图像阴影校正方法，其特征在于，对原始CT重建图像进行图像纹理去除操作，得到平滑图像，包括：
    利用L0范数平滑算法对所述原始CT重建图像进行边缘保护同时及图像纹理去除，得到所述平滑图像。
  3. 根据权利要求1所述的CT图像阴影校正方法，其特征在于，根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像，包括：
    采用图像分割方法，将所述原始CT重建图像分割出多个人体组织区域；
    分别在不同的人体组织区域填充相应组织对应X射线球管电压下的CT值，得到所述模版图像。
  4. 根据权利要求1所述的CT图像阴影校正方法，其特征在于，根据所述平滑图像及模版图像进行阴影校正，包括：
    将所述平滑图像与模版图像做差，得到残差图像，所述残差图像中包括图像阴影及组织结构误差；
    对所述残差图像的组织结构误差进行低通滤波处理，得到CT图像阴影分布；
    利用所述CT图像阴影分布对所述原始CT重建图像进行补偿处理，得到修正后的CT图像。
  5. 根据权利要求4所述的CT图像阴影校正方法，其特征在于，对所述残差图像的组织结构误差进行低通滤波处理，包括：
    利用Savitzky-Golay局部低通滤波器对所述残差图像的组织结构误差进行低通滤波处理。
  6. 根据权利要求2所述的CT图像阴影校正方法，其特征在于，利用L0范数平滑算法对所述原始CT重建图像进行图像纹理去除，包括：
    通过目标函数计算无纹理平滑图像，所述目标函数如下：
    Figure PCTCN2017070410-appb-100001
    其中，
    Figure PCTCN2017070410-appb-100002
    Sp为无纹理平滑图像S的第p个像素索引，Ip为原始CT重建图像I的第p个像素索引，C(S)为
    Figure PCTCN2017070410-appb-100003
    像素索引p的个数，λ为平滑因子，
    Figure PCTCN2017070410-appb-100004
    及
    Figure PCTCN2017070410-appb-100005
    为像素索引在x及y两个方向的偏导数。
  7. 一种CT图像阴影校正装置，其特征在于，包括：
    平滑图像生成单元，用于对原始CT重建图像进行图像纹理去除操作，得到平滑图像；
    模版图像构建单元，用于根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像；
    校正单元，用于根据所述平滑图像及模版图像进行阴影校正。
  8. 根据权利要求7所述的CT图像阴影校正装置，其特征在于，所述平滑图像生成单元具体用于：
    利用L0范数平滑算法对所述原始CT重建图像进行边缘保护同时及图像纹理去除，得到所述平滑图像。
  9. 根据权利要求7所述的CT图像阴影校正装置，其特征在于，所述模版图像构建单元包括：
    分割模块，用于采用图像分割方法，将所述原始CT重建图像分割出多个人体组织区域；
    模版构建模块，用于分别在不同的人体组织区域填充相应组织对应X射线球管电压下的CT值，得到所述模版图像。
  10. 根据权利要求7所述的CT图像阴影校正装置，其特征在于，所述校正单元包括：
    残差图像构建模块，用于将所述平滑图像与模版图像做差，得到残差图像，所述残差图像中包括图像阴影及组织结构误差；
    低通滤波模块，用于对所述残差图像的组织结构误差进行低通滤波处理，得到CT图像阴影分布；
    补偿处理模块，用于利用所述CT图像阴影分布对所述原始CT重建图像进行补偿处理，得到修正后的CT图像。
  11. 根据权利要求10所述的CT图像阴影校正装置，其特征在于，所述低通滤波器模块具体用于：
    利用Savitzky-Golay局部低通滤波器对所述残差图像的组织结构误差进行低通滤波处理。
  12. 一种电子设备，该电子设备包括：
    处理器；和
    包括计算机可读指令的存储器，所述计算机可读指令在被执行时使所述处理器执行以下操作：
    对原始CT重建图像进行图像纹理去除操作，得到平滑图像；
    根据人体组织对所述原始CT重建图像的结构成分进行分割处理，构建模版图像；
    根据所述平滑图像及模版图像进行阴影校正。
  13. 根据权利要求12所述的电子设备，其特征在于，所述指令使所述处理器利用L0范数平滑算法对所述原始CT重建图像进行边缘保护同时及图像纹理去除，得到所述平滑图像。
  14. 根据权利要求12所述的电子设备，其特征在于，所述指令使所述处理器执行以下操作：
    采用图像分割方法，将所述原始CT重建图像分割出多个人体组织区域；
    分别在不同的人体组织区域填充相应组织对应X射线球管电压下的CT值，得到所述模版图像。
  15. 根据权利要求12所述的电子设备，其特征在于，所述指令使所述处理器执行以下操作：
    将所述平滑图像与模版图像做差，得到残差图像，所述残差图像中包括图像阴影及组织结构误差；
    对所述残差图像的组织结构误差进行低通滤波处理，得到CT图像阴影分布；
    利用所述CT图像阴影分布对所述原始CT重建图像进行补偿处理，得到修正后的CT图像。
  16. 根据权利要求15所述的电子设备，其特征在于，所述指令使所述处理器利用Savitzky-Golay局部低通滤波器对所述残差图像的组织结构误差进行低通滤波处理。
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