WO2016062107A1 - 选择检测区域的方法及装置及弹性检测系统 - Google Patents

选择检测区域的方法及装置及弹性检测系统 Download PDF

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WO2016062107A1
WO2016062107A1 PCT/CN2015/081817 CN2015081817W WO2016062107A1 WO 2016062107 A1 WO2016062107 A1 WO 2016062107A1 CN 2015081817 W CN2015081817 W CN 2015081817W WO 2016062107 A1 WO2016062107 A1 WO 2016062107A1
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organ tissue
region
detection
image
area
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PCT/CN2015/081817
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English (en)
French (fr)
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邵金华
孙锦
段后利
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无锡海斯凯尔医学技术有限公司
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Priority to BR112017008162-8A priority Critical patent/BR112017008162B1/pt
Priority to JP2017512997A priority patent/JP6588087B2/ja
Priority to EP15852840.6A priority patent/EP3210541A4/en
Priority to KR1020177006749A priority patent/KR101913976B1/ko
Priority to RU2017117301A priority patent/RU2695619C2/ru
Priority to AU2015335554A priority patent/AU2015335554B2/en
Publication of WO2016062107A1 publication Critical patent/WO2016062107A1/zh
Priority to US15/478,021 priority patent/US10925582B2/en

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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61B8/48—Diagnostic techniques
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    • A—HUMAN NECESSITIES
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    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/0035—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography
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    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61B8/486—Diagnostic techniques involving arbitrary m-mode

Definitions

  • Embodiments of the present invention relate to the field of medical image processing technologies, and in particular, to a method and apparatus for selecting a detection area and an elastic detection system.
  • the organ tissue detection region is mainly selected by the following two methods: the first type, the organ tissue of a fixed depth range is used as the detection area; and the second, the organ tissue detection area is artificially selected.
  • the first method because the detection depth is fixed, but in fact different people, different locations of the same person, its tissue position and shape are different.
  • the equipment used to detect the instantaneous elasticity of organs on the market is generally fixed to the subcutaneous 2.5-6.5 cm organ tissue, but for obese or large individuals, the subcutaneous 3.5 cm may still be the cortex. Therefore, the method of using a fixed detection range introduces errors for some individuals.
  • the second method which adopts the method of manually selecting the detection area, requires the operator to be very familiar with the organ tissue structure and image information, in order to accurately select the organ tissue boundary, and therefore requires high operator; meanwhile, a test process is introduced. The process of artificial selection also takes longer to detect.
  • the present invention provides a method of selecting a detection region, comprising:
  • An organ tissue detection region is determined according to the organ tissue boundary region and a preset feature value condition.
  • the present invention provides an apparatus for selecting a detection area, comprising: an area dividing unit for dividing the organ tissue information to be identified into a plurality of detection sub-areas; and a feature value calculation unit for calculating the detection sub-area a feature value of the organ tissue information; a boundary region identifying unit configured to determine an organ tissue boundary region according to the organ tissue information to be identified; a detection region determining unit configured to use the organ tissue boundary region and the preset feature value Conditions determine the organ tissue detection area.
  • the present invention provides an elastic detection system including an information acquisition device, an elastic imaging device, a probe setting device, a processing device, and a display device, and a device for selecting a detection region provided in any embodiment of the present invention, wherein
  • the information acquiring device is configured to acquire organ tissue information to be identified
  • the probe setting device is configured to adjust a position of the probe in the elastic imaging device, so that a detection range of the probe includes the device for selecting the detection region.
  • the elastic imaging device is configured to acquire elasticity information of an organ tissue; and the display device is configured to display elasticity information in the detection region.
  • the method and apparatus for selecting a detection area and the elastic detection system provided in the embodiments of the present invention can automatically adjust an organ tissue detection area.
  • Selective detection area provided in the embodiment of the present invention determines the boundary region of the organ tissue according to the organ tissue information to be identified, and determines the organ tissue detection region according to the organ tissue boundary region and the preset feature value condition.
  • the position and size of the detection region are different, that is, the method can adjust the position and size of the organ detection region.
  • FIG. 1 is a flowchart showing an implementation of a method for selecting a detection area according to a first embodiment of the present invention
  • FIG. 2 is a flowchart showing an implementation of a method for selecting a detection area according to a second embodiment of the present invention
  • FIG. 3 is an effect diagram of a selection detection region of an M-type ultrasonic signal based on organ tissue in a second embodiment of the present invention
  • Figure 4 is a schematic view showing the quantitative elastic modulus of organ tissues in the second embodiment of the present invention.
  • FIG. 5 is a flowchart showing an implementation of a method for selecting a detection area according to a third embodiment of the present invention.
  • FIG. 6 is an effect diagram of a selection detection region of an organ tissue-based B-type ultrasonic signal in a third embodiment of the present invention.
  • Figure 7 is a schematic view showing the quantitative elastic modulus of organ tissues in a third embodiment of the present invention.
  • FIG. 8 is a flowchart showing an implementation of a method for selecting a detection area according to a fourth embodiment of the present invention.
  • Figure 9 is an effect diagram of a three-dimensional image boundary based on organ tissue in a fourth embodiment of the present invention.
  • Figure 10 is an effect diagram of a selection detection area of a three-dimensional image based on organ tissue in a fourth embodiment of the present invention.
  • Figure 11 is a schematic view showing the quantitative elastic modulus of organ tissues in a fourth embodiment of the present invention.
  • FIG. 12 is a schematic structural diagram of an apparatus for selecting a detection area according to a fifth embodiment of the present invention.
  • Figure 13 is a block diagram showing the structure of an elastic detecting system provided in a sixth embodiment of the present invention.
  • FIG. 1 is a flow chart showing an implementation of a method of selecting a detection area provided in a first embodiment of the present invention, which may be performed by a device that selects a detection area.
  • the implementation process includes:
  • Step 11 Divide the organ tissue information to be identified into a plurality of detection sub-regions, and calculate feature values of organ tissue information in each detection sub-region.
  • the organ tissue information to be identified may include a one-dimensional, two-dimensional or three-dimensional ultrasound image of the organ tissue, and may also include one-dimensional, two-dimensional or three-dimensional ultrasound signals of the organ tissue, for example, the organ tissue information may be a type A of the organ tissue.
  • the characteristic value of the organ tissue information may be an average of organ tissue information or a standard deviation of organ tissue information.
  • Step 12 Determine a boundary region of the organ tissue according to the organ tissue information to be identified.
  • the boundary region of the organ tissue may be determined according to the characteristic value of the organ tissue information in each detection sub-region calculated in step 11, and may also adopt an image processing technique or a signal processing technique according to the organ tissue corresponding to the organ tissue information. Characteristics of features and organ tissue boundaries identify organ tissue boundary regions in the organ tissue information.
  • the organ tissue boundary region is determined according to a feature value of organ tissue information in the detection sub-region;
  • the organ tissue information is a three-dimensional ultrasound image of an organ tissue, the organ tissue information is identified according to the characteristics of the organ tissue and the characteristics of the organ tissue boundary Organ tissue boundary area.
  • Step 13 Determine an organ tissue detection region according to the organ tissue boundary region and a preset feature value condition.
  • the preset feature value condition may be that the distance from the boundary region of the organ tissue is within a preset depth range. That is, organ tissue information within a predetermined depth range from the boundary region of the organ tissue may be determined as a detection region of organ tissue information. Wherein, the preset depth range may be 2.6-6.5 cm.
  • the preset eigenvalue condition may be: an average value corresponding to an intensity value of an image or a signal in each detection sub-region The standard deviations all meet the preset range.
  • determining an organ tissue detection region according to the organ tissue boundary region and a preset feature value condition may include: if the organ tissue boundary region is within In the continuous detection sub-region, the standard deviation corresponding to the intensity value of the ultrasonic signal in each detection sub-region is smaller than the standard deviation threshold, and the plurality of consecutive detection sub-regions are determined as the organ tissue detection region.
  • determining an organ tissue detection region according to the organ tissue boundary region and a preset feature value condition may include: In the plurality of consecutive detection sub-regions within the boundary region, the mean values corresponding to the intensity values of the images in each detection sub-region are smaller than the mean threshold, and the standard deviation corresponding to the intensity values of the images in each detection sub-region is less than the standard deviation.
  • the threshold value is determined by the plurality of consecutive detection sub-regions as the organ tissue detection region.
  • the mean threshold may be 20% of the maximum intensity value of the ultrasound signal or image in each detection sub-area
  • the standard deviation threshold may be 5% of the maximum intensity value of the ultrasound signal or image in each detection sub-area.
  • the intensity of the CT image in the detection sub-area in the liver tissue ranges from 0 to 300 HU (Hounsfield unit, Heinz), and the mean threshold can be 60 HU, and the standard deviation threshold The value can be 15HU.
  • the method further includes: calculating an elasticity value of the organ tissue in the organ tissue detection region. That is, the elasticity value of the organ tissue in the determined area of the organ tissue detection is calculated to achieve ultrasonic detection of the organ tissue.
  • the method for selecting a detection region divides organ tissue information into a plurality of detection sub-regions, and calculates feature values of organ tissue information in each detection sub-region; and determines the organ tissue according to organ tissue information.
  • the boundary region, and the organ tissue detection region is determined according to the organ tissue boundary region and the preset feature value condition, that is, the method can automatically select the detection region.
  • the method for selecting a detection region provided in the first embodiment of the present invention when the organ tissue information is different, the determined detection regions are different, that is, in the first embodiment of the present invention, the characteristics of the organ tissue information according to different individuals can be , automatically adjust the position and size of the detection area.
  • FIG. 2 is a flow chart showing an implementation of a method of selecting a detection region provided in a second embodiment of the present invention, which is applicable to a one-dimensional ultrasonic signal of organ tissue.
  • Fig. 3 is a view showing the effect of the selective detection region of the M-type ultrasonic signal based on the organ tissue in the second embodiment of the present invention; and
  • Fig. 4 is a view showing the quantitative elastic modulus of the organ tissue in the second embodiment of the present invention. Referring to Figures 2 to 4, the method includes:
  • Step 21 The ultrasonic signal of the organ tissue is divided into a plurality of detection sub-regions S i .
  • the one-dimensional ultrasound signal of the organ tissue may be a type A ultrasound signal of an organ tissue or an M-type ultrasound signal of an organ tissue.
  • an ultrasound signal contains n sampling points whose corresponding organ tissue ultrasound signal has a scanning depth of d (unit: mm), and then contains n/d points per 1 mm depth.
  • the n sampling points are divided into multi-segment detection sub-regions S i , and the scanning depth corresponding to the detection sub-regions S i is d i , where i takes an integer, and the scanning depth d i may be the depth mean or end value of the detection sub-region S i , here take the end value.
  • the z sampling points are divided into multi-segment detection sub-regions S i at z intervals. Since in the ultrasound imaging, the bottom of the image (ie, the deepest corresponding scanning depth) generally does not include the detection target, the bottom of the image can be ignored.
  • Information, at this time, i 1, 2, ..., [d/z]-1, z is the length of the interval of the detection sub-region (unit: mm), and [] is the round-up operation.
  • each segment of the detection sub-region contains [zn/d] sampling points.
  • Step 22 the ultrasonic signal is calculated for each R i organs detecting sub-area S i of the Nakagami distribution value m i.
  • the Nakagami statistical model is a kind of ultrasonic tissue characterization technique. Specifically, according to the following formula, R ultrasound signal corresponding to the image calculation organs within each detection sub-area S i i Nakagami distribution value of m i,
  • the probability density function of the Nakagami distribution is:
  • E(.) is the mean function
  • ⁇ (.) represents the gamma function
  • ⁇ E(r 2 )
  • U(.) represents the unit step function
  • m is the Nakagami distribution value
  • r is the probability distribution function f ( The dependent variable of r), r ⁇ 0, m ⁇ 0; for each detection sub-region S i , m i is the value of m in the S i region, and R i is the envelope value of the ultrasonic signal.
  • the one-dimensional ultrasonic signal of the organ tissue obeys the pre-Rayleigh distribution; when the m value is equal to 1, the one-dimensional ultrasonic echo signal obeys the Rayleigh distribution; When the m value is greater than 1, the one-dimensional ultrasonic echo signal obeys the post-Rayleigh distribution.
  • Step 23 Calculate the weight W i of each detection sub-area S i according to the following formula, and determine the detection sub-area corresponding to the maximum weight value as the organ tissue boundary area:
  • d i is a scanning depth corresponding to the detection sub-region S i , and may also take a depth mean or end value of the detection sub-region S i , traverse the weight W i of each detection sub-region, and select a detector corresponding to the maximum weight value.
  • Area as the boundary area of organ tissues.
  • Step 24 If the standard deviation corresponding to the intensity value of the one-dimensional ultrasonic signal in each detection sub-area is less than the standard deviation threshold in the plurality of consecutive detection sub-areas within the boundary region of the organ tissue, the plurality of consecutive detections The sub-area is determined as the organ tissue detection area.
  • the quantitative elasticity information may include the value of the quantitative elasticity information of the organ tissue in the detection area determined in the step 23 measured by the elastic measuring means, usually in units of kPa.
  • the vertical axis represents the scanning depth and the horizontal axis represents the time.
  • the quantitative elastic information may also include a trajectory image of transient vibration propagating along the depth over time during transient elastography.
  • the line also contains a line segment AB indicating the propagation of the instantaneous vibration.
  • the propagation velocity of the shear wave generated by the transient vibration in the region indicated by the indication mark can be calculated, thereby calculating the elastic modulus of the organ tissue.
  • the method for selecting a detection region provided in the second embodiment of the present invention can automatically select an organ tissue detection region by an ultrasound signal of an A- or M-supermicro of an organ tissue.
  • the recognition efficiency of the organ tissue boundary is high, so that real-time automatic localization of the organ tissue boundary can be realized.
  • FIG. 5 is a flow chart showing an implementation of a method of selecting a detection region provided in a third embodiment of the present invention, which is applicable to an ultrasonic signal of an organ tissue.
  • Fig. 6 is a view showing the effect of the selective detection region of the B-type ultrasonic signal based on the organ tissue in the third embodiment of the present invention; and
  • Fig. 7 is a view showing the quantitative elastic modulus of the organ tissue in the third embodiment of the present invention.
  • the method includes:
  • Step 31 Divide the two-dimensional ultrasound image of the organ tissue into a plurality of rectangular detection sub-regions R ij , i, j are natural numbers, and respectively represent row and column numbers of each detection sub-region.
  • the two-dimensional ultrasound image of the organ tissue may be a B-mode ultrasound image of organ tissue.
  • the size of a B-ultrasound image is w*h, where w is the width of the two-dimensional ultrasound image of the organ tissue, and h is the height of the two-dimensional ultrasound image of the organ tissue (the units of w and h are both pixels), corresponding
  • the scanning depth is d (unit: mm), and the depth of 1 mm includes h/d pixels on one scanning line in the depth direction.
  • the B-mode image of size w*h is divided into a plurality of rectangular detection sub-regions R ij .
  • Step 32 Calculate the weight W ij of each detection sub-region R ij , and determine the detection sub-region corresponding to the maximum weight value as the organ tissue boundary region.
  • the two-dimensional ultrasound image may be divided into two along the midline, and only the detectors in the half-dimensional two-dimensional ultrasound image above the midline are calculated.
  • the weight W kj can be calculated according to the following formula:
  • M kj is the gray mean of the two-dimensional ultrasound image of the organ tissue in the detection sub-region R kj
  • SD kj is the gray standard deviation of the two-dimensional ultrasound image of the organ tissue in the detection sub-region R kj
  • d kj is the detector The scan depth corresponding to the region R kj .
  • the apparent k i max / 2, two-dimensional ultrasound image of the organ when the tissue is divided into a rectangular region of the side length z, And k is an integer, and i max is the maximum value in the range of values of i.
  • the hepatic envelope region exhibits a uniform high echo on the B-ultrasound image
  • the grayscale mean value of the organ boundary region is large; in addition, since the hepatic envelope region has uniform uniformity on the B-mode ultrasound image, The gray scale standard deviation is small.
  • the search is performed from the detection sub-area located at the center line of the B-mode image, and if the detection sub-area R k1 is the one with the largest weight among the series of detection areas R kj Then, the detection sub-region R k1 is determined as a boundary region of liver tissue.
  • Step 33 If a plurality of consecutive detection sub-regions within the boundary region of the organ tissue, the mean value corresponding to the intensity value of the image in each detection sub-region is less than a mean threshold, and the intensity value of the image in each detection sub-region The standard deviation of each is less than the standard deviation threshold, and the plurality of consecutive detection sub-regions are determined as the organ tissue detection region.
  • the mean value corresponding to the intensity value of the image in each detection sub-region is smaller than the mean threshold, and each detection sub-region The standard deviation of the intensity values of the inner images is less than the standard deviation threshold, and then the plurality of consecutive detection sub-regions are determined as the detection regions, that is, the automatic selection of the detection regions is completed.
  • the quantitative elasticity information includes the numerical value (in kPa) of the quantitative elasticity information of the organ tissue in the detection region in the organ histogram obtained by the elastic measurement.
  • Elastic modulus letter The information includes an elastic modulus distribution map of the organ structure in the detection area.
  • the elastic modulus distribution map can be color coded, different colors represent different elastic moduli; the distribution of the elastic modulus can also be expressed in gray scale or other coding form, that is, color coding, gray scale or other coding forms can be used.
  • the elastic modulus profile further includes a scale map representing the elastic modulus coding.
  • the method of selecting a detection region provided in the third embodiment of the present invention can automatically select an organ tissue detection region by an image of B-mode ultrasound of an organ tissue.
  • the recognition efficiency of the organ tissue boundary is high, so that real-time automatic localization of the organ tissue boundary can be realized.
  • FIG. 8 is a flowchart showing an implementation of a method of selecting a detection area according to a fourth embodiment of the present invention, which is applicable to a three-dimensional image of organ tissue.
  • 9 is an effect diagram of a three-dimensional image boundary based on organ tissue in a fourth embodiment of the present invention.
  • FIG. 10 is an effect diagram of a selection detection region of a three-dimensional image based on organ tissue in the fourth embodiment of the present invention;
  • Step 41 Extract a binary image of the skin and a binary image of the bone in the CT image of the organ tissue or the MRI image of the organ tissue by using a region growing segmentation method.
  • the binary image of the skin is extracted by the region growing segmentation method, wherein the criterion for the region growth corresponding to the CT value of the air is [-1024,-500]HU (Hounsfield unit , Heinz).
  • a binary image of the bone is extracted, including a binary image of the vertebrae and a binary image of the rib.
  • a threshold segmentation with a threshold range of [350, 1024] HU is performed on the entire image, and a binary image of the skeleton is extracted.
  • Step 42 Calculate a centroid of the binary image of the bone, and calculate a point on the binary image of the skin that is closest to the centroid.
  • centroid P C of the binary image of the bone Since the ribs are generally bilaterally symmetrical along the vertebrae and the vertebrae have a large specific gravity in the bone image, the centroid of the bone image is the centroid of the vertebrae P C .
  • Step 43 Divide the organ tissue information into four quadrants according to the centroid and a point closest to the centroid.
  • the CT image is divided into four quadrants by using the centroid P C and the point P N closest to the centroid, that is, the line where the centroid P C and the point P N closest to the centroid are located as the ordinate axis, passes through the centroid P C and a straight line perpendicular to the ordinate axis is the abscissa axis.
  • organ tissue as an example of liver tissue, most of the liver is located in the second quadrant.
  • Step 44 Fit each rib point in the second quadrant to obtain a rib fitting curve.
  • Step 45 Move the rib fitting curve to a first quadrant to move a preset value as a boundary curve, and determine a region between the boundary curve and the rib fitting curve as an organ tissue boundary region.
  • the rib curve Since the rib curve is close to the liver capsule, the rib curve is moved inward by a preset value as a boundary curve, and the area between the boundary curve and the rib fitting curve is determined as an organ tissue boundary region.
  • the preset value may be 5 mm.
  • Step 46 Determine an area surrounded by the boundary region of the organ tissue as an organ tissue region.
  • Step 47 Divide the organ tissue region into a plurality of detection sub-regions.
  • Step 48 Calculate a standard deviation corresponding to the mean value and the intensity value corresponding to the intensity value of the two-dimensional ultrasonic image of the organ tissue in each of the detection sub-regions.
  • Step 49 If a plurality of consecutive detection sub-areas in the area surrounded by the boundary area of the organ tissue, the mean values corresponding to the intensity values of the images in each detection sub-area are less than the mean threshold, and each detection If the standard deviation corresponding to the intensity values of the images in the sub-areas is less than the standard deviation threshold, the plurality of consecutive detection sub-regions are determined as the organ tissue detection regions.
  • the mean values corresponding to the intensity values of the images in each detection sub-region are smaller than the mean threshold, and within each detection sub-region If the standard deviation of the intensity values of the images is less than the standard deviation threshold, the plurality of consecutive detection sub-regions are determined as the organ tissue detection region, that is, the automatic selection of the organ tissue is completed.
  • the quantitative elasticity information includes the numerical value (in kPa) of the quantitative elasticity information of the tissue in the histogram obtained by the elastic measurement.
  • the elastic modulus information also includes an elastic modulus distribution map of the tissue indicated by the indicator in the organization chart. The figure can be color coded and the different colors represent different elastic moduli.
  • the distribution of the elastic modulus can also be expressed in grayscale or other encoded form.
  • the above elastic modulus distribution map further includes a scale map indicating the elastic modulus coding.
  • the automatic recognition of the boundary region of the organ tissue and the automatic adjustment of the position and size of the detection region are realized by using the CT image or the MRI image.
  • the automatically selected detection area can be a three-dimensional geometry.
  • the detection region of the image of the frame organ tissue is automatically selected by the method for selecting the detection region provided in this embodiment. Then, using each detection area corresponding to each frame image, a three-dimensional geometry is reconstructed, that is, a three-dimensional detection area.
  • the elastic detection probe is used to detect the elastic information in the two-dimensional detection area of each frame CT image, and then the three-dimensional elastic distribution of the organ tissue is reconstructed, thereby obtaining the three-dimensional quantitative elastic information of the organ tissue.
  • the method of selecting a detection region provided in the fourth embodiment of the present invention can automatically select an organ tissue detection region by a three-dimensional image of an organ tissue, for example, a CT image of an organ tissue or an MRI image of an organ tissue.
  • a three-dimensional image of an organ tissue for example, a CT image of an organ tissue or an MRI image of an organ tissue.
  • FIG. 12 is a schematic structural diagram of an apparatus for selecting a detection area according to a fifth embodiment of the present invention.
  • the apparatus for selecting a detection area according to this embodiment may include: an area dividing unit 51 for waiting The identified organ tissue information is divided into a plurality of detection sub-regions; the feature value calculation unit 52 is configured to calculate feature values of the organ tissue information in the detection sub-region; and the boundary region identification unit 53 is configured to determine, according to the organ tissue information to be identified The organ tissue boundary region; the detection region determining unit 54 is configured to determine an organ tissue detection region according to the organ tissue boundary region and a preset feature value condition.
  • the device may further comprise: an elasticity value calculation unit, configured to calculate an elasticity value of the organ tissue in the organ tissue detection region.
  • the preset feature value condition may be that the distance from the boundary region of the organ tissue is within a preset depth range.
  • the preset eigenvalue condition may be: an average value corresponding to an intensity value of an image or a signal in each detection sub-region The standard deviations all meet the preset range.
  • the boundary region identification unit 53 may include: a calculating sub-unit, for calculating a one-dimensional ultrasonic signal R i each detection organ and tissue in the sub-region S i a Nakagami distribution value m i ; a region identification sub-unit for calculating a weight W i of each detection sub-region S i according to the following formula, and determining a detection sub-region corresponding to the maximum weight value as an organ tissue boundary region:
  • d i is the scanning depth detecting sub-area corresponding to S i.
  • the region dividing unit 51 may be specifically configured to: calculate the weight W kj of each detection sub-region R kj according to the following formula, and detect the maximum weight value corresponding to the detection The subregion is determined as the organ tissue boundary region:
  • M kj is the gray mean of the two-dimensional ultrasound image of the organ tissue in the detection sub-region R kj
  • SD kj is the gray standard deviation of the two-dimensional ultrasound image of the organ tissue in the detection sub-region R kj
  • the two-dimensional ultrasound image of the organ tissue is divided into a plurality of rectangular detection sub-regions R ij ; the boundary region identification unit 53 can be specifically configured to: calculate the weight W kj of each detection sub-region R kj according to the following formula, and The detection sub-region corresponding to the weight value is determined as the organ tissue boundary region:
  • M kj is the gray mean of the two-dimensional ultrasound image of the organ tissue in the detection sub-region R kj
  • SD kj is the gray standard deviation of the two-dimensional ultrasound image of the organ tissue in the detection sub-region
  • d kj is the detection sub-region corresponding Scan depth
  • the boundary region identifying unit 53 may specifically include: a binary image acquiring subunit for using the image segmentation method in the organ tissue Extracting a binary image of the skin and a binary image of the bone in the CT image or the MRI image of the organ tissue; a feature point determining subunit for calculating a centroid of the binary image of the bone, and calculating the skin a point on the binary image that is closest to the centroid; an image dividing subunit for dividing the image of the organ tissue into four quadrants according to the centroid and a point closest to the centroid; a curve fitting subunit For fitting each rib point in the second quadrant to obtain a rib fitting curve; a boundary region determining subunit for moving the rib fitting curve toward the first quadrant to a preset value as a boundary region curve, and A region between the boundary region curve and the rib fitting curve is determined as an organ tissue boundary region.
  • the apparatus for selecting a detection area provided in the fifth embodiment of the present invention the organ group to be identified
  • the woven information is divided into a plurality of detection sub-regions, and the eigenvalues of the organ tissue information in the detection sub-region are calculated, and the organ tissue boundary region is determined according to the organ tissue information to be identified, and according to the determined organ tissue boundary region and the preset eigenvalue Conditions determine the organ tissue detection area.
  • the organ tissue information in the device is different, the position and size of the detection region are different, that is, the device can adjust the position and size of the detection region.
  • FIG. 13 is a schematic structural diagram of an elastic detecting system according to a sixth embodiment of the present invention.
  • the elastic detecting system of the present embodiment may include an information acquiring device 61, an elastic imaging device 63, and a probe setting device.
  • the processing device 65 and the display device 66 further include the device 62 for selecting a detection area provided in the fifth embodiment of the present invention, wherein the information acquisition device 61 is configured to acquire organ tissue information to be identified; a setting device 64, configured to adjust a position of the probe in the elastic imaging device, such that the detection range of the probe includes a detection area determined by the device that selects the detection area; and the elastic imaging device 63 is configured to acquire elastic information of the organ tissue
  • the processing device 65 is configured to process the elasticity information acquired by the elastic imaging device to obtain elasticity information in the detection area, and the display device 66 is configured to display the elasticity information in the detection area.
  • the elastic detecting system provided in the sixth embodiment of the present invention can automatically identify the boundary area of the organ tissue and automatically adjust the position and size of the detecting area, thereby saving the elastic detection time and reducing the different operations between different operators and the same operator.
  • the difference in operation is to achieve accurate, fast, and reproducible elastic detection of organ tissues.

Abstract

一种选择检测区域的方法、装置及弹性检测系统，其中该方法包括：将待识别的器官组织信息划分为多个检测子区域，并计算检测子区域中器官组织信息的特征值（11）；根据待识别的器官组织信息，确定所述器官组织边界区域（12）；根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域（13）。该方法能够根据器官组织信息确定器官组织边界，并根据器官组织边界自动的调整检测区域。

Description

选择检测区域的方法及装置及弹性检测系统 技术领域
本发明实施例涉及医学图像处理技术领域，尤其涉及选择检测区域的方法及装置及弹性检测系统。
背景技术
很多临床应用上需要利用包括超声成像、磁共振成像(MRI，Magnetic Resonance Imaging)、计算机断层扫描(CT，Computed Tomography)等在内的传统医学成像来定位器官组织的检测区域，例如：弹性检测及彩色多普勒超声检查等。
目前，主要通过以下两种方式选择器官组织检测区域：第一种，将固定深度范围的器官组织作为检测区域；第二种，人为地选择器官组织检测区域。
其中，第一种方法，由于检测深度固定，但是实际上不同的人，同一个人不同的位置，其组织位置和形态都有差别。比如目前市场上所见用于检测器官瞬时弹性的设备对于普通人，其检测范围是固定的皮下2.5-6.5cm的器官组织，但是对于肥胖或者体型大的个体，皮下3.5cm都有可能还是皮层，因此，采用固定检测范围的方法对于一些个体会引入误差。
第二种方法，采用手动选择检测区域的方法，需要操作者对于器官组织结构和图像信息非常熟悉，才能准确的选择器官组织边界，因此对于操作者要求高；同时，由于检测过程中引入了一个人为选择的过程，检测时间也更长。
综上，尚且缺乏一种自动调整检测区域的方法。
发明内容
本发明的目的是提出选择检测区域的方法及装置及弹性检测系统，以自动调整检测区域。
一方面，本发明提供了一种选择检测区域的方法，包括：
根据待识别的器官组织信息，确定所述器官组织边界，并将所述器官组织边界所围的区域确定为器官组织区域；
将待识别的器官组织信息划分为多个检测子区域，并计算检测子区域中器官组织信息的特征值；
根据待识别的器官组织信息，确定器官组织边界区域；
根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域。
另一方面，本发明提供了一种选择检测区域的装置，包括：区域划分单元，用于将待识别的器官组织信息划分为多个检测子区域；特征值计算单元，用于计算检测子区域中器官组织信息的特征值；边界区域识别单元，用于根据待识别的器官组织信息，确定器官组织边界区域；检测区域确定单元，用于根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域。
又一方面，本发明提供了一种弹性检测系统，包括信息获取装置、弹性成像装置、探头设置装置、处理装置和显示装置，还包括本发明任意实施例中提供的选择检测区域的装置，其中，所述信息获取装置，用于获取待识别的器官组织信息；所述探头设置装置，用于调整弹性成像装置中探头的位置，使所述探头的检测范围包含所述选择检测区域的装置确定的检测区域；所述弹性成像装置，用于获取器官组织的弹性信息；所述显示装置，用于显示所述检测区域中的弹性信息。
本发明实施例中提供的选择检测区域的方法及装置及弹性检测系统，能够自动的调整器官组织检测区域。本发明实施例中提供的选择检测区域 的方法，依据待识别的器官组织信息确定器官组织边界区域，并根据器官组织边界区域和预设的特征值条件确定器官组织检测区域。该方法中器官组织信息不相同时，检测区域的位置和大小不相同，即该方法能够调整器官检测区域的位置和大小。
附图说明
此处所说明的附图用来提供对本发明实施例的进一步理解，构成本发明实施例的一部分，并不构成对本发明实施例的限定。在附图中：
图1是本发明第一实施例中提供的选择检测区域的方法的实现流程图；
图2是本发明第二实施例中提供的选择检测区域的方法的实现流程图；
图3是本发明第二实施例中的基于器官组织的M型超声信号的选择检测区域的效果图；
图4是本发明第二实施例中器官组织的定量弹性模量的示意图；
图5是本发明第三实施例中提供的选择检测区域的方法的实现流程图；
图6是本发明第三实施例中的基于器官组织的B型超声信号的选择检测区域的效果图；
图7是本发明第三实施例中器官组织的定量弹性模量的示意图；
图8是本发明第四实施例中提供的选择检测区域的方法的实现流程图；
图9是本发明第四实施例中的基于器官组织的三维图像边界的效果图；
图10是本发明第四实施例中的基于器官组织的三维图像的选择检测区域的效果图；
图11是本发明第四实施例中器官组织的定量弹性模量的示意图；
图12是本发明第五实施例中提供的选择检测区域的装置的结构示意图；
图13是本发明第六实施例中提供的弹性检测系统的结构示意图。
具体实施方式
下面结合附图及具体实施例对本发明实施例进行更加详细与完整的说明。可以理解的是，此处所描述的具体实施例仅用于解释本发明实施例，而非对本发明实施例的限定。另外还需要说明的是，为了便于描述，附图中仅示出了与本发明实施例相关的部分而非全部内容。
第一实施例：
图1是本发明第一实施例中提供的选择检测区域的方法的实现流程图，该方法可以由选择检测区域的装置来执行。如图1所示，该实现流程包括：
步骤11、将待识别的器官组织信息划分为多个检测子区域，并计算各检测子区域中器官组织信息的特征值。
其中，待识别的器官组织信息可以包括器官组织的一维、二维或三维超声图像，也可以包括器官组织的一维、二维或三维超声信号，如器官组织信息可以为器官组织的A型超声信号、器官组织的M型超声信号、器官组织的B型超声图像、器官组织的CT图像或器官组织的MRI图像。其中，所述器官组织信息的特征值可以为器官组织信息的均值或器官组织信息的标准差等。
步骤12、根据待识别的器官组织信息，确定所述器官组织的边界区域。
可以依据步骤11中计算得到的各检测子区域中器官组织信息的特征值，确定所述器官组织的边界区域，也可以采用图像处理技术或信号处理技术根据所述器官组织信息对应的器官组织的特征及器官组织边界的特征，识别出所述器官组织信息中的器官组织边界区域。
如，在所述器官组织信息为器官组织的一维超声信号或器官组织的二维超声图像时，根据所述检测子区域中器官组织信息的特征值确定所述器官组织边界区域；在所述器官组织信息为器官组织的三维超声图像时，根据器官组织的特征及器官组织边界的特征，识别出所述器官组织信息中的 器官组织边界区域。
步骤13、根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域。
其中，所述预设的特征值条件可以为：距所述器官组织边界区域的距离在预设深度范围内。即，可以将距所述器官组织边界区域预设深度范围内的器官组织信息确定为器官组织信息的检测区域。其中，所述预设深度范围可以为2.6-6.5cm。
其中，若所述器官组织信息为器官组织的一维、二维或三维图像或信号，所述预设的特征值条件可以为：每个检测子区域内图像或信号的强度值对应的均值和标准差均满足预设范围。
例如，若所述器官组织信息为器官组织的一维超声信号，根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域，可以包括：若器官组织边界区域以内的多个连续的检测子区域中，每个检测子区域内超声信号的强度值对应的标准差均小于标准差阈值，则将该多个连续的检测子区域确定为所述器官组织检测区域。
例如，若所述器官组织信息为器官组织的二维超声图像或器官组织的三维图像，根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域，可以包括：若器官组织边界区域以内的多个连续的检测子区域中，每个检测子区域内图像的强度值对应的均值均小于均值阈值，且每个检测子区域内图像的强度值对应的标准差均小于标准差阈值，则将该多个连续的检测子区域确定为所述器官组织检测区域。
需要说明的是，均值阈值可以为各检测子区域内超声信号或图像的最大强度值的20％，标准差阈值可以为各检测子区域内超声信号或图像的最大强度值的5％。例如，肝脏组织中检测子区域内CT图像的强度值范围为0-300HU(Hounsfield unit，亨氏)，其均值阈值可以为60HU，其标准差阈 值可以为15HU。
其中，根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域之后，还可以包括：计算所述器官组织检测区域内器官组织的弹性值。即计算确定的器官组织检测区域内器官组织的弹性值，以实现对器官组织的超声检测。
本发明笫一实施例中提供的选择检测区域的方法，将器官组织信息划分为多个检测子区域，并计算各检测子区域中器官组织信息的特征值；根据器官组织信息确定所述器官组织边界区域，并根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域，即，该方法能够自动地选择检测区域。由于本发明第一实施例中提供的选择检测区域的方法中，所述器官组织信息不同时，确定的检测区域不同，即本发明第一实施例中，能够根据不同个体的器官组织信息的特征，自动调整检测区域的位置和大小。
第二实施例：
图2是本发明第二实施例中提供的选择检测区域的方法的实现流程图，该方法适用于器官组织的一维超声信号。图3是本发明第二实施例中的基于器官组织的M型超声信号的选择检测区域的效果图；图4是本发明第二实施例中器官组织的定量弹性模量的示意图。结合图2-图4，该方法包括：
步骤21、将所述器官组织的超声信号划分为多个检测子区域Si。
所述器官组织的一维超声信号可以为器官组织的A型超声信号或器官组织的M型超声信号。假设一条超声信号包含n个采样点，其对应的器官组织的超声信号的扫描深度为d(单位：mm)，则每1mm深度包含n/d个点。将n个采样点划分为多段检测子区域Si，检测子区域Si对应的扫描深度为di，其中，i取整数，扫描深度di可以为检测子区域Si的深度均值或端值，此处取端值。
例如，以z为间距将n个采样点划分为多段检测子区域Si，由于在超声成像中，图像最底部(即对应扫描深度最深处)一般不包含检测目标，因此可以忽略图像最底部的信息，此时，i＝1,2,…,[d/z]-1，z为检测子区域的区间长度(单位：mm)，[]是向上取整运算。此时，每段检测子区域中分别包含[zn/d]个采样点。举例而言，当超声信号的扫描深度d为20mm，间距z取3mm时，将n个采样点划分为[d/z]-1＝6段检测子区域S1～S6，其中S1对应0～3mm区间，S2对应3～6mm区间，…，S6对应15～18mm区间，图像最底部(即对应18～20mm区间)由于通常不包含检测目标因此被忽略。
步骤22、计算每个检测子区域Si中的器官组织的超声信号Ri的Nakagami分布值mi。
其中，Nakagami统计模型是超声组织定征技术的一种。具体的，依据如下公式，计算每个检测子区域Si内的器官组织的图像对应的超声信号Ri的Nakagami分布值mi，
Figure PCTCN2015081817-appb-000001
其中，Nakagami分布的概率密度函数为：
Figure PCTCN2015081817-appb-000002
其中，E(.)为均值函数，Г(.)表示伽玛函数，Ω＝E(r2)，U(.)表示单位阶跃函数，m是Nakagami分布值，r是概率分布函数f(r)的因变量，r≥0，m≥0；对于每个检测子区域Si而言，mi是Si区域内的m值，Ri是超声信号的包络值。
m值在(0,1)范围内时，器官组织的一维超声信号服从前瑞利(pre-Rayleigh)分布；m值等于1时，一维超声回波信号服从瑞利(Rayleigh)分布；m值大于1时，一维超声回波信号服从后瑞利(post-Rayleigh)分布。
步骤23、依据如下公式计算每个检测子区域Si的权重Wi，并将最大权 重值对应的检测子区域确定为器官组织边界区域：
Figure PCTCN2015081817-appb-000003
其中，di为检测子区域Si对应的扫描深度，同样可以取检测子区域Si的深度均值或端值，遍历每个检测子区域的权重Wi，选择出最大权重值对应的检测子区域，作为器官组织边界区域。
步骤24、若器官组织边界区域以内的多个连续的检测子区域中，每个检测子区域内一维超声信号的强度值对应的标准差均小于标准差阈值，则将该多个连续的检测子区域确定为所述器官组织检测区域。
计算所述器官组织边界区域以内的每个检测子区域Si内超声信号Ri的强度值对应的标准差SDi，且遍历器官组织边界区域以内的每个检测子区域，若从某个检测子区域开始，多个连续的检测子区域中，每个检测子区域内的一维超声信号的强度值对应的标准差均小于标准差阈值，那么就将该多个连续的检测子区域确定为器官组织检测区域，即完成器官组织检测区域的自动选择。
如图4，定量弹性信息可以包括弹性测量装置测得的步骤23中确定的检测区域内的器官组织的定量弹性信息的数值，通常以kPa为单位。其中，纵轴表示扫描深度，横轴表示时间。定量弹性信息还可以包括瞬时弹性成像过程中，瞬时振动随着时间沿深度传播的轨迹图像。该图中还包含指示瞬时振动传播的线段AB。另外，依据图4所示的器官组织的定量弹性信息，可以计算出瞬时振动产生的剪切波在指示标志所指区域的传播速度，从而计算出器官组织的弹性模量。
本发明第二实施例中提供的选择检测区域的方法，能够通过器官组织的A超或M超的超声信号，自动地选择器官组织检测区域。另外，由于本算法的复杂度低，因而具有较高的器官组织边界的识别效率，从而能够实现器官组织边界的实时自动定位。
第三实施例：
图5是本发明第三实施例中提供的选择检测区域的方法的实现流程图，该方法适用于器官组织的超声信号。图6是本发明第三实施例中的基于器官组织的B型超声信号的选择检测区域的效果图；图7是本发明第三实施例中器官组织的定量弹性模量的示意图。结合图5-图7，该方法包括：
步骤31、将所述器官组织的二维超声图像划分为多个矩形检测子区域Rij，i、j为自然数，分别用于表示各检测子区域的行、列序号。
所述器官组织的二维超声图像可以为器官组织的B型超声图像。假设一幅B超图像的大小为w*h，其中w为器官组织的二维超声图像的宽度，h为器官组织的二维超声图像的高度(w和h的单位均是像素)，对应的扫描深度为d(单位：mm)，则在深度方向上的一条扫描线上，1mm深度包含h/d个像素点。将大小为w*h的B超图像划分为多个矩形检测子区域Rij。
例如，以z为边长将大小为w*h的B超图像划分为多个正方形检测子区域Rij，与第一实施例类似，由于在超声成像中，图像最底部(即对应扫描深度最深处)以及宽度方向最边缘处一般不包含检测目标，因此可以忽略图像最底部及宽度方向最边缘处的信息，此时i＝1,2,…,
Figure PCTCN2015081817-appb-000004
Figure PCTCN2015081817-appb-000005
其中z为正方形检测子区域的边长(单位：mm)。[]为向上取整运算。此时，每个正方形检测子区域Rij的宽度和高度均为[zh/d]个像素。
步骤32、计算每个检测子区域Rij的权重Wij，并将最大权重值对应的检测子区域确定为器官组织边界区域。此处，为了减少运算量，可以仅计算一半数量的检测子区域的权重值，例如可将二维超声图像沿中线一分为二，仅计算中线以上的半幅二维超声图像中的各检测子区域Rkj的权重Wkj(k＝imax/2)来找到中线以上的边界子区域，再把这个边界子区域沿宽度方向(侧向)延伸即可得到整个边界区域。其中，权重Wkj可依据如下公式计 算：
Figure PCTCN2015081817-appb-000006
其中，Mkj为检测子区域Rkj中器官组织的二维超声图像的灰度均值，SDkj为检测子区域Rkj中器官组织的二维超声图像的灰度标准差，dkj为检测子区域Rkj对应的扫描深度。根据k＝imax/2可知，当器官组织的二维超声图像被划分为边长为z的矩形区域时，
Figure PCTCN2015081817-appb-000007
且k取整数，imax为i取值范围内的最大值。
例如，由于肝包膜区在B超图像上呈现为均匀一致的高回声，因此器官边界区域的灰度均值较大；另外，由于肝包膜区域在B型超声图像上具有均匀一致性，故灰度标准差较小。为避免凸阵探头扫描时扇形B超图像两侧的黑色背景区域，从位于B超图像中线处的检测子区域中搜索，若检测子区域Rk1为一系列检测区域Rkj中权重最大的一个，则将该检测子区域Rk1确定为肝脏组织的边界区域。
步骤33、若所述器官组织边界区域以内的多个连续的检测子区域中，每个检测子区域内图像的强度值对应的均值均小于均值阈值，且每个检测子区域内图像的强度值的标准差均小于标准差阈值，则将该多个连续的检测子区域确定为所述器官组织检测区域。
若从所述器官组织边界区域以内的某一检测子区域开始，多个连续的检测子区域中，每个检测子区域内图像的强度值对应的均值均小于均值阈值，且每个检测子区域内图像的强度值的标准差均小于标准差阈值，那么就将该多个连续的检测子区域确定为检测区域，即完成检测区域的自动选择。
如图7所示，定量弹性信息包括弹性测量获得的器官组织结构图中检测区域内的器官组织的定量弹性信息的数值(以kPa为单位)。弹性模量信 息包括检测区域中器官组织结构的弹性模量分布图。其中，弹性模量分布图可以用彩色编码，不同颜色表示不同的弹性模量；也可以用灰度或者其他编码形式表示弹性模量的分布，即，可以用彩色编码、灰度或其他编码形式表示检测子区域内器官组织的二维超声图像的强度值。对应的，弹性模量分布图还包括一个表示弹性模量编码的尺度图。
本发明第三实施例中提供的选择检测区域的方法，能够通过器官组织的B型超声的图像，自动地选择器官组织检测区域。另外，由于本算法的复杂度低，因而具有较高的器官组织边界的识别效率，从而能够实现器官组织边界的实时自动定位。
第四实施例：
图8是本发明第四实施例中提供的选择检测区域的方法的实现流程图，该方法适用于器官组织的三维图像。图9是本发明第四实施例中的基于器官组织的三维图像边界的效果图；图10是本发明第四实施例中的基于器官组织的三维图像的选择检测区域的效果图；图11是本发明第四实施例中器官组织的定量弹性模量的示意图。结合图8-图11，该方法包括：
步骤41、采用区域生长分割方法在所述器官组织的CT图像或所述器官组织的MRI图像中提取皮肤的二值图像和骨骼的二值图像。
首先，提取皮肤的二值图像。以图像坐标为(0,0)的像素为种子点，利用区域生长分割方法提取皮肤的二值图像，其中空气的CT值对应的区域生长的准则是[-1024，-500]HU(Hounsfield unit，亨氏)。
其次，提取骨骼的二值图像，包括椎骨的二值图像和肋骨的二值图像。对整幅图像进行阈值范围为[350，1024]HU的阈值分割，提取出骨骼的二值图像。
步骤42、计算所述骨骼的二值图像的质心，并计算所述皮肤的二值图像上距离所述质心最近的点。
计算骨骼的二值图像的质心PC。由于肋骨一般沿椎骨左右对称，并且椎骨在骨骼图像中比重较大，因此骨骼图像的质心即为椎骨的质心PC。
以椎骨质心PC为起点，搜寻皮肤的二值图像上距离所述质心PC最近的点，记为PN。
步骤43、依据所述质心和距离所述质心最近的点，将所述器官组织信息划分为四个象限。
利用质心PC和距离质心最近的点PN将CT图像划分为四个象限，即，将质心PC和距离质心最近的点PN这两个点所在的直线作为纵坐标轴，经过质心PC且与纵坐标轴垂直的直线作为横坐标轴。以器官组织为肝脏组织为例，肝脏的大部分区域位于第二象限内。
步骤44、拟合第二象限内的各个肋骨点，得到肋骨拟合曲线。
以B样条曲线或皮肤曲线拟合第二象限内的肋骨各点，得到肋骨拟合曲线。
步骤45、将所述肋骨拟合曲线朝向第一象限移动预设值，作为边界曲线，且将所述边界曲线与所述肋骨拟合曲线之间的区域确定为器官组织边界区域。
由于肋骨曲线接近于肝脏包膜，将肋骨曲线向内移动预设值，作为边界曲线，将边界曲线与肋骨拟合曲线之间的区域确定为器官组织边界区域。
其中，所述预设值可以为5mm。
步骤46、将所述器官组织边界区域所围的区域确定为器官组织区域。
步骤47、将所述器官组织区域划分为多个检测子区域。
步骤48、计算每个检测子区域中器官组织的二维超声图像的强度值对应的均值和强度值对应的标准差。
步骤49、若器官组织边界区域所围的区域内多个连续的检测子区域中，每个检测子区域内图像的强度值对应的均值均小于均值阈值，且每个检测 子区域内图像的强度值对应的标准差均小于标准差阈值，则将该多个连续的检测子区域确定为所述器官组织检测区域。
从肝脏组织的边界开始，搜寻肝脏内部各个检测子区域，若连续的多个检测子区域中，每个检测子区域内图像的强度值对应的均值均小于均值阈值，且每个检测子区域内图像的强度值的标准差均小于标准差阈值，则将该多个连续的检测子区域确定为所述器官组织检测区域，即完成器官组织的自动选择。
如图11，定量弹性信息包括弹性测量获得的组织结构图中内的组织的定量弹性信息的数值(以kPa为单位)。其中弹性模量信息也包括组织结构图中指示标志所表示区域的组织的弹性模量分布图。该图可以用彩色编码，不同颜色表示不同的弹性模量。也可以用灰度或者其他编码形式表示弹性模量的分布。对应的，上述弹性模量分布图还包括一个表示弹性模量编码的尺度图。
需要说明的是，利用CT图像或MRI图像实现器官组织边界区域的自动识别及检测区域位置及大小的自动调整时。自动选择的检测区域可以为三维几何体。
以CT序列图像为例，对每一帧CT图像，采用本实施例中提供的选择检测区域的方法，自动选择出该帧器官组织的图像的检测区域。再利用每一帧图像分别对应的各个检测区域，重新构建出一个三维几何体，即为三维的检测区域。利用弹性检测探头检测出每一帧CT图像的二维检测区域内的弹性信息，再重建出器官组织的三维弹性分布，从而得到器官组织的三维定量弹性信息。
本发明笫四实施例中提供的选择检测区域的方法，能够通过器官组织的三维图像，例如，器官组织的CT图像或器官组织的MRI图像，自动地选择器官组织检测区域。另外，由于本算法的复杂度低，因而具有较高的 器官组织边界的识别效率，从而能够实现器官组织边界的实时自动定位。
第五实施例：
图12是本发明第五实施例中提供的选择检测区域的装置的结构示意图，如图12所示，本实施例所述的选择检测区域的装置可以包括：区域划分单元51，用于将待识别的器官组织信息划分为多个检测子区域；特征值计算单元52，用于计算检测子区域中器官组织信息的特征值；边界区域识别单元53，用于根据待识别的器官组织信息，确定所述器官组织边界区域；检测区域确定单元54，用于根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域。
其中，该装置还可以包括：弹性值计算单元，用于计算所述器官组织检测区域内器官组织的弹性值。
其中，所述预设的特征值条件可以为：距所述器官组织边界区域的距离在预设深度范围内。
其中，若所述器官组织信息为器官组织的一维、二维或三维图像或信号，所述预设的特征值条件可以为：每个检测子区域内图像或信号的强度值对应的均值和标准差均满足预设范围。
其中，若所述器官组织信息为器官组织的一维超声信号，边界区域识别单元53可以包括：计算子单元，用于计算每个检测子区域Si中的器官组织的一维超声信号Ri的Nakagami分布值mi；区域识别子单元，用于依据如下公式计算每个检测子区域Si的权重Wi，并将最大权重值对应的检测子区域确定为器官组织边界区域：
Figure PCTCN2015081817-appb-000008
其中，di为检测子区域Si对应的扫描深度。
其中，若所述器官组织信息为器官组织的二维超声图像，区域划分单元51具体可以用于：依据如下公式计算每个检测子区域Rkj的权重Wkj，并 将最大权重值对应的检测子区域确定为器官组织边界区域：
Figure PCTCN2015081817-appb-000009
其中，Mkj为检测子区域Rkj中器官组织的二维超声图像的灰度均值，SDkj为检测子区域Rkj中器官组织的二维超声图像的灰度标准差，dkj为检测子区域Rkj对应的扫描深度，k＝imax/2。
将所述器官组织的二维超声图像划分为多个矩形检测子区域Rij；边界区域识别单元53具体可以用于：依据如下公式计算每个检测子区域Rkj的权重Wkj，并将最大权重值对应的检测子区域确定为器官组织边界区域：
Figure PCTCN2015081817-appb-000010
其中，Mkj为检测子区域Rkj中器官组织的二维超声图像的灰度均值，SDkj为检测子区域中器官组织的二维超声图像的灰度标准差，dkj为检测子区域对应的扫描深度，k＝imax/2。
其中，若所述器官组织信息为器官组织的CT图像或器官组织的MRI图像，所述边界区域识别单元53具体可以包括：二值图像获取子单元，用于采用图像分割方法在所述器官组织的CT图像或所述器官组织的MRI图像中提取皮肤的二值图像和骨骼的二值图像；特征点确定子单元，用于计算所述骨骼的二值图像的质心，并计算所述皮肤的二值图像上距离所述质心最近的点；图像划分子单元，用于依据所述质心和距离所述质心最近的点，将所述器官组织的图像划分为四个象限；曲线拟合子单元，用于拟合第二象限内的各个肋骨点，得到肋骨拟合曲线；边界区域确定子单元，用于将所述肋骨拟合曲线朝向第一象限移动预设值，作为边界区域曲线，且将所述边界区域曲线与所述肋骨拟合曲线之间的区域确定为器官组织边界区域。
本发明第五实施例中提供的选择检测区域的装置，将待识别的器官组 织信息划分为多个检测子区域，并计算检测子区域中器官组织信息的特征值，根据待识别的器官组织信息确定器官组织边界区域，且根据确定的器官组织边界区域和预设的特征值条件确定器官组织检测区域。该装置中器官组织信息不相同时，检测区域的位置和大小不相同，即该装置能够调整检测区域的位置和大小。
第六实施例：
图13是本发明第六实施例中提供的弹性检测系统的结构示意图，如图13所示，本实施例所述的弹性检测系统的可以包括信息获取装置61、弹性成像装置63、探头设置装置64、处理装置65和显示装置66，还包括本发明第五实施例中提供的选择检测区域的装置62，其中，所述信息获取装置61，用于获取待识别的器官组织信息；所述探头设置装置64，用于调整弹性成像装置中探头的位置，使所述探头的检测范围包含所述选择检测区域的装置确定的检测区域；所述弹性成像装置63，用于获取器官组织的弹性信息；所述处理装置65，用于对弹性成像装置获取的弹性信息进行处理，得到所述检测区域中的弹性信息；所述显示装置66，用于显示所述检测区域中的弹性信息。
本发明第六实施例中提供的弹性检测系统，能够自动识别器官组织边界区域，并自动调整检测区域的位置和大小，从而节约弹性检测时间、减少不同操作者之间及同一操作者在不同操作下的操作差异，实现准确、快捷、重复性高的器官组织的弹性检测。
上所述仅为本发明实施例的优选实施例，并不用于限制本发明实施例，对于本领域技术人员而言，本发明实施例可以有各种改动和变化。凡在本发明实施例的精神和原理之内所作的任何修改、等同替换、改进等，均应包含在本发明实施例的保护范围之内。

Claims (15)

  1. 一种选择检测区域的方法，其特征在于，包括：
    将待识别的器官组织信息划分为多个检测子区域，并计算各检测子区域中器官组织信息的特征值；
    根据待识别的器官组织信息，确定器官组织边界区域；
    根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域。
  2. 根据权利要求1所述的方法，其特征在于，根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域之后，还包括：
    计算所述器官组织检测区域内器官组织的弹性值。
  3. 根据权利要求1所述的方法，其特征在于，
    所述预设的特征值条件为：距所述器官组织边界区域距离在预设深度范围内。
  4. 根据权利要求1所述的方法，其特征在于，若所述器官组织信息为器官组织的一维、二维或三维图像或信号，
    所述预设的特征值条件为：每个检测子区域内图像或信号的强度值对应的均值和标准差均满足预设范围。
  5. 根据权利要求1-4任一项所述的方法，其特征在于，若所述器官组织信息为器官组织的一维超声信号，
    根据待识别的器官组织信息，确定所述器官组织边界区域，包括：
    计算每个检测子区域Si中的器官组织的一维超声信号Ri的Nakagami分布值mi；
    依据如下公式计算每个检测子区域Si的权重Wi，并将最大权重值对应的检测子区域确定为器官组织边界区域：
    Figure PCTCN2015081817-appb-100001
    其中，di为检测子区域Si对应的扫描深度，i为自然数。
  6. 根据权利要求1-4任一项所述的方法，其特征在于，若所述器官组织信息为器官组织的二维超声图像，
    将待识别的器官组织信息划分为多个检测子区域包括：将所述器官组织的二维超声图像划分为多个矩形检测子区域Rij，i、j为自然数；
    根据待识别的器官组织信息，确定所述器官组织边界区域包括：
    依据如下公式计算每个检测子区域Rkj的权重Wkj，并将最大权重值对应的检测子区域确定为器官组织边界区域：
    Figure PCTCN2015081817-appb-100002
    其中，Mkj为检测子区域Rkj中器官组织的二维超声图像的灰度均值，SDkj为检测子区域Rkj中器官组织的二维超声图像的灰度标准差，dkj为检测子区域Rkj对应的扫描深度，k＝imax/2且k为自然数，imax为i取值范围内的最大值。
  7. 根据权利要求1-4任一项所述的方法，其特征在于，若所述器官组织信息为器官组织的CT图像或器官组织的MRI图像，
    根据待识别的器官组织信息，确定所述器官组织边界区域，包括：
    采用图像分割方法在所述器官组织的CT图像或所述器官组织的MRI图像中提取皮肤的二值图像和骨骼的二值图像；
    计算所述骨骼的二值图像的质心，并计算所述皮肤的二值图像上距离所述质心最近的点；
    依据所述质心和距离所述质心最近的点，将所述器官组织的CT图像或所述器官组织的MRI图像划分为四个象限；
    拟合第二象限内的各个肋骨点，得到肋骨拟合曲线；
    将所述肋骨拟合曲线朝向第一象限移动预设值，作为边界区域曲线，且将所述边界区域曲线与所述肋骨拟合曲线之间的区域确定为器官组织边界区域。
  8. 一种选择检测区域的装置，其特征在于，包括：
    区域划分单元，用于将待识别的器官组织信息划分为多个检测子区域；
    特征值计算单元，用于计算检测子区域中器官组织信息的特征值；
    边界区域识别单元，用于根据待识别的器官组织信息，确定器官组织边界区域；
    检测区域确定单元，用于根据所述器官组织边界区域，及预设的特征值条件确定器官组织检测区域。
  9. 根据权利要求8所述的装置，其特征在于，还包括：
    弹性值计算单元，用于计算所述器官组织检测区域内器官组织的弹性值。
  10. 根据权利要求8所述的装置，其特征在于，
    所述预设的特征值条件为：距所述器官组织边界区域距离在预设深度范围内。
  11. 根据权利要求8所述的装置，其特征在于，若所述器官组织信息为器官组织的一维、二维或三维图像或信号，
    所述预设的特征值条件为：每个检测子区域内图像或信号的强度值对应的均值和标准差均满足预设范围。
  12. 根据权利要求8-11任一项所述的装置，其特征在于，若所述器官组织信息为器官组织的一维超声信号，
    边界区域识别单元包括：
    计算子单元，用于计算每个检测子区域Si中的器官组织的一维超声信号Ri的Nakagami分布值mi；
    区域识别子单元，用于依据如下公式计算每个检测子区域Si的权重Wi，并将最大权重值对应的检测子区域确定为器官组织边界区域：
    Figure PCTCN2015081817-appb-100003
    其中，di为检测子区域Si对应的扫描深度，i为自然数。
  13. 根据权利要求8-11任一项所述的装置，其特征在于，若所述器官组织信息为器官组织的二维超声图像，
    区域划分单元具体用于：将所述器官组织的二维超声图像划分为多个矩形检测子区域Rij，i、j为自然数；
    边界区域识别单元具体用于：
    依据如下公式计算每个检测子区域Rkj的权重Wkj，并将最大权重值对应的检测子区域确定为器官组织边界区域：
    Figure PCTCN2015081817-appb-100004
    其中，Mkj为检测子区域Rkj中器官组织的二维超声图像的灰度均值，SDkj为检测子区域Rkj中器官组织的二维超声图像的灰度标准差，dkj为检测子区域Rkj对应的扫描深度，k＝imax/2且k为自然数，imax为i取值范围内的最大值。
  14. 根据权利要求8-11任一项所述的装置，其特征在于，若所述器官组织信息为器官组织的CT图像或器官组织的MRI图像，
    所述边界区域识别单元具体包括：
    二值图像获取子单元，用于采用图像分割方法在所述器官组织的CT图像或所述器官组织的MRI图像中提取皮肤的二值图像和骨骼的二值图像；
    特征点确定子单元，用于计算所述骨骼的二值图像的质心，并计算所述皮肤的二值图像上距离所述质心最近的点；
    图像划分子单元，用于依据所述质心和距离所述质心最近的点，将所 述器官组织的CT图像或所述器官组织的MRI图像划分为四个象限；
    曲线拟合子单元，用于拟合第二象限内的各个肋骨点，得到肋骨拟合曲线；
    边界区域确定子单元，用于将所述肋骨拟合曲线朝向第一象限移动预设值，作为边界区域曲线，且将所述边界区域曲线与所述肋骨拟合曲线之间的区域确定为器官组织边界区域。
  15. 一种弹性检测系统，其特征在于，包括信息获取装置、弹性成像装置、探头设置装置和显示装置，还包括权利要求8-14任一项所述的选择检测区域的装置，其中，
    所述信息获取装置，用于获取待识别的器官组织信息；
    所述探头设置装置，用于调整弹性成像装置中探头的位置，使所述探头的检测范围包含所述选择检测区域的装置确定的检测区域；
    所述弹性成像装置，用于获取器官组织的弹性信息；
    所述显示装置，用于显示所述检测区域中的弹性信息。
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JP2017536856A (ja) 2017-12-14
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US20170202540A1 (en) 2017-07-20
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KR101913976B1 (ko) 2018-10-31
AU2015335554B2 (en) 2018-03-22
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EP3210541A1 (en) 2017-08-30
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