CN112807024B - Ultrasonic image quantitative evaluation system - Google Patents

Ultrasonic image quantitative evaluation system Download PDF

Info

Publication number
CN112807024B
CN112807024B CN202110117792.0A CN202110117792A CN112807024B CN 112807024 B CN112807024 B CN 112807024B CN 202110117792 A CN202110117792 A CN 202110117792A CN 112807024 B CN112807024 B CN 112807024B
Authority
CN
China
Prior art keywords
cartilage
ultrasonic
joint
image
wall
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110117792.0A
Other languages
Chinese (zh)
Other versions
CN112807024A (en
Inventor
罗建文
王媛媛
何琼
高孟泽
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tsinghua University
Original Assignee
Tsinghua University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tsinghua University filed Critical Tsinghua University
Priority to CN202110117792.0A priority Critical patent/CN112807024B/en
Publication of CN112807024A publication Critical patent/CN112807024A/en
Application granted granted Critical
Publication of CN112807024B publication Critical patent/CN112807024B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0875—Detecting organic movements or changes, e.g. tumours, cysts, swellings for diagnosis of bone
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • 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
    • G06T2207/10132—Ultrasound 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30008—Bone

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Radiology & Medical Imaging (AREA)
  • Medical Informatics (AREA)
  • Veterinary Medicine (AREA)
  • Biophysics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Pathology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Public Health (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Orthopedic Medicine & Surgery (AREA)
  • Rheumatology (AREA)
  • Ultra Sonic Daignosis Equipment (AREA)

Abstract

The invention relates to an ultrasonic image quantitative evaluation system, which comprises: the image preprocessing module is used for preprocessing the acquired joint ultrasonic image; the image segmentation module is used for carrying out image segmentation on the joint ultrasonic image; the quantitative analysis index extraction module is used for carrying out quantitative analysis index extraction on the joint ultrasonic image; and the parameter joint analysis module is used for carrying out multi-parameter joint analysis on the joint ultrasonic image. The invention automatically segments the front and back walls and the internal area of the cartilage on an ultrasonic image, quantitatively analyzes the segmented interested area, extracts quantitative analysis indexes related to ultrasonic echo intensity, sound attenuation, cartilage thickness, scatterometry distribution and the like, and comprehensively analyzes various indexes by using a multivariate analysis method, thereby realizing the noninvasive and quantitative evaluation of the articular cartilage lesion degree.

Description

Ultrasonic image quantitative evaluation system
Technical Field
The invention relates to an ultrasonic image quantitative evaluation system, in particular to an ultrasonic image quantitative evaluation system which can be used for evaluating articular cartilage lesions.
Background
Osteoarthritis of various etiologies is the most common chronic degenerative disease of the joints in the elderly. Early identification of osteoarthritis and accurate staging of joint degeneration facilitates timely and appropriate intervention in the condition, thereby controlling disease progression and significantly improving prognosis. Clinically, Conventional Radiography (CR) is often used to assess bone joint structural changes, but it does not reveal soft tissue lesions. Magnetic Resonance Imaging (MRI) can be used to detect articular cartilage, synovium, meniscus and soft tissue lesions, but it is expensive, long scanning time and insensitive to cartilage early lesions. The ultrasonic diagnosis has the advantages of real time, portability, easy use, low price and the like, and can display structural changes of bones and cartilages and inflammatory changes in joints, so the ultrasonic diagnosis is suitable for screening osteoarthritis in a large group of people. However, the existing clinical ultrasonic diagnosis has the problems of strong subjectivity, incapability of giving quantitative results and the like.
Based on gold standard arthroscopy, the degree of joint pathology can be divided into three degrees: at the temperature of I, the probe can touch the articular cartilage to soften, and a small amount of surface fibrosis, closed cartilage separation and vacuolar change can be observed; at II degree, a small amount of articular cartilage fiber bundle-like change can be observed, and the articular cartilage fiber bundle-like change is crab meat-like appearance; at III, it was observed that cartilage necrosis was lost, subchondral bone was exposed and "ivory" appeared. Arthroscopy is an invasive examination technique, however, and is not suitable for routine screening of osteoarthritis.
Currently, studies on the evaluation of cartilage degradation using a quantitative ultrasound method are mainly applied to ex vivo experiments or in vivo arthroscopy. In these studies, the axial resolution is improved by using a high-frequency probe, and quantitative indicators related to the cartilage surface acoustic reflection characteristics, roughness and the like are extracted from the ultrasonic radio-frequency signals to characterize the change of the cartilage surface microstructure caused by a lesion, or the indicators related to the cartilage backscattering characteristics, acoustic attenuation and the like are extracted by performing power spectrum analysis on the ultrasonic radio-frequency signals. However, in clinical ultrasound examination, due to the tradeoff between probe frequency and imaging depth, the high-frequency probe used in the ex vivo experiment or arthroscopy cannot detect the depth of the articular cartilage, and thus is not suitable for clinical examination of the articular cartilage. Also, most of the currently used ultrasound machines in clinical use are not capable of providing ultrasound radio frequency data. Therefore, the index reflecting the change of the cartilage surface microstructure commonly used in the in vitro experiment and arthroscopy and the index extracted by the ultrasonic radio frequency data spectrum analysis cannot be applied to clinical ultrasonic examination.
Therefore, it is urgently needed to provide a non-invasive/quantitative articular cartilage lesion assessment method for clinical application in lesion screening and grading diagnosis of a large-scale population.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide an ultrasound image quantitative evaluation system, which performs quantitative analysis on an ultrasound image to extract an index sensitive to cartilage lesions, and performs non-invasive and quantitative evaluation on the cartilage lesion degree by comprehensively analyzing various indexes through multi-parameter joint analysis.
In order to achieve the purpose, the invention adopts the following technical scheme: a method of quantitative assessment of ultrasound images, comprising:
firstly, preprocessing the acquired joint ultrasonic image; secondly, carrying out image segmentation on the joint ultrasonic image; thirdly, carrying out quantitative analysis index extraction on the joint ultrasonic image; and fourthly, carrying out multi-parameter joint analysis on the joint ultrasonic image.
The method for quantitatively evaluating an ultrasound image preferably includes the step of preprocessing the acquired ultrasound image of the joint, including: and carrying out preprocessing of interpolation, normalization and attenuation compensation on the acquired joint ultrasonic image.
The method for quantitatively evaluating an ultrasound image preferably includes the step of performing image segmentation on the joint ultrasound image, including: regions of interest contained within the anterior cartilage wall, the posterior cartilage wall, and the interior of the cartilage are segmented from the ultrasound images.
Preferably, the step of performing quantitative analysis index extraction on the joint ultrasound image includes: according to the B-ultrasonic representation of the articular cartilage lesion, quantitative analysis indexes related to ultrasonic echo intensity, sound attenuation, cartilage thickness and scatterer statistical distribution are extracted from an interested region obtained after preprocessing and image segmentation.
The method for quantitatively evaluating an ultrasound image preferably includes the step of performing multi-parameter joint analysis on an ultrasound image of a joint, including: and (4) performing comprehensive analysis on each quantitative analysis index extracted in the third step by using a joint analysis or machine learning method to realize noninvasive quantitative grading of the cartilage lesions and finally obtain grading results of the cartilage lesions.
The method for quantitatively evaluating the ultrasonic image preferably performs preprocessing of interpolation, normalization and attenuation compensation on the acquired joint ultrasonic image, and comprises the following steps of: 1) interpolating the joint ultrasonic images in the depth direction to enable the joint ultrasonic images acquired at different depths to have the same pixel size; 2) normalizing each frame of joint ultrasonic image by using the maximum intensity value of each frame, and eliminating the influence of gain on the ultrasonic echo intensity; meanwhile, the same ultrasonic system is used, under the same imaging configuration, a joint ultrasonic image of a uniform simulated body is acquired, and a signal of the cartilage with the corresponding depth is normalized by a simulated body signal to correct the influence of the depth on the ultrasonic echo intensity; 3) the attenuation compensation method is used for compensating the sound attenuation caused by soft tissue covered by the upper layer of the cartilage in the joint ultrasonic image.
The quantitative evaluation method of the ultrasonic image preferably uses manual or image segmentation algorithm based on region, boundary, mathematical morphology, wavelet theory or neural network to segment the region of interest contained in the cartilage anterior wall, cartilage posterior wall and cartilage interior from the ultrasonic image of the joint.
Preferably, the method for quantitatively evaluating an ultrasound image extracts quantitative analysis indexes related to ultrasound echo intensity, acoustic attenuation, cartilage thickness and scatterometer statistical distribution from an area of interest obtained after preprocessing and image segmentation, and includes the following steps: 1) calculating the ultrasonic echo intensity; 2) calculating the roughness of the surface of the cartilage; 3) calculating the sound attenuation deceleration; 4) calculating the thickness of the cartilage; 5) and calculating the statistic distribution of scatterers in the cartilage.
The method for quantitatively evaluating an ultrasound image preferably includes, when performing step 1), the following steps:
calculating the average value and the standard deviation of the ultrasonic echo intensities of the cartilage anterior wall and the cartilage posterior wall to reflect the average echo intensity and the uniformity of the cartilage anterior wall and the cartilage posterior wall, wherein the calculation formula is as follows:
Figure GDA0003575047410000031
Figure GDA0003575047410000032
Figure GDA0003575047410000033
Figure GDA0003575047410000034
in the formula, RawAnd SVRawMean echo intensity and homogeneity of the cartilage anterior wall are respectively represented; rpwAnd SVRpwMean echo intensity and homogeneity of the cartilage posterior wall are respectively represented;
Figure GDA0003575047410000035
and
Figure GDA0003575047410000036
respectively representing the ultrasonic echo intensity at the ith position of the cartilage anterior wall and the cartilage posterior wall; m represents the length component of cartilage along the transverse direction;
secondly, calculating the average value and the standard deviation of the ultrasonic echo intensity inside the cartilage to reflect the ultrasonic echo intensity and the uniformity inside the cartilage, wherein the calculation formula is as follows:
Figure GDA0003575047410000037
Figure GDA0003575047410000038
in the formula, RcartilageAnd SVRcartilageRespectively representing the ultrasonic echo intensity and the uniformity of the cartilage interior;
Figure GDA0003575047410000039
representing the ultrasonic echo intensity at the ith position in the cartilage;
and thirdly, calculating the ratio of the ultrasonic echo intensity of the cartilage front wall and the cartilage back wall to the ultrasonic echo intensity inside the cartilage to reflect the echo contrast of the cartilage front wall and the cartilage back wall to the cartilage inside, wherein the calculation formula is as follows:
Figure GDA0003575047410000041
Figure GDA0003575047410000042
in the formula, contastawRepresenting the contrast of the echoes of the anterior cartilage wall and the interior of the cartilage; contastpwIndicating the echogenic contrast of the posterior wall of the cartilage to the interior of the cartilage.
The method for quantitatively evaluating an ultrasound image preferably includes the following steps in the step 2):
according to the joint ultrasonic image, position information of the upper boundary of the cartilage is obtained, high-pass filtering is carried out on the vertical distance between each position of the upper boundary of the cartilage and the surface of the ultrasonic probe to obtain a filtered distance signal, the standard deviation of the filtered distance signal is obtained to reflect the roughness of the surface of the cartilage, and the calculation formula of the roughness is as follows:
Figure GDA0003575047410000043
in the formula, URI represents the roughness of the cartilage surface; diRepresenting the filtered range signal at the ith position;
Figure GDA0003575047410000044
represents an average value of the filtered distance signal; m represents the length component of cartilage along the transverse direction.
The method for quantitatively evaluating an ultrasound image preferably includes, when performing step 3), the following steps:
selecting an interested region with the length of n from the lower parts of the front cartilage wall and the rear cartilage wall, equally dividing the interested region into a plurality of parts, linearly fitting the ultrasonic echo intensity and the corresponding depth of each position in the interested region, and representing the echo amplitude attenuation coefficient by using the slope of a fitting straight line, thereby obtaining the echo amplitude attenuation coefficient in each section range and the whole range of the interested region contained in the front cartilage wall and the rear cartilage wall and reflecting the change of the sound attenuation characteristic related to cartilage pathological changes;
and obtaining the echo amplitude attenuation coefficients in each section range and the whole range of the region of interest contained in the cartilage in the same way.
The method for quantitatively evaluating an ultrasound image preferably includes, in the step 4), the following steps:
and calculating the vertical distances of the cartilage anterior wall and the cartilage posterior wall at different positions, wherein the average value, the standard value and the minimum value of all the vertical distances can respectively reflect the average value, the thickness uniformity and the minimum thickness of the cartilage thickness.
The method for quantitatively evaluating an ultrasound image preferably includes, when performing step 5), the following steps:
fitting scattering photon distribution parameters inside the cartilage by using an ultrasonic envelope signal according to a scattering photon statistical distribution model, wherein the Nakagami distribution model adopted by the scattering photon statistical distribution model is as follows:
Figure GDA0003575047410000051
in the formula, r is the local ultrasonic echo intensity inside the cartilage, and r is more than or equal to 0; n is a shape parameter, and n > 0; omega is a scale parameter, and omega is more than 0; Γ (n) represents a Gamma function with respect to the shape parameter n.
Due to the adoption of the technical scheme, the invention has the following advantages: the invention automatically segments the front and back walls and the internal area of the cartilage on an ultrasonic image, quantitatively analyzes the segmented interesting area, extracts quantitative analysis indexes related to ultrasonic echo intensity, sound attenuation, cartilage thickness, scatterometry distribution and the like, and comprehensively analyzes various indexes by using a multivariate analysis method, thereby realizing noninvasive and quantitative evaluation of the articular cartilage lesion degree. The method provided by the invention is also suitable for non-invasive quantitative evaluation of the lesion degree of other in-vivo tissues or organs (such as carotid plaque, thyroid, liver, kidney and the like).
Drawings
Fig. 1 is a schematic flow chart of a method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the method for quantitatively evaluating an ultrasound image provided by this embodiment includes the following steps:
firstly, preprocessing an acquired ultrasonic image:
in this embodiment, the acquired ultrasound image is a joint ultrasound image acquired in a standard format (such as DICOM format) by an ultrasound system according to a unified standard operation procedure (for example, outco measurements in surgery, oma analysis criteria), and then preprocessing such as interpolation, normalization, attenuation compensation and the like is performed on the ultrasound image, so as to reduce the influence on the echo signal intensity caused by factors such as imaging depth, gain, and acoustic attenuation of cartilage superior tissues among different examiners as much as possible, specifically:
1) carrying out depth direction interpolation on the ultrasonic images so as to enable the ultrasonic images acquired at different depths to have the same pixel size;
2) normalizing each frame of ultrasonic image by using the maximum intensity value of each frame, and eliminating the influence of gain on the ultrasonic echo intensity; meanwhile, the same ultrasonic system is used, under the same imaging configuration, ultrasonic images of uniform simulated bodies are collected, and simulated body signals are used for normalizing signals of corresponding depths of cartilage so as to correct the influence of the depths on the ultrasonic echo intensity;
3) the acoustic attenuation caused by soft tissue overlying the cartilage in the ultrasound image is compensated for using a suitable attenuation compensation method, such as an optimal power spectral shift estimation method.
Secondly, carrying out image segmentation on the ultrasonic image:
in this embodiment, regions of interest (ROIs) contained in the anterior wall, the posterior wall and the interior of the cartilage are segmented from the ultrasound image using manual or region, boundary, mathematical morphology, wavelet theory or neural network based image segmentation algorithms.
Thirdly, carrying out quantitative analysis index extraction on the ultrasonic image:
due to the limitations of the frequency of the clinical ultrasound probe and the acquisition system, quantitative analysis methods that rely on high frequency probes and radio frequency data analysis cannot be applied to image analysis obtained by clinical acquisition. Therefore, in this embodiment, according to the B-mode ultrasound representation of the articular cartilage disorder, quantitative analysis indexes related to the ultrasound echo intensity, the sound attenuation, the cartilage thickness, the scatterer statistical distribution, and the like are extracted from the ROI obtained after the preprocessing and the image segmentation, and specifically:
1) calculating the ultrasonic echo intensity:
calculating the average value and the standard deviation of the ultrasonic echo intensities of the cartilage anterior wall and the cartilage posterior wall to reflect the average echo intensity and the uniformity of the cartilage anterior wall and the cartilage posterior wall, wherein the calculation formula is as follows:
Figure GDA0003575047410000061
Figure GDA0003575047410000062
Figure GDA0003575047410000063
Figure GDA0003575047410000064
in the formula, RawAnd SVRawMean echo intensity and homogeneity of the cartilage anterior wall are respectively represented; rpwAnd SVRpwMean echo intensity and homogeneity of the cartilage posterior wall are respectively represented;
Figure GDA0003575047410000065
and
Figure GDA0003575047410000066
respectively representing the ultrasonic echo intensity at the ith position of the cartilage anterior wall and the cartilage posterior wall; m represents the length component of cartilage along the transverse direction.
Secondly, calculating the average value and the standard deviation of the ultrasonic echo intensity inside the cartilage to reflect the ultrasonic echo intensity and the uniformity inside the cartilage, wherein the calculation formula is as follows:
Figure GDA0003575047410000067
Figure GDA0003575047410000071
in the formula, RcartilageAnd SVRcartilageRespectively representing the ultrasonic echo intensity and the uniformity of the cartilage interior;
Figure GDA0003575047410000072
representing the ultrasound echo intensity at the ith location inside the cartilage.
And thirdly, calculating the ratio of the ultrasonic echo intensity of the cartilage front wall and the cartilage back wall to the ultrasonic echo intensity inside the cartilage to reflect the echo contrast of the cartilage front wall and the cartilage back wall to the cartilage inside, wherein the calculation formula is as follows:
Figure GDA0003575047410000073
Figure GDA0003575047410000074
in the formula, contastawRepresenting the contrast of the echoes of the anterior cartilage wall and the interior of the cartilage; contastpwIndicating the echogenic contrast of the posterior wall of the cartilage to the interior of the cartilage.
2) Cartilage surface roughness calculation:
according to the ultrasonic image, position information of the boundary on the cartilage is obtained, high-pass filtering is carried out on the vertical distance between each position of the boundary on the cartilage and the surface of the ultrasonic probe to obtain a filtered distance signal, and the standard deviation of the filtered distance signal is obtained to reflect the roughness of the upper wall (namely the cartilage surface) of the cartilage, wherein the calculation formula of the roughness is as follows:
Figure GDA0003575047410000075
in the formulaURI represents roughness of the cartilage surface; diRepresenting the filtered range signal at the ith position;
Figure GDA0003575047410000076
representing the average of the filtered distance signal.
3) Calculation of acoustic attenuation velocity:
selecting ROIs with the length of n (in the embodiment, n is 0.1mm to 5mm) from the lower parts of the front cartilage wall and the rear cartilage wall, equally dividing the ROIs into a plurality of parts (for example, four parts), performing linear fitting on the ultrasonic echo intensity and the corresponding depth at each position inside the ROIs, and representing the echo amplitude attenuation coefficient by using the slope of a fitting straight line, so as to obtain the echo amplitude attenuation coefficients in each section range and the whole range of the ROIs contained in the front cartilage wall and the rear cartilage wall, wherein the echo amplitude attenuation coefficients are used for reflecting the change of the sound attenuation characteristics related to cartilage lesions.
And obtaining the echo amplitude attenuation coefficient in each section range and the whole range of the ROI contained in the cartilage in the same way.
4) Cartilage thickness calculation:
the vertical distances of the cartilage anterior wall and the cartilage posterior wall at different positions are calculated by the existing method, and the average value, the standard value and the minimum value of all the vertical distances can respectively reflect the average value, the thickness uniformity and the minimum thickness of the cartilage thickness.
5) Calculating the statistic distribution of scatterers in the cartilage:
the scatterer distribution parameters inside the cartilage were fitted with the ultrasound envelope signal according to a scatterer statistical distribution model (in this example, a Nakagami distribution model is used), wherein the Nakagami distribution model is as follows:
Figure GDA0003575047410000081
wherein r is the local ultrasonic echo intensity inside the cartilage and is more than or equal to 0; n is a shape parameter, and n > 0; omega is a scale parameter, and omega is more than 0; Γ (n) represents a Gamma function with respect to the shape parameter n.
Fourthly, carrying out multi-parameter joint analysis on the ultrasonic image:
and (3) carrying out comprehensive analysis on all quantitative analysis indexes extracted in the third step by using a joint analysis or machine learning method to realize noninvasive quantitative grading of the cartilage lesions and finally obtaining grading results of the cartilage lesions (namely the cartilage lesions of I, II and III degrees).
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (11)

1. An ultrasonic image quantitative evaluation system, comprising:
an image preprocessing module for preprocessing the acquired joint ultrasonic image, including preprocessing of interpolation, normalization and attenuation compensation, specifically to the acquired joint ultrasonic image
1) Performing depth direction interpolation on the joint ultrasonic images to enable the joint ultrasonic images acquired at different depths to have the same pixel size;
2) normalizing each frame of joint ultrasonic image by using the maximum intensity value of each frame, and eliminating the influence of gain on the ultrasonic echo intensity; meanwhile, the same ultrasonic system is used, under the same imaging configuration, a joint ultrasonic image of a uniform simulated body is acquired, and a signal of the cartilage with the corresponding depth is normalized by a simulated body signal to correct the influence of the depth on the ultrasonic echo intensity;
3) compensating for the sound attenuation caused by soft tissue covered on the upper layer of the cartilage in the joint ultrasonic image by using an attenuation compensation method;
the image segmentation module is used for carrying out image segmentation on the joint ultrasonic image;
the quantitative analysis index extraction module is used for carrying out quantitative analysis index extraction on the joint ultrasonic image;
and the parameter joint analysis module is used for carrying out multi-parameter joint analysis on the joint ultrasonic image.
2. The quantitative ultrasound image evaluation system of claim 1, wherein image segmenting the ultrasound image of the joint comprises: regions of interest contained within the anterior cartilage wall, the posterior cartilage wall, and the interior of the cartilage are segmented from the ultrasound images.
3. The ultrasonic image quantitative evaluation system of claim 2, wherein the quantitative analysis index extraction of the joint ultrasonic image comprises: according to the B-ultrasonic representation of the articular cartilage lesion, quantitative analysis indexes related to ultrasonic echo intensity, sound attenuation, cartilage thickness and scatterer statistical distribution are extracted from an interested region obtained after preprocessing and image segmentation.
4. The quantitative ultrasound image evaluation system of claim 3, wherein performing a multi-parameter joint analysis on the joint ultrasound image comprises: and performing comprehensive analysis on each quantitative analysis index extracted by the quantitative analysis index extraction module by using a joint analysis or machine learning method.
5. The quantitative evaluation system of ultrasonic image according to claim 2, characterized in that the region of interest contained in the anterior cartilage wall, the posterior cartilage wall and the cartilage interior is segmented from the ultrasonic image of the joint using manual or image segmentation algorithms based on region, boundary, mathematical morphology, wavelet theory or neural network.
6. The ultrasonic image quantitative evaluation system of claim 3, wherein the extracting of quantitative analysis indexes related to ultrasonic echo intensity, acoustic attenuation, cartilage thickness and scatterometry distribution from the region of interest obtained after preprocessing and image segmentation comprises:
1) calculating the ultrasonic echo intensity;
2) calculating the roughness of the surface of the cartilage;
3) calculating the sound attenuation deceleration;
4) calculating the thickness of the cartilage;
5) and calculating the statistic distribution of scatterers in the cartilage.
7. The ultrasonic image quantitative evaluation system of claim 6, wherein the step 1) is performed by the following steps:
calculating the average value and the standard deviation of the ultrasonic echo intensities of the cartilage anterior wall and the cartilage posterior wall to reflect the average echo intensity and the uniformity of the cartilage anterior wall and the cartilage posterior wall, wherein the calculation formula is as follows:
Figure FDA0003575047400000021
Figure FDA0003575047400000022
Figure FDA0003575047400000023
Figure FDA0003575047400000024
in the formula, RawAnd SVRawMean echo intensity and homogeneity of the cartilage anterior wall are respectively represented; rpwAnd SVRpwMean echo intensity and homogeneity of the cartilage posterior wall are respectively represented;
Figure FDA0003575047400000025
and
Figure FDA0003575047400000026
respectively representing the ultrasonic echo intensity at the ith position of the cartilage anterior wall and the cartilage posterior wall; m represents the length component of cartilage along the transverse direction;
secondly, calculating the average value and the standard deviation of the ultrasonic echo intensity inside the cartilage to reflect the ultrasonic echo intensity and the uniformity inside the cartilage, wherein the calculation formula is as follows:
Figure FDA0003575047400000027
Figure FDA0003575047400000028
in the formula, RcartilageAnd SVRcartilageRespectively representing the ultrasonic echo intensity and the uniformity of the cartilage interior;
Figure FDA0003575047400000029
representing the ultrasonic echo intensity at the ith position in the cartilage;
and thirdly, calculating the ratio of the ultrasonic echo intensity of the cartilage front wall and the cartilage back wall to the ultrasonic echo intensity inside the cartilage to reflect the echo contrast of the cartilage front wall and the cartilage back wall to the cartilage inside, wherein the calculation formula is as follows:
Figure FDA00035750474000000210
Figure FDA0003575047400000031
in the formula, contastawRepresenting the contrast of the echoes of the anterior cartilage wall and the interior of the cartilage; contastpwRepresenting the contrast of the echoes of the posterior wall of the cartilage to the interior of the cartilage。
8. The ultrasonic image quantitative evaluation system of claim 6, wherein the step 2) is performed by specifically including the following steps:
according to the joint ultrasonic image, position information of the upper boundary of the cartilage is obtained, high-pass filtering is carried out on the vertical distance between each position of the upper boundary of the cartilage and the surface of the ultrasonic probe to obtain a filtered distance signal, the standard deviation of the filtered distance signal is obtained to reflect the roughness of the surface of the cartilage, and the calculation formula of the roughness is as follows:
Figure FDA0003575047400000032
in the formula, URI represents the roughness of the cartilage surface; diRepresenting the filtered range signal at the ith position;
Figure FDA0003575047400000033
represents an average value of the filtered distance signal; m represents the length component of cartilage along the transverse direction.
9. The ultrasonic image quantitative evaluation system of claim 6, wherein the step 3) is performed by the following steps:
selecting an interested region with the length of n from the lower parts of the front cartilage wall and the rear cartilage wall, equally dividing the interested region into a plurality of parts, linearly fitting the ultrasonic echo intensity and the corresponding depth of each position in the interested region, and representing the echo amplitude attenuation coefficient by using the slope of a fitting straight line, thereby obtaining the echo amplitude attenuation coefficient in each section range and the whole range of the interested region contained in the front cartilage wall and the rear cartilage wall and reflecting the change of the sound attenuation characteristic related to cartilage pathological changes;
and obtaining the echo amplitude attenuation coefficients in each section range and the whole range of the region of interest contained in the cartilage in the same way.
10. The ultrasonic image quantitative evaluation system of claim 6, wherein the following is specifically included in the step 4):
and calculating the vertical distances of the cartilage anterior wall and the cartilage posterior wall at different positions, wherein the average value, the standard value and the minimum value of all the vertical distances can respectively reflect the average value, the thickness uniformity and the minimum thickness of the cartilage thickness.
11. The ultrasonic image quantitative evaluation system of claim 6, wherein the step 5) is performed by specifically including the following steps:
fitting scattering photon distribution parameters inside the cartilage by using an ultrasonic envelope signal according to a scattering photon statistical distribution model, wherein the Nakagami distribution model adopted by the scattering photon statistical distribution model is as follows:
Figure FDA0003575047400000041
in the formula, r is the local ultrasonic echo intensity inside the cartilage, and r is more than or equal to 0; n is a shape parameter, and n is more than 0; omega is a scale parameter and is more than 0; Γ (n) represents a Gamma function with respect to the shape parameter n.
CN202110117792.0A 2021-01-28 2021-01-28 Ultrasonic image quantitative evaluation system Active CN112807024B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110117792.0A CN112807024B (en) 2021-01-28 2021-01-28 Ultrasonic image quantitative evaluation system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110117792.0A CN112807024B (en) 2021-01-28 2021-01-28 Ultrasonic image quantitative evaluation system

Publications (2)

Publication Number Publication Date
CN112807024A CN112807024A (en) 2021-05-18
CN112807024B true CN112807024B (en) 2022-05-24

Family

ID=75859970

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110117792.0A Active CN112807024B (en) 2021-01-28 2021-01-28 Ultrasonic image quantitative evaluation system

Country Status (1)

Country Link
CN (1) CN112807024B (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1689020A (en) * 2002-09-16 2005-10-26 成像治疗仪股份有限公司 Imaging markers in musculoskeletal disease
CN102973293A (en) * 2011-09-06 2013-03-20 通用电气公司 Method and system for ultrasound based automated detection, quantification and tracking of pathologies
CN104116524A (en) * 2014-01-21 2014-10-29 深圳市一体医疗科技有限公司 Ultrasonic attenuation coefficient compensation system and liver fat detection system
CN104997534A (en) * 2015-07-24 2015-10-28 复旦大学 Ultrasound attenuation coefficient imaging method based on a least squares method with constraints
CN105377145A (en) * 2013-05-24 2016-03-02 森尼布鲁克研究所 System and method for classifying and characterizing tissues using first-order and second-order statistics of quantitative ultrasound parametric maps
CN105636520A (en) * 2013-10-07 2016-06-01 古野电气株式会社 Ultrasound diagnosis device, ultrasound diagnosis method, and ultrasound diagnosis program
CN110313940A (en) * 2019-08-01 2019-10-11 无锡海斯凯尔医学技术有限公司 Signal decay calculation method, apparatus, equipment and computer readable storage medium

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010099359A1 (en) * 2009-02-25 2010-09-02 Mohamed Rashwan Mahfouz Customized orthopaedic implants and related methods
EP3395257A4 (en) * 2015-12-24 2019-08-07 Olympus Corporation Ultrasonic observation device, method for operating ultrasonic observation device, and program for operating ultrasonic observation device
EP3694419B1 (en) * 2017-10-13 2024-05-08 The Cleveland Clinic Foundation Advanced ultrasonic detection of different tissue types
KR20210027246A (en) * 2018-04-04 2021-03-10 토모웨이브 래버러토리즈, 인코포레이티드 Quantitative imaging system and use thereof

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1689020A (en) * 2002-09-16 2005-10-26 成像治疗仪股份有限公司 Imaging markers in musculoskeletal disease
CN102973293A (en) * 2011-09-06 2013-03-20 通用电气公司 Method and system for ultrasound based automated detection, quantification and tracking of pathologies
CN105377145A (en) * 2013-05-24 2016-03-02 森尼布鲁克研究所 System and method for classifying and characterizing tissues using first-order and second-order statistics of quantitative ultrasound parametric maps
CN105636520A (en) * 2013-10-07 2016-06-01 古野电气株式会社 Ultrasound diagnosis device, ultrasound diagnosis method, and ultrasound diagnosis program
CN104116524A (en) * 2014-01-21 2014-10-29 深圳市一体医疗科技有限公司 Ultrasonic attenuation coefficient compensation system and liver fat detection system
CN104997534A (en) * 2015-07-24 2015-10-28 复旦大学 Ultrasound attenuation coefficient imaging method based on a least squares method with constraints
CN110313940A (en) * 2019-08-01 2019-10-11 无锡海斯凯尔医学技术有限公司 Signal decay calculation method, apparatus, equipment and computer readable storage medium

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
A Novel Indicator for Quantitative Assessment of Articular Cartilage Degeneration Using Ultrasound;ZHANG J, 等;《2nd International Conference on Biomedical and Biological Engineering 2017》;20171231;全文 *
Intravascular ultrasound (IVUS): a potential arthroscopic tool for quantitative assessment of articular cartilage;Huang Y P,等;《The open biomedical engineering journal》;20090626;全文 *
Männicke N, 等.3-D high-frequency ultrasound backscatter analysis of human articular cartilage.《Ultrasound in Medicine & Biology》.2014,全文. *
Ultrasound guided fluorescence molecular tomography with improved quantification by an attenuation compensated born-normalization and in vivo preclinical study of cancer;Li B, 等;《Review of Scientific Instruments》;20140525;全文 *
超声射频信号定量分析发现关节软骨退行;李姗姗，等;《中国医疗设备》;20191231;第34卷(第5期);全文 *

Also Published As

Publication number Publication date
CN112807024A (en) 2021-05-18

Similar Documents

Publication Publication Date Title
Loizou et al. Snakes based segmentation of the common carotid artery intima media
US7292721B2 (en) Calibration of tissue densities in computerized tomography
Molinari et al. A state of the art review on intima–media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound
Loizou A review of ultrasound common carotid artery image and video segmentation techniques
CN103054563B (en) A kind of quantification of blood vessel wall image texture characteristic and extracting method
Hsu et al. Breast tumor classification using different features of quantitative ultrasound parametric images
DE102012108121A1 (en) Method and system for ultrasound-assisted automatic detection, quantification and tracking of pathologies
Baek et al. Clusters of ultrasound scattering parameters for the classification of steatotic and normal livers
CN103720489A (en) Lesion tissue growth monitoring method and system
CN112819773B (en) Ultrasonic image quantitative evaluation system
Moon et al. Computer-aided diagnosis based on speckle patterns in ultrasound images
Tsui et al. Characterization of lamina propria and vocal muscle in human vocal fold tissue by ultrasound Nakagami imaging
JP5235103B2 (en) Ultrasonic diagnostic equipment
Baek et al. Disease-specific imaging utilizing support vector machine classification of H-scan parameters: assessment of steatosis in a rat model
Fang et al. Ultrasound parametric imaging of hepatic steatosis using the homodyned-K distribution: An animal study
Treece et al. Ultrasound attenuation measurement in the presence of scatterer variation for reduction of shadowing and enhancement
Seabra et al. A 3-D ultrasound-based framework to characterize the echo morphology of carotid plaques
CN111528914A (en) Image processing method and device
CN112807024B (en) Ultrasonic image quantitative evaluation system
Liu et al. Segmentation of elastographic images using a coarse-to-fine active contour model
Schlaps et al. Ultrasonic tissue characterization using a diagnostic expert system
CN109394263B (en) Ultrasonic scatterer diameter multi-scale imaging method based on backscattering coefficient
Fakhr et al. Automatic diagnosis of liver diseases from ultrasound images
Zenteno et al. Improving the quality of attenuation imaging using full angular spatial compounding
Qi et al. Detection of carotid intima and media thicknesses based on ultrasound B-mode images clustered with Gaussian mixture model

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant