WO2009110260A1 - Dispositif de traitement d'image - Google Patents

Dispositif de traitement d'image Download PDF

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Publication number
WO2009110260A1
WO2009110260A1 PCT/JP2009/051069 JP2009051069W WO2009110260A1 WO 2009110260 A1 WO2009110260 A1 WO 2009110260A1 JP 2009051069 W JP2009051069 W JP 2009051069W WO 2009110260 A1 WO2009110260 A1 WO 2009110260A1
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WIPO (PCT)
Prior art keywords
image
trabecular bone
trabecular
image processing
emphasized
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PCT/JP2009/051069
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English (en)
Japanese (ja)
Inventor
美紗恵 田▲崎▼
司 伊藤
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コニカミノルタエムジー株式会社
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Priority to JP2010501818A priority Critical patent/JP5353876B2/ja
Publication of WO2009110260A1 publication Critical patent/WO2009110260A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/505—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of bone
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G06T7/41—Analysis of texture based on statistical description of texture
    • G06T7/42—Analysis of texture based on statistical description of texture using transform domain methods
    • 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
    • G06T2207/10116—X-ray image
    • 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
    • G06T2207/20048—Transform domain processing
    • G06T2207/20064—Wavelet transform [DWT]
    • 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
    • G06T2207/30008—Bone

Definitions

  • the present invention relates to an image processing apparatus.
  • a wavelet called a Gabor wavelet is used in the method of Patent Document 1, but the Gabor wavelet has a high processing load. Further, trabecular bone corresponds to the medium frequency component, but Gabor wavelet has low resolution of the medium frequency component, and accurate information on the trabecular bone cannot be obtained.
  • the energy subtraction requires imaging twice, and low energy X-ray irradiation is performed, resulting in an increase in imaging time and exposure dose.
  • An object of the present invention is to obtain a trabecular feature amount accurately and easily.
  • An image processing apparatus including an image processing unit that calculates a trabecular feature amount using an emphasized image is provided.
  • the image processing unit reconstructs an image using a frequency component at a level other than a high frequency component at a lowest level and / or a low frequency component at a highest level from each frequency component subjected to frequency decomposition.
  • An image processing apparatus according to item 1 is provided.
  • the level corresponding to the trabecular bone is a level in which the frequency intensity peak of the wavelet waveform is in a range where the wavelength when converted into the actual size of the subject is 100 ⁇ m or more and 500 ⁇ m or less.
  • the described image processing apparatus is provided.
  • the trabecular feature amount is the density of the trabecular bone
  • the image processing unit creates a weighted image in which a frequency component at a level corresponding to a trabecular bone is weighted in the main scanning direction and the sub scanning direction of the image, determines an image portion corresponding to the trabecular bone in the emphasized image
  • the image processing device according to any one of claims 1 to 3, wherein the density of the trabecular bone is calculated from the area ratio between the image determined to be the trabecular bone and the entire emphasized image. .
  • the feature amount of the trabecular bone is the number of trabecular bones
  • the image processing unit creates an emphasized image obtained by weighting only a frequency component of a level corresponding to a trabecular bone only in the main scanning direction of the image and an emphasized image obtained by weighting only the sub-scanning direction of the image.
  • the image processing apparatus according to any one of claims 1 to 3, wherein an image portion corresponding to the above is determined, and the number of trabeculae is calculated based on the number of pixels of the image determined to be a trabecular bone Is provided.
  • the image processing unit further determines an image portion corresponding to the trabecular bone in the enhanced image obtained by weighting the frequency component of the level corresponding to the trabecular bone in the main scanning direction and the sub scanning direction of the image, and is determined to be the trabecular bone Only the image portion where the image portion and the image portion determined to be trabecular in the weighted image weighted only in the main scanning direction or only the sub-scanning direction of the image are subject to calculation of the number of trabeculae.
  • An image processing apparatus according to item 5 is provided.
  • the image processing unit performs anisotropic thinning on each of the emphasized image weighted only in the main scanning direction and the emphasized image weighted only in the sub-scanning direction, and the trabecular bone in the emphasized image subjected to the thinning processing.
  • the image processing unit Prior to the anisotropic thinning process, the image processing unit performs anisotropic expansion / contraction processing on each of the emphasized image weighted only in the main scanning direction and the emphasized image weighted only in the sub-scanning direction.
  • An image processing apparatus according to claim 7 is provided.
  • the image processing unit extracts an image portion for calculating a trabecular feature amount from the created enhanced image, and calculates the trabecular feature amount using the extracted image.
  • An image processing apparatus according to any one of the items is provided.
  • the trabecular bone having a fine structure can be accurately grasped, and the density thereof can be calculated with high accuracy.
  • the trabecular line structure extending in the main scanning direction and the trabecular line structure extending in the sub-scanning direction it is possible to accurately grasp the trabecular line structure extending in the main scanning direction and the trabecular line structure extending in the sub-scanning direction.
  • the number can be calculated for each direction in which the line structure extends. Therefore, the number can be obtained with higher accuracy.
  • the trabecular line image extending in the main scanning direction or the sub-scanning direction is reduced in the extending direction. Can be prevented. Thereby, the disappearance of the trabecular line image can be prevented and the number thereof can be accurately calculated.
  • the image portions that should originally constitute one line structure are connected, and It is a line structure and can be processed so as not to connect image portions that should not be connected. Therefore, the number of trabeculae can be accurately calculated.
  • FIG. 1 shows a functional configuration of the image processing apparatus 10 in the present embodiment.
  • the image processing apparatus 10 includes a control unit 11, an operation unit 12, a display unit 13, a communication unit 14, a storage unit 15, and an image processing unit 16 as shown in FIG.
  • the control unit 11 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), and the like.
  • the control unit 11 performs various calculations and cooperates with a control program stored in the storage unit 15 and performs operations of each unit. Centralized control.
  • the operation unit 12 includes a keyboard and the like, generates operation signals corresponding to these operations, and outputs them to the control unit 11.
  • the display unit 13 includes a display, and displays various operation screens and medical images according to display control of the control unit 11.
  • the communication unit 14 includes a communication interface, and communicates with an external apparatus such as a server that stores and distributes medical images and an imaging apparatus that captures medical images via a network.
  • an external apparatus such as a server that stores and distributes medical images and an imaging apparatus that captures medical images via a network.
  • the communication unit 14 receives a medical image for image processing from a server or an imaging device, or transmits a medical image that has been subjected to image processing to the server.
  • the storage unit 15 stores a control program, an image processing program, parameters necessary for executing the program, and the like.
  • the storage unit 15 stores medical images to be image processed. A hard disk or the like can be applied as the storage unit 15.
  • the image processing unit 16 performs necessary image processing on the medical image. Examples of image processing include sharpening processing and gradation conversion processing. Since the type and conditions of the required image processing differ depending on the imaging region, the image processing unit 16 determines the necessary image processing type and image processing condition information for each imaging region, and performs image processing according to the information.
  • the image processing unit 16 performs image processing on a medical image obtained by photographing a bone part, and calculates a feature amount of the trabecular bone. The calculation result is output to the control unit 11.
  • the image processing unit 16 when a medical image to be image-processed is received from a server or the like via the communication unit 14, the image processing unit 16 performs necessary image processing according to the imaging part. For the medical image of the bone part, the image processing unit 16 executes a process of calculating the trabecular feature amount.
  • the trabecular feature amount is information that is an index of the progress and degree of disease such as osteoporosis, and is information on the density of the trabecula and the number of trabeculae.
  • the trabecular bone forms a network structure in which several line images intersect on the image.
  • the number of trabecular bones refers to the number of line images.
  • the medical image of the bone part has a high resolution in order to calculate an accurate feature amount.
  • the size of one pixel when converted to the actual size of the subject is preferably 50 ⁇ m or less. More preferably, it is 30 ⁇ m or less.
  • Such a medical image may be an image captured and generated by an imaging device capable of realizing a high resolution, or may be an image obtained by reading a medical image recorded on a film.
  • phase contrast imaging refers to an X-ray source and a subject, a subject and an X-ray in enlarged imaging, as described in JP-A-2001-91479, JP-A-2001-311701, JP-A-2003-180607, and the like. This is an imaging method in which the distance between the detection units, the X-ray irradiation conditions, and the like are predetermined, and has the effect of enhancing the edges of the structure included in the image. By enlarging a fine trabecular portion by magnified imaging or phase contrast imaging, the feature amount can be calculated with high accuracy.
  • FIG. 2 is a diagram showing an example of an imaging device for imaging a bone part of a hand.
  • the photographing apparatus 20 includes the photographing unit 3 and a main body unit 4 that performs photographing control and the like.
  • the imaging unit 3 is configured such that an arm 31 attached to a support column 32 can be moved up and down along the support column 32.
  • the arm 31 is provided with an X-ray source 33, a subject table 34, and an X-ray detector 35.
  • the X-ray detector 35 is a cassette or the like that accommodates an FPD (Flat Panel Detector) or a phosphor plate.
  • the subject table 34 and the X-ray detection unit 35 are configured to be movable up and down along the support shaft of the arm 31.
  • the arm 31, the subject table 34, or the X-ray detection unit 35 is moved up and down to adjust its height position.
  • the arrows shown in FIG. 2 indicate the movement directions of the arm 31, the subject table 34, and the X-ray detection unit 35.
  • the main unit 4 is a computer device that includes a display, operation buttons, a communication unit, and the like, and is used for shooting operations.
  • the main body unit 4 performs imaging control by the imaging unit 3, reads a medical image from the X-ray detection unit 35, and transfers the read medical image to a server.
  • Magnified photographing is performed by providing a distance between the subject H and the X-ray detector 35 as shown in FIG.
  • the X-rays emitted from the X-ray source 33 in the shape of a cone beam as shown in FIG.
  • the obtained medical image (b) is an image having an enlarged size compared to the close-contact photographed image (a) close to the actual size of the subject H.
  • the enlargement ratio M can be adjusted by changing the ratio of the distances R1 and R2.
  • the X-ray focal diameter ( ⁇ m), the tube voltage applied to the X-ray source 33, and the like are set in a predetermined range, and the distances R1, R2, and R3 are set as predetermined distances.
  • the focal range is 30 ⁇ m or more and 350 ⁇ m or less at the nominal focal diameter at the time of IEC compliance evaluation.
  • the focal range is particularly preferably from 50 cm to 280 cm. Further, the reading pitch at this time is 43.75 ( ⁇ m).
  • An edge enhancement effect can be obtained in medical images taken under these conditions.
  • the X-rays refracted by passing through the edge of the subject H overlap with the X-rays passed without passing through the subject H, and the X-ray intensity of the overlapped portion increases.
  • a phenomenon occurs in which the X-ray intensity is weakened in a portion inside the edge of the subject H.
  • the edge of the structure of the subject H is emphasized. This is the edge enhancement effect.
  • the imaging apparatus differs in mechanical structure and the like depending on which part the bone part to be imaged belongs to, but the basic imaging method is the same as described above.
  • the image processing unit 16 extracts an image part (this is called ROI: Region Of Interest) from the medical image of the bone part (step S1). This is because a portion unrelated to the trabecular bone is excluded from the processing target and the feature amount is calculated with high accuracy.
  • ROI Region Of Interest
  • the size of the ROI may be determined as appropriate. For example, as shown in FIG. 8, the extracted image G2 having a size of 200 pixels ⁇ 200 pixels extracted from the medical image G1 of the bone is used. Since only the image portion of the trabecular bone is extracted as much as possible, it is necessary to determine the position of the ROI while avoiding the tissue called the growth line F that appears inside the bone and the edge E of the bone. This is because the growth line F or the edge portion E having a signal value smaller than that of the trabecular bone can be noise. When extracting, a position that does not include the growth line F or the edge portion E in advance is empirically determined in advance, and a region centered on that position may be extracted. For example, when the bone portion is a rib, as shown in FIG. 8, the edge E of the distal end portion of the rib is detected by an edge detection filter, template matching, or the like, and the center 200 is located about 400 pixels away from the distal end portion. An area of pixels ⁇ 200 pixels is extracted as an ROI image portion.
  • the image processing unit 16 performs frequency decomposition on the extracted image using a binomial wavelet (Dyadic Wavelet) to create an emphasized image in which the trabecular bone is emphasized (step S2).
  • the enhancement image to be created is an enhancement image in which only the frequency component in the x direction of the trabecular bone is emphasized, and only the frequency component in the y direction when the main scanning direction of the medical image is represented in the x direction and the sub scanning direction in the y direction.
  • the enhanced image and the enhanced image in which both frequency components in the x direction and the y direction are enhanced.
  • the enhanced image in which both frequency components in the x direction and the y direction are enhanced is used for processing for calculating the trabecular density feature amount.
  • the enhanced image in which only the frequency component in the x direction is emphasized and the enhanced image in which only the frequency component in the y direction is enhanced are used for processing for calculating the feature quantity of the number of trabeculae.
  • the types of emphasized images used for processing differ from each other because the density of the trabecular bone shows the proportion of the trabecular bone occupying in the bone portion. Therefore, it is necessary to grasp the entire trabecular bone regardless of the x and y directions.
  • the trabecular bone has a mesh structure in which line structures (linear structures) intersect, and the number of trabecular bones needs to grasp the linear images extending in the x direction and the y direction, respectively. It depends.
  • the wavelet function of the binomial wavelet is expressed by the following equation 2.
  • i represents the scale of the wavelet function, and the larger the value of i, the smaller the frequency of the wavelet function ⁇ i, j (x). That is, the value of i indicates a level at which frequency decomposition is performed.
  • j indicates the position of the wavelet function. The position where the wavelet function ⁇ i, j (x) oscillates moves according to the value of j .
  • the characteristic of the binomial wavelet is that the minimum movement unit of the position of the wavelet function is a constant value j regardless of the level i.
  • orthogonal wavelets and biorthogonal wavelets as shown in the following Equation 3 are widely used.
  • trabecular bone is very fine, and high resolution of the image is required for quantification of trabecular bone. Further, since the trabecular bone has a spongy structure and the structure itself is very thin, the trabecular bone depicted in the image is not clear in comparison with other tissues. That is, the contour of the trabecular bone cannot be captured in the high frequency range.
  • FIG. 9 is a graph showing the frequency characteristics of the binomial wavelet, and shows the waveforms from level 1 to level 5 of the wavelet function group.
  • the horizontal axis represents the wavelength (unit: ⁇ m, expressed as a wavelength obtained by converting the size of one pixel into the actual size of the subject), and the vertical axis represents the wave intensity.
  • the thickness of the trabecular bone is said to be 100 ⁇ m or more and 200 ⁇ m or less, and the distance between the trabecular and trabecular is about the same.
  • the repetitive period of the trabecular structure is estimated to be about 200 ⁇ m or more and 400 ⁇ m or less. Therefore, it can be said that a wavelet function having a wavelength component comparable to the range from the lower limit 100 to the upper limit 400 ( ⁇ m) is suitable for detecting a trabecular bone.
  • the level corresponding to the trabecular bone is a waveform level having a peak value in a range of 100 ⁇ m or more and 400 ⁇ m or less, but as shown in FIG. 9, a high level having a peak value in the vicinity of 500 ( ⁇ m).
  • the upper limit is expanded in this embodiment, and a level having a waveform having a peak value in the range of wavelength 100 to 500 ⁇ m is set as a level corresponding to the trabecular bone. deal with.
  • the level of the waveform preferably has a peak value in a range of 200 ⁇ m and 300 ⁇ m or less. From the graph shown in FIG.
  • the minimum movement unit of the wavelet function is 2 i and is defined discretely. As level i increases, the number of moving units increases at an accelerated rate, so that the resolution is very low in the middle frequency range such as level 3 or level 4.
  • the minimum moving unit j of the wavelet function can be arbitrarily specified. Therefore, by specifying j to a small value, regardless of the level i, that is, frequency resolution with high resolution at any level. Is possible. Therefore, in order to emphasize a trabecular bone having a fine structure whose outline is not clear, a binomial wavelet capable of realizing a high resolution even in the middle frequency range is particularly effective.
  • Level n wavelet transform can be obtained by filter processing as shown in FIG.
  • HPF and HPF ′ are high-pass filters
  • LPF and LPF ′ are low-pass filters.
  • x attached to the HPF and LPF indicates processing in the x direction
  • y indicates processing in the y direction.
  • filter coefficients used here are those described in the document ⁇ Stephan Mallat and Sifen Characterization of Signals from Multiscale Edges, IEEE Transaction on Pattern Analysis and Machine Intelligence, 14, (7), 710-732, 1992 '' Is mentioned.
  • the original image S0 is decomposed into a low frequency component S1 and high frequency components Wx1 and Wy1.
  • the low frequency component S1 is further decomposed into a low frequency component S2 and high frequency components Wx2 and Wy2 by a level 2 wavelet transform.
  • the low frequency component Sn and the high frequency components Wx1 to Wxn and Wy1 to Wyn are decomposed.
  • the processing from level 3 to level (n-2) is omitted.
  • Each of the decomposed frequency components Sn, Wx1 to Wxn, and Wy1 to Wyn can completely reconstruct the original image S0 by inverse wavelet transform. That is, when each frequency component to be reconfigured is represented by Sn ′, Wxn ′, Wyn ′ as shown in FIG. , LPFnx and LPFny to perform inverse transform, and synthesize the obtained components to obtain Sn-1 ′. The same processing is repeated at each level, and finally the original image S0 is reconstructed.
  • the image processing unit 16 weights the frequency components Sn, Wx1 to Wxn, and Wy1 to Wyn of each level in order to emphasize the frequency components corresponding to the trabecular bone before performing the inverse transformation.
  • the trabecular bone corresponds to a medium frequency component such as levels 3, 4 and the like, and it is considered that the low frequency component at the highest level and the high frequency component at the lowest level are not at least components of the trabecular bone. Therefore, the weighting is performed so that the image is reconstructed only with the frequency components of other levels except the highest level low frequency component and / or the lowest level high frequency component.
  • the weighting condition is determined according to the feature value to be calculated.
  • one or more weights are assigned to level 3 and level 4 frequency components Wx3, Wx4, Wy3, and Wy4 among level 1 to level 4 frequency components.
  • the other frequency components Wx1, Wx2, Wy1, and Wy2 are multiplied by a weighting coefficient of 0 to obtain Wx1 ′ to Wx4 ′ and Wy1 ′ to Wy4 ′ for each level from level 1 to level 4.
  • S4 is adjusted to a constant value (for example, an intermediate gray value) Sn ′.
  • the enhanced image S0 ′ in which the frequency components at levels 3 and 4 are enhanced in both the x direction and the y direction. Is reproduced.
  • the low-frequency component Sn at the highest level 4 may be subjected to image reconstruction by excluding Sn by multiplying by a weighting coefficient of 0.
  • the frequency components Wx3 and Wx4 in the x direction at levels 3 and 4 are multiplied by a weighting factor of 1 or more, and the other frequency components Wx1, Wx2, Wy1, Wy2, Wy3, and Wy4 are weighted with 0.
  • S4 is adjusted to a constant value (for example, a value that becomes intermediate gray) Sn ′.
  • the enhanced image S0 ′ in which the frequency components of level 3 and 4 are enhanced only in the x direction is reproduced. Is done.
  • the frequency components Wy3 and Wy4 in the y direction at levels 3 and 4 are multiplied by one or more weighting coefficients, and the other frequency components Wx1, Wx2, Wx3, Wx4, Wy1, and Wy2 are weighted with 0.
  • S4 is similar to the case where only the x direction is emphasized.
  • the enhanced image G4 shown in FIG. 11 is an image enhanced in both the x direction and the y direction
  • the enhanced image G5 is an image enhanced only in the x direction
  • the enhanced image G6 is an image enhanced only in the y direction.
  • the structure of the trabecular bone is clearer in the enhanced image G4 than before the frequency decomposition (extracted image G2 shown in FIG. 8).
  • the linear trabecular structure extending in the x direction is emphasized in the emphasized image G5
  • conversely, only the linear trabecular structure extending in the y direction is emphasized in the emphasized image G6.
  • the image processing unit 16 When the enhanced image is created, the image processing unit 16 performs a trabecular density calculation process (step S3) and a trabecular number calculation process (step S4) using the enhanced image.
  • the image processing unit 16 first binarizes the emphasized image G4 (step S31).
  • FIG. 12 shows an example of a binarized enhanced image G41.
  • the image processing unit 16 determines the image portion of the trabecular bone in the binarized enhanced image G41 and calculates the area (step S32).
  • the image processing unit 16 determines that the pixel having the signal value 0 (white pixel in FIG. 12) in the binarized enhanced image G41 is the image portion of the trabecular bone.
  • the area of the image of the trabecular bone is calculated.
  • the number of pixels of the image portion determined to be the trabecular bone is counted as indicating the area.
  • the image processing unit 16 calculates the density of the trabecular bone according to the following equation 4 from the calculated area (number of pixels) of the trabecular bone and the area of the entire image G2 extracted as the ROI (indicated by the number of pixels) (step S4). S33).
  • Trabecular density area of image determined to be trabecular / area of extracted image of ROI (4)
  • processing for calculating the number is an enhanced image G5 in which only the x direction is enhanced, and an enhanced image G6 in which only the y direction is enhanced.
  • the image processing unit 16 binarizes each of the enhanced image G5 and the enhanced image G6 (step S41).
  • step S42 mask processing is performed on each of the binarized enhanced images G5 and G6 to determine an image portion of the trabecular bone (step S42).
  • the image processing unit 16 primarily determines the image portion of the trabecular bone in each of the binarized enhanced images G5 and G6.
  • the determination method is the same as described above, and a white pixel having a signal value of 0 is determined as a trabecular bone.
  • the image portion determined as the trabecular bone is determined also in the enhanced image G4 in which both the x direction and the y direction are emphasized among the image portions determined primarily.
  • the image processing unit 16 replaces the signal value with 1 for an image portion that is not determined to be trabecular in the enhanced image G4 among image portions that are primarily determined as trabecular in the enhanced images G5 and G6.
  • the frequency component of the trabeculae may be emphasized more than necessary. Since the emphasized image G4 in which both the x direction and the y direction are emphasized is close to the state of the trabecular bone when viewed, the trabecular determination accuracy is close to that of the visual image. Only the determined image portion is a target for calculating the number of trabeculae.
  • the image processing unit 16 performs anisotropic expansion / contraction processing on the binarized enhanced images G5 and G6 (step S43).
  • the expansion / contraction process is performed by combining the expansion process and the contraction process.
  • the dilation processing refers to pixels around a certain pixel (referred to as a pixel of interest), and if there is at least one pixel with a signal value of 1, the signal value of that pixel of interest is set to 1, and the signal values of other surrounding pixels Is a process of setting 0 to 0.
  • the contraction process is the opposite, and if there is at least one pixel with a signal value of 0 in the periphery, the signal value of the pixel of interest is set to 0, and the signal values of other peripheral pixels are set to 1.
  • the image portion of the trabecular bone should have many long line images (linear images) extending in the x direction or the y direction. Later, the number of lines will be calculated by converting the line image into an image having a pixel width of 1 by thinning processing, but the images forming the same line structure are connected as much as possible, and the image parts forming another line structure are not connected. By doing so, the number of trabeculae can be calculated with high accuracy. Therefore, an expansion / contraction process is performed prior to the thinning process, and the image portions of the trabeculae that should originally constitute one line structure are connected.
  • FIG. 13 shows an example of the result of processing the expansion process four times and the contraction process four times in this order.
  • the processed image G51 is a processing result of the enhanced image G5
  • the processed image G61 is a processed result of the enhanced image G6. If the expansion process and the contraction process are simply repeated, the image portion that is supposed to be connected to form the same line as the part surrounded by a circle in FIG. The image parts of different lines that should not be connected are connected.
  • anisotropic expansion / contraction processing is performed in which expansion / contraction is performed only in either the x direction or the y direction. That is, for the enhanced image G5 in which only the x direction is enhanced, the image portion of the trabecular bone that extends linearly in the x direction is to be connected, so that it expands or contracts only in the x direction. Similarly, the enhanced image G6 only in the y direction is expanded or contracted only in the y direction so as to connect the image portions of the trabeculae extending linearly in the y direction.
  • templates T11 to T14 shown in FIG. 14 are used.
  • the template T11 is matched so that the target pixel having the signal value 0 is located in the center, and any one of pixels adjacent to the x direction (pixels indicated by asterisks in FIG. 14). However, if there is a signal value of 1, the signal value of the target pixel is set to 1.
  • the same process may be performed using the template T13.
  • the template T12 is used for matching so that the target pixel having the signal value 1 is located in the center, and any one of the adjacent pixels in the x direction has a signal value of 0. If there is, the signal value of the target pixel is set to zero.
  • the reduction process is performed only in the y direction
  • the same process is performed using the template T14.
  • FIG. 15 shows the result of the anisotropic expansion / contraction process.
  • the processed image G52 is the processing result of the enhanced image G5
  • the processed image G62 is the processed result of the enhanced image G6.
  • the image portions of different lines are connected, but in the processed image G52 of FIG. 15, such connection is not made.
  • the image portion of the trabecular bone extending in the y direction could not be connected well and was disconnected, but in the processed image G62 in FIG. 15, the connection was successful.
  • the thinning process is a process of converting a trabecular image into a line image having a width of 1 pixel.
  • the thinning process can employ a technique described in “Digital Image Processing for Image Understanding (II)” (by Junichiro Toriwaki, Shogodo, 1999). For example, pay attention to a pixel having a signal value of 1, and refer to 3 pixels ⁇ 3 pixels centered on the target pixel. Prepare several templates to detect the area where the signal value is 1 in half of the 3 pixels x 3 pixels and the signal value is 0 in the remaining half of the area. For example, the signal value of the target pixel is set to 0.
  • the thinning process is a process of reducing the line width so that the processed line image is positioned at the center of the original image. Therefore, if the thinning process is simply performed, the line width in the x direction and the y direction is reduced. Will be reduced.
  • the trabecular line image is shorter than the trabecular image in the original processed image G62, The image itself will be missing. In this case, the significance of connecting the image portions of the trabeculae by the expansion / contraction process is lost, and the number of trabeculae cannot be accurately calculated.
  • anisotropic thinning processing is performed. That is, the processing image G52 that is emphasized only in the x direction is thinned only in the y direction, and the processing image G62 that is emphasized only in the y direction is thinned only in the x direction.
  • the trabecular image is reduced only in the y direction, and in the processed image G62, the trabecular image is reduced only in the x direction. Therefore, the lengths of the line images of the plurality of trabeculae extending in the x direction or the y direction, each of which has been enhanced, are not shortened by thinning.
  • templates T21 and T22 shown in FIG. 17 are used.
  • Templates T21 and T22 in FIG. 17 are templates for detecting a line structure extending in the y direction. That is, an image of 3 pixels ⁇ 3 pixels centering on the target pixel of signal value 1 is extracted, and this extracted image and templates T21 and T22 are collated. If the signal values 0 and 1 defined at the 3 ⁇ 3 positions of the templates T21 and T22 match the signal values at the positions of the extracted image, it is determined that the template T21 and T22 are matched, and Change signal value 1 to 0.
  • the same processing may be performed using the templates T23 and T24 shown in FIG.
  • Templates T23 and T24 are templates for detecting a line structure extending in the x direction.
  • the templates T21 to T24 are thinned by four linkages, the thinning may be performed in an oblique direction using an eight-linked template.
  • 4-link and 8-link is described in the document “Digital Signal Processing Series Vol. 7, Digital Image Processing for Image Understanding (II) (p12 to p14, Junichiro Toriwaki et al., Shosodo, 1994)”. Are known.
  • FIG. 18 is a diagram showing a processed image G64 obtained by performing anisotropic thinning processing on the processed image G62. Since the processed image G64 is thinned only in the x direction, the image of the trabecular bone disappeared in the processed image G63 in FIG. 16 without reducing the trabecular image in the y direction (FIG. 16). The part surrounded by a frame also remains as a line image in the processed image G64 of FIG.
  • the image processing unit 16 determines a pixel having a signal value of 0 as an image portion of the trabecular in the processed image, and determines the number of pixels of the image portion of the trabecular bone.
  • Count (step S45). The total number of pixels counted corresponds to the sum of the lengths of a plurality of trabeculae included in the image extracted as the ROI.
  • the image processing unit 16 calculates the number of trabeculae from Expression 5 below, assuming that the length of one trabecular bone extending in the x direction or the y direction corresponds to the length of one side of the image G2 extracted as the ROI.
  • the number of Expression 5 is an index indicating how many line images cross the extracted image G2 in the x direction or the y direction.
  • the length of one side of the extracted image G2 used in Equation 5 is the length in the x direction when the number is obtained from the processed image that is emphasized only in the x direction, and is the length of the processed image that is emphasized only in the y direction.
  • the length in the y direction is adopted.
  • Number of trabeculae number of pixels in trabecular image / length of one side of ROI extraction image (5)
  • the processing image G64 shown in FIG. 19 will be described as an example.
  • the length of one side of the image G2 extracted as the ROI is 200 pixels on one side in both the x and y directions. Since the processed image G64 is an image that is emphasized only in the y direction, the length of one side of the extracted image is 200 pixels in the y direction.
  • the number of pixels in the image portion of the trabecular bone is 600 pixels
  • the image processing unit 16 appends the calculated feature quantity information to the header of the original medical image. Then, the feature amount information is transmitted to an external server or the like together with the medical image by the communication control of the control unit 11.
  • the image processing unit 16 performs frequency decomposition by binomial wavelet transform on the medical image of the bone, and weights the frequency component at a level corresponding to the trabecular bone.
  • the levels corresponding to the trabeculae are levels 3 and 4 in which the peak of the frequency intensity of the wavelet waveform is in the range of the wavelength of 100 ⁇ m or more and 500 ⁇ m or less converted to the actual size of the subject.
  • the image is reconstructed to create an emphasized image in which the trabecular bone is emphasized.
  • the feature amount of the trabecular bone is calculated using the created enhanced image.
  • the image is reconstructed using only frequency components of other levels excluding the frequency component. Since the trabecular bone corresponds to the level of the medium frequency component, the lowest level high frequency component and the highest level low frequency component that do not correspond to the trabecular bone can be excluded, and an enhanced image that emphasizes only the trabecular bone is created. Can do. Thereby, the trabecular feature amount can be calculated with high accuracy.
  • the image processing unit 16 calculates the trabecular density as the trabecular feature amount.
  • the image processing unit 16 creates a reconstructed enhanced image G4 by weighting the frequency components of levels 3 and 4 corresponding to the trabecular bone in the x direction and the y direction, and the image portion of the trabecular bone in the enhanced image G4. judge. Then, the area ratio between the area of the image portion determined to be a trabecular bone and the area of the entire extracted image G2 from the emphasized image is calculated as a density.
  • the image portion of the trabecular bone can be grasped with high accuracy, and the density thereof can be calculated with high accuracy.
  • the image processing unit 16 calculates the number of trabeculae as the trabecular feature amount.
  • the image processing unit 16 creates an enhanced image G5 reconstructed by weighting only the x direction in the level 3 and 4 frequency components corresponding to the trabecular bone, and an enhanced image G6 reconstructed by weighting only the y direction. Then, the image portion of the trabecular bone is determined in the emphasized images G5 and G6, and the number of trabecular bones is calculated from the number of pixels.
  • determining the trabecular bone masking is applied to the emphasized images G5 and G6 to determine the image portion of the trabecular bone. Thereby, a trabecular bone can be determined by removing an image portion emphasized more than necessary.
  • anisotropic thinning is performed. Thereby, the length of the line image extending in the x direction or the y direction can be reduced without being reduced in the extending direction.
  • an anisotropic expansion / contraction process is performed prior to the anisotropic thinning process.
  • the calculation of the trabecular feature amount is described using a medical image obtained by X-ray imaging as an example.
  • Medical images obtained by MRI (Magnetic Resonance Imaging) or the like may be targeted.
  • frequency decomposition is performed up to levels 3 and 4 corresponding to the trabecular bone
  • a configuration in which the frequency decomposition at a higher level than the levels 3 and 4 corresponding to the trabecular bone may be performed.
  • frequency components from level 1 to level 5 may be performed, and weighting may be performed so that image reconstruction is performed by removing frequency components of the lowest level 1 and the highest level 5 among them.
  • the effect of anisotropic expansion / contraction treatment or anisotropic thinning treatment is greater when the direction of the trabecular bone forming the network structure matches the x direction and the y direction as much as possible.
  • processing such as rotation of the medical image may be performed so that the trabecular direction matches the x and y directions as much as possible. For example, an imaging region (hand, finger, etc.) is detected from a medical image, and the angle of how much is displaced is detected. Then, a process for rotating the medical image by the angle of deviation is performed.
  • the feature amount may be calculated after detecting the angle shifted as described above and performing the rotation process.
  • a non-volatile memory such as a ROM and a flash memory
  • a portable recording medium such as a CD-ROM
  • a carrier wave (carrier wave) is also applied to the present invention as a medium for providing program data according to the present invention via a communication line.

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Abstract

La présente invention concerne un dispositif de traitement d'image caractérisé en ce qu'il présente une unité de traitement d'image qui réalise une décomposition fréquentielle d'une image médicale d'une partie d'os, par transformation en ondelette binaire. Ce dispositif permet de pondérer le composant de fréquence d'un niveau correspondant à un os trabéculaire. Ainsi, il est possible de reconstruire ensuite l'image afin de créer (à l'étape S2) une image accentuée avec l'os trabéculaire accentué, et de calculer (aux étapes S3 et S4) les quantités mises en avant de l'os trabéculaire en utilisant l'os accentué.
PCT/JP2009/051069 2008-03-06 2009-01-23 Dispositif de traitement d'image WO2009110260A1 (fr)

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