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WO2012168985A1 - Procédé de traitement d'images et appareil associé - Google Patents

Procédé de traitement d'images et appareil associé Download PDF

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Publication number
WO2012168985A1
WO2012168985A1 PCT/JP2011/003319 JP2011003319W WO2012168985A1 WO 2012168985 A1 WO2012168985 A1 WO 2012168985A1 JP 2011003319 W JP2011003319 W JP 2011003319W WO 2012168985 A1 WO2012168985 A1 WO 2012168985A1
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Prior art keywords
image
pixel
noise
noise suppression
image processing
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Ceased
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PCT/JP2011/003319
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English (en)
Japanese (ja)
Inventor
新悟 馬場
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Shimadzu Corp
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Shimadzu Corp
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Priority to PCT/JP2011/003319 priority Critical patent/WO2012168985A1/fr
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Anticipated expiration legal-status Critical
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • 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
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20004—Adaptive image processing
    • G06T2207/20012—Locally adaptive

Definitions

  • the present invention relates to an image processing method and apparatus for suppressing noise components in an X-ray image, and more particularly to recognition of a structure in an X-ray image.
  • a plurality of band images are created from an input image (for example, a fluoroscopic image). Then, in each of the plurality of band images, pixel energy is calculated for each target pixel while setting each pixel as the target pixel. This pixel energy is an index value used to represent the size of the structure present in the input image (inverse of flatness).
  • processing for suppressing noise is performed for each band image using the index value to suppress noise in the input image (see, for example, Patent Document 1). For example, noise suppression processing is performed in the structure and its vicinity so that noise suppression is weak because noise is low, and noise suppression is increased in other regions.
  • the pixel energy described above is Ve
  • the gradient is n
  • the gradient n is a different direction
  • each direction is represented by 0, 1, 2, and 3
  • the pixel value of the band image is SB
  • each band image is m
  • the pixel energy Ve is specifically calculated by the following equation.
  • q ′ n, m GradientFilter n (SB m )
  • q n, m GaussFilter n (
  • Ve m [i] (q 0, m [i] + q 1, m [i] + q 2, m [i] + q 3, m [i]) / 4
  • a gradient filter in four directions of 0 °, 45 °, 90 °, and 135 ° is applied to the band image, and each component of the gradient in the four directions at the target pixel is calculated as q ′ n, m .
  • a Gaussian filter is applied to the absolute value of each component in the four directions for each pixel of interest to calculate the magnitude of each direction component as q n, m .
  • the average value of the size of each direction component in each pixel of interest is obtained, and this is obtained as the pixel energy Ve for each pixel.
  • X-ray fluoroscopic images under low dose conditions and X-ray radiographic images with low dose levels have higher signal strength than X-ray fluoroscopic images under high dose conditions and X-ray radiographic images with high dose levels. Is weak and the noise intensity is high. Therefore, it is difficult to distinguish between a signal indicating a structure in an X-ray image and a signal indicating noise. For this reason, in the above-described conventional example, it is possible to reduce erroneous detection of a noise component as a signal by taking an absolute value average with respect to gradients in four directions of pixels.
  • the wire mentioned above is a guide wire inserted in advance of the catheter in, for example, an operation method using the catheter. Therefore, the wire may appear to be broken or connected in the X-ray fluoroscopic image, which may hinder the catheter operation.
  • FIG. 7A and 7B are X-ray images after noise suppression processing according to a conventional example.
  • FIG. 7A is an overall view and FIG. 7B is a partially enlarged view.
  • FIG. 7 (a) is an X-ray image obtained by laminating several acrylic plates, shifting the positions so as to form a staircase, and imaging the wire on the uppermost surface. As shown in the enlarged portion of FIG. 7B, a part of the wire that should be continuous appears broken.
  • the present invention has been made in view of such circumstances, and an object of the present invention is to provide an image processing method and apparatus capable of enhancing structure recognition by devising an algorithm.
  • the present invention has the following configuration. That is, the image processing method according to the present invention is an image processing method for suppressing noise in an X-ray image, a band image generation process for generating band images of different frequency bands from input images, and each pixel of interest for each band image. A pixel energy calculation process for calculating the maximum value of the magnitudes of gradient components in different directions in each pixel as the pixel energy of the target pixel, and noise suppression for suppressing noise on the input image based on the pixel energy And a process.
  • the band image generation process band images of different frequency bands are obtained from the input image, and in the pixel energy calculation process, the magnitudes of gradient components in different directions in each pixel of interest are different. Is calculated as the pixel energy of the target pixel.
  • the absolute value average of the magnitudes of the gradient components in a plurality of directions is not calculated, but the maximum value is calculated as the pixel energy. Therefore, the pixel energy is calculated larger than in the conventional example. That is, a signal corresponding to a structural portion in the input image can be strongly recognized. Therefore, in the noise suppression process, noise suppression is performed on the input image based on the pixel energy. Therefore, it is possible to increase the noise suppression other than the part recognized as the structure, or weaken the noise suppression of the part recognized as the structure. Control of noise processing in an image can be easily performed. As a result, the quality of the input image can be improved.
  • the pixel energy calculation process sets four directions of the gradient component as four directions. By limiting the plurality of gradient component directions to four directions, the calculation load can be reduced.
  • the noise suppression process obtains a dose based on the input image and a signal value-dose conversion corresponding to a preset reference dose value, and based on the dose and the pixel energy. It is preferable to use the calculated noise suppression degree. Since the pixel energy is calculated to be large in the structure part, it is easy to distinguish between the structure part and the other parts, and the noise suppression degree can be calculated appropriately. Therefore, noise processing can be appropriately performed by using the noise suppression degree.
  • the present invention relates to an image processing apparatus for suppressing noise of an X-ray image, an image processing apparatus for suppressing noise of an X-ray image, and band image generation means for generating band images of different frequency bands from an input image; Pixel energy calculating means for calculating the maximum value of the magnitude values of gradient components in different directions in each pixel of interest for each image as the pixel energy of the pixel of interest, and an input image based on the pixel energy Noise suppression means for suppressing noise with respect to.
  • the band image generation means obtains band images of different frequency bands from the input image, and the pixel energy calculation means determines the magnitudes of gradient components in different directions in each pixel of interest. Is calculated as the pixel energy of the target pixel.
  • the absolute value average of the magnitudes of the gradient components in a plurality of directions is not calculated, but the maximum value is calculated as the pixel energy. Therefore, the pixel energy is calculated larger than in the conventional example. That is, a signal corresponding to a structural portion in the input image is strongly recognized.
  • the noise suppression means performs noise suppression on the input image based on the pixel energy, the noise suppression other than the portion recognized as the structure is strengthened, or the noise suppression of the portion recognized as the structure is weakened. Control of noise processing in an image can be easily performed. As a result, the quality of the input image can be improved.
  • the pixel energy calculation means sets a plurality of directions of the gradient component as four directions. By limiting the plurality of gradient component directions to four directions, the calculation load on the pixel energy calculation means can be reduced.
  • the noise suppression means obtains a dose based on the input image and a signal value-dose conversion corresponding to a preset reference dose value, and based on the dose and the pixel energy. It is preferable to use the calculated noise suppression degree. Since the pixel energy is calculated to be large in the structure part, it is easy to distinguish between the structure part and the other parts, and the noise suppression degree can be calculated appropriately. Therefore, noise processing by the noise suppression unit can be appropriately performed by using the noise suppression degree.
  • the image processing method of the present invention in the band image generation process, band images of different frequency bands are obtained, and in the pixel energy calculation process, the absolute values of the magnitudes of gradient components in different directions in each pixel of interest Among these, the maximum is calculated as the pixel energy of the target pixel.
  • the absolute value average of the magnitudes of the gradient components in a plurality of directions is not calculated, but the maximum value is calculated as the pixel energy. Therefore, the pixel energy is calculated larger than in the conventional example. That is, a signal corresponding to a structural portion in the input image can be strongly recognized. Therefore, in the noise suppression process, noise suppression is performed on the input image based on the pixel energy. Therefore, it is possible to increase the noise suppression other than the part recognized as the structure, or weaken the noise suppression of the part recognized as the structure. Control of noise processing in an image can be easily performed. As a result, the quality of the input image can be improved.
  • FIG. 1 is a schematic diagram for explaining the flow of the image processing method body according to the embodiment
  • FIG. 2 is a schematic diagram illustrating an example of a gradient filter.
  • a solid line arrow in FIG. 1 indicates a flow of an image
  • a dotted line arrow indicates a flow of a feature amount.
  • the image processing method handles an X-ray image or an X-ray fluoroscopic image as an input image to be processed.
  • the input image is generated as a band image (also referred to as a band limited image) having a plurality of different frequency bands (step S1).
  • a band image also referred to as a band limited image
  • a generation method by Laplacian pyramid decomposition may be mentioned.
  • a generation method by multi-resolution conversion such as wavelet conversion can be adopted for generation of the band image.
  • a gradient filter is applied to each band image, and each pixel of each band image is used as a target pixel in order, and gradient components in different directions are obtained for each target pixel (step S2).
  • This gradient component is the difference between the pixel value of the pixel of interest and the pixel value of the pixel located in a certain direction from the pixel of interest, and represents the component consisting of the difference in pixel value and the direction.
  • the gradient component increases in the direction where the pixel value difference is large in the band image, and the gradient component decreases in the direction where the pixel value difference is small.
  • a gradient filter Gradient Filter
  • FIG. 2 illustrates an example of a gradient filter of 0 °, 45 °, 90 °, and 135 °, and illustrates a case of a 31 ⁇ 31 pixel spatial filter.
  • m is, for example, 1 to 6
  • SB the pixel value of each band image
  • the above GradientFilter is a kind of primary differential filter. This filter emphasizes more as the difference in pixel values is larger, and the value obtained by this filter represents the strength of the edge.
  • the pixel energy Ve is calculated for each pixel i by applying the magnitude q n, m of each direction component to the following equation (step S3).
  • Ve m [i] Max (q 0, m [i], q 1, m [i], q 2, m [i], q 3, m [i])
  • Max represents selecting the largest one of the magnitudes q n, m [i] of each direction component in parentheses.
  • the pixel energy Ve is calculated as the maximum value among the absolute values of the gradient components in four different directions at each pixel position (target pixel) of each band image.
  • This pixel energy Ve represents the size of the structure in the image (inverse of flatness), and the larger the value, the higher the probability that the pixel is a structure in the image.
  • a gradient vector indicating the direction of the edge is calculated using the magnitudes q n, m of each direction component in each pixel of interest (step S4).
  • the gradient vector is calculated because the pixel energy Ve is calculated by taking an absolute value, so that the magnitude can be determined but the direction cannot be determined.
  • the gradient vector here indicates a direction and its magnitude (q n, m [i]), and when the above-described four-direction gradient filter is used, 0 °, 45 °, The size in four directions of 90 ° and 135 ° is shown.
  • a dose is obtained from the pixel energy Ve, a predetermined reference dose value (step PP1), an input image, and a signal value-dose conversion (step PP2) corresponding to the reference dose value. Based on this, a noise suppression degree is calculated (step S5).
  • the reference dose value represents an arbitrary dose value set in advance within the range of doses that can be irradiated with X-rays. For example, the reference dose value is set to a different value depending on a procedure such as X-ray imaging or X-ray fluoroscopy, or an X-ray irradiation site such as a chest or abdomen. When the dose value at the time of obtaining an image is smaller than the reference dose value, the image becomes dark and noise increases.
  • the signal value-dose conversion is a table for each reference dose value showing the correspondence between the pixel value corresponding to the signal value and the dose that generates the signal value.
  • the noise suppression degree represents the degree of noise removal.
  • the edge reliability representing the probability of the edge is calculated for each pixel (step S6). For example, when the gradient vector q n, m [i] is large and the pixel energy Ve is large, the edge reliability indicates that the probability that the pixel represents a structure is high. Further, based on the direction of the gradient vector q n, m [i] whose magnitude is large, a smoothing direction representing the direction in which the pixel value is to be smoothed is calculated (step S7). This smoothing direction is calculated in order to prevent a pixel group having a high probability of constituting an edge from being smoothed.
  • an isotropic filter is applied to each band image (step S8). Since the isotropic filter smoothes the image and removes the noise component, it is necessary to change it according to the size of the noise component of the band image. Therefore, the isotropic filter is applied according to the relationship between the frequency of the band image and the reference dose value. Further, an anisotropic filter is applied to each band image (step S9). The anisotropic filter performs smoothing in the edge direction so that the edge of the band image is not smoothed. An anisotropic filter is also applied according to the relationship with the reference dose value in order to change according to the size of the noise component of the band image. By this processing, an image in which the edge of the band image is emphasized is generated. These results are added.
  • edge extraction is performed in consideration of edge reliability (step S10), and an edge smoothed image is generated for each band image smoothed except for the edges.
  • edge reliability an anisotropic filter is applied to the part that seems to be an edge, and an isotropic filter is applied to the other part, so that the remaining edges are smoothed.
  • weighting is performed according to the noise suppression degree or the noise suppression degree and edge reliability, and the corresponding edge smoothed image is subtracted (edge exclusion) from each band image (step S11).
  • the noise suppression degree indicates the degree of noise removal according to the pixel value
  • the edge reliability indicates the likelihood of the edge of the pixel. Therefore, the weight in each pixel is changed according to these and the noise in the noise image is changed. Controls the strength of ingredients. For example, a portion with a high degree of noise suppression and a portion with low edge reliability has a lot of noise components, and thus is smoothed in the edge smooth image. Therefore, if weighting is performed to increase the pixel value, the noise component in the noise image can be reduced when subtracted.
  • an edge portion (structure) is excluded from each band image, and a noise image without an edge is generated for each band image.
  • performing weighted subtraction affects the result of noise subtraction in step S14, which will be described later, and as a result also affects the degree of suppression of noise components in the processed image.
  • the above weighting can increase the control accuracy of the noise component in the noise image when both the noise suppression degree and the edge reliability are used. However, it is not necessary to use edge reliability using only the noise suppression degree. Thereby, the calculation load can be reduced.
  • Band synthesis is performed based on the noise image for each band image (step S12), and density dependent coefficient processing is performed on the noise image subjected to band synthesis to perform tone correction according to the density of the input image (step S13).
  • noise subtraction is performed to subtract the noise image that has been subjected to the density dependence coefficient processing from the input image (step S14). As a result, a processed image in which noise components are suppressed is obtained from the input image.
  • Step S1 corresponds to the “band image generation process” in the present invention
  • step S3 corresponds to the “pixel energy calculation process” in the present invention
  • steps S4 to S14 correspond to the “noise suppression process” in the present invention.
  • FIG. 4 shows a graph in which the pixel in the arrow direction in the circle in FIG. 3 is plotted on the horizontal axis and the pixel energy Ve is plotted on the vertical axis.
  • the example shows that the edge of the wire is clearer than the conventional example. Further, it can be seen from the graph that the pixel energy Ve is calculated to be larger in the embodiment than in the conventional example, and the structure recognition can be enhanced from this point.
  • FIG. 5 is an image after the noise suppression processing
  • (a) is an image according to the embodiment
  • (b) is an image according to a conventional example.
  • an anisotropic filter is applied that smoothes only in the longitudinal direction while leaving an edge along the longitudinal direction of the wire, which can be determined by the pixel energy Ve.
  • the wire edge is clear, and it can be seen that the breakage of the wire is suppressed as compared with the conventional example.
  • band images having different frequency bands are obtained, and the maximum value among the absolute values of the gradient components in different directions in each pixel of interest is calculated as the pixel energy Ve of the pixel of interest.
  • the absolute value average of the magnitudes of the gradient components in a plurality of directions is not obtained as in the conventional example, but the maximum value is calculated as the pixel energy Ve. Therefore, the pixel energy Ve is calculated larger than in the conventional example. That is, a signal corresponding to a structural portion in the input image can be strongly recognized. Accordingly, since noise suppression is performed on the input image based on the pixel energy Ve, noise processing in the input image can be performed by increasing noise suppression other than the portion recognized as the structure or weakening noise suppression in the portion recognized as the structure. Can be easily controlled. As a result, the quality of the input image can be improved.
  • the pixel energy Ve may be calculated smaller than in the present invention. Therefore, the noise suppression degree in step S5 in FIG. 1 is calculated to be small, and the edge reliability in step S6 is calculated to be low. Then, in step S10, switching between the isotropic filter and the anisotropic filter may be inappropriate, and as a result, a portion where the edge becomes sweet in the edge smooth image is generated. Moreover, the case where the weighting subtraction in step S11 becomes inappropriate may occur. Accordingly, the noise component of the noise image may include a part of the structure portion. As a result, the structure portion may be subtracted during the noise subtraction step S14, and the structure portion may be missing in the processed image.
  • FIG. 6 is a block diagram illustrating a schematic configuration of the image processing apparatus according to the embodiment.
  • This image processing apparatus is provided in, for example, an X-ray fluoroscopic apparatus or an X-ray imaging apparatus.
  • the image processing apparatus includes a band image generation unit 1, a pixel energy calculation unit 3, and a noise suppression unit 5.
  • the band image generation unit 1 corresponds to the “band image generation unit” in the present invention
  • the pixel energy calculation unit 3 corresponds to the “pixel energy calculation unit” in the present invention
  • the noise suppression unit 5 corresponds to the “noise” in the present invention. It corresponds to “suppressing means”.
  • the band image generation unit 1 takes in an input image and performs the above-described band division (step S1).
  • the pixel energy calculation unit 3 calculates the pixel energy Ve based on the above-described formula for each band image.
  • the noise suppression unit 5 performs the processing from steps S4 to S14 including steps PP1 and PP2 described above, and outputs a noise suppression image.
  • the output noise-suppressed image is output to a display device (not shown) or stored in a storage device, for example.
  • the above-described image processing method can be suitably implemented.
  • the present invention is not limited to the above embodiment, and can be modified as follows.
  • the gradient components are obtained in four directions, but gradient components in eight directions or more may be obtained in accordance with the arithmetic processing capability. Thereby, the direction of a structure can be calculated
  • the present invention is suitable for an image processing method and apparatus for suppressing noise components in an X-ray image.

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Abstract

Selon l'invention, des images à des bandes de fréquences respectives différentes sont déterminées. Parmi les valeurs absolues des amplitudes des composantes de gradient dans des directions différentes multiples pour chaque pixel d'intérêt, la valeur la plus élevée est calculée comme l'énergie du pixel d'intérêt. À la différence des antériorités dans lesquelles la moyenne des valeurs absolues des amplitudes des composantes de gradient dans des directions multiples était déterminée, la valeur maximale est calculée comme énergie de pixel. Ainsi, les énergies de pixel sont calculées pour être supérieures à celles des antériorités. Autrement dit, il est possible de percevoir des signaux forts qui correspondent à des composantes structurelles de l'image d'entrée. Comme la réduction du bruit dans l'image d'entrée est réalisée en fonction de l'énergie de pixels, un traitement de bruit dans l'image d'entrée peut aisément être régulé par renforcement de la réduction de bruit dans des zones autres que les zones qui sont perçues comme des structures, ou par affaiblissement de la réduction de bruit dans des zones qui sont perçues comme des structures. La présente invention permet ainsi d'améliorer la qualité de l'image d'entrée.
PCT/JP2011/003319 2011-06-10 2011-06-10 Procédé de traitement d'images et appareil associé Ceased WO2012168985A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016527935A (ja) * 2013-06-28 2016-09-15 タレス 蛍光透視画像シーケンスにおけるノイズを減らす方法
WO2023024660A1 (fr) * 2021-08-23 2023-03-02 深圳前海微众银行股份有限公司 Procédé et appareil d'optimisation d'image
CN116894794A (zh) * 2023-09-11 2023-10-17 长沙超创电子科技有限公司 一种视频的快速去噪方法

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Publication number Priority date Publication date Assignee Title
JP2001057677A (ja) * 1999-06-10 2001-02-27 Fuji Photo Film Co Ltd 画像処理方法および装置並びに記録媒体
JP2003534754A (ja) * 2000-05-23 2003-11-18 コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ ディジタル画像中の糸状構造を全体的に抽出する画像処理方法、システム及び検査装置
JP2004242285A (ja) * 2003-01-14 2004-08-26 Fuji Photo Film Co Ltd ノイズ抑制処理方法および装置並びにプログラム
JP2005021456A (ja) * 2003-07-03 2005-01-27 Fuji Photo Film Co Ltd 放射線画像用画像処理装置、方法およびプログラム

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001057677A (ja) * 1999-06-10 2001-02-27 Fuji Photo Film Co Ltd 画像処理方法および装置並びに記録媒体
JP2003534754A (ja) * 2000-05-23 2003-11-18 コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ ディジタル画像中の糸状構造を全体的に抽出する画像処理方法、システム及び検査装置
JP2004242285A (ja) * 2003-01-14 2004-08-26 Fuji Photo Film Co Ltd ノイズ抑制処理方法および装置並びにプログラム
JP2005021456A (ja) * 2003-07-03 2005-01-27 Fuji Photo Film Co Ltd 放射線画像用画像処理装置、方法およびプログラム

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016527935A (ja) * 2013-06-28 2016-09-15 タレス 蛍光透視画像シーケンスにおけるノイズを減らす方法
WO2023024660A1 (fr) * 2021-08-23 2023-03-02 深圳前海微众银行股份有限公司 Procédé et appareil d'optimisation d'image
CN116894794A (zh) * 2023-09-11 2023-10-17 长沙超创电子科技有限公司 一种视频的快速去噪方法
CN116894794B (zh) * 2023-09-11 2023-11-21 长沙超创电子科技有限公司 一种视频的快速去噪方法

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