CN109886938B - Automatic measuring method for blood vessel diameter of ultrasonic image - Google Patents
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Abstract
The invention relates to the technical field of medical image analysis, and discloses an automatic measuring method for the diameter of a blood vessel of an ultrasonic image, which comprises the following steps: acquiring a preprocessed ultrasonic image; obtaining a multi-threshold segmentation ultrasonic image according to the obtained preprocessed ultrasonic image; and according to the obtained multi-threshold segmentation ultrasonic image, automatically measuring the diameter of the blood vessel through ellipse fitting. By performing enhancement operation on an ultrasonic image, because the ultrasonic image is spread with a large amount of noise particles, noise smoothing is performed on the enhanced image, then multi-threshold image segmentation is performed on the smoothed image, the ultrasonic image is divided into four different areas according to pixel gray values, because the blood vessel is generally circular in shape, the pathologic blood vessel is generally elliptical, elliptical curve fitting is performed on the segmented blood vessel area, manual intervention is not needed in the whole algorithm, automatic measurement of the blood vessel diameter of the ultrasonic image is realized, and therefore an important clinical auxiliary diagnosis technology is provided for PICC or CVC operation.
Description
Technical Field
The invention relates to the technical field of medical image analysis, in particular to an automatic measuring method for the diameter of a blood vessel by using an ultrasonic image.
Background
PICC (central venous catheter for peripheral venipuncture) or CVC (central venous catheter) is a special silicone tube which can be punctured through skin and is retained in the great venous lumen (superior vena cava, inferior vena cava, brachiocephalic vein, internal jugular vein, subclavian vein, iliac vein and femoral vein) for long-term transfusion. In the operation process, the diameter of the catheter is selected to be too large or too small, which brings adverse effects to the operation.
In order to realize that a proper catheter can be selected for intubation according to the blood vessel diameter, imaging display of a blood vessel region is needed to be carried out on a puncture part by means of ultrasonic equipment, and a proper intubation is selected for operation according to blood vessel diameter information of a display result. The traditional measurement method requires a doctor with abundant experience to manually draw the ultrasonic image blood vessel region, is greatly influenced by subjective factors, consumes time in drawing and increases the recognition difficulty. Ultrasonic imaging has become one of the important tools for clinical auxiliary diagnosis because of the characteristics of real time, no damage, low price and the like, so that the ultrasonic imaging is more and more widely applied to PICC or CVC puncture operation.
At present, blood vessel diameter measurement is realized by adopting a method based on Ojin segmentation and morphological processing, the method needs to firstly mark a blood vessel region to be segmented by using a square frame by using a detection method based on HAAR features (namely linear features, edge features, point features and diagonal features) and a Adaboost (Adaptive boosting) classifier, then uses a square central point as a blood vessel central point, obtains a binarized image by cutting an Ojin threshold value, obtains a blood vessel boundary by combining a morphological processing method, and finally directly measures the distance from the central point to the boundary to be used as radius information of a final blood vessel. The method does not consider the influence of noise on the edge of the blood vessel, and the center point is selected more randomly, so that the calculation accuracy is lower. The blood vessel region is displayed as hypoechoic in the image, and the color is dark, so that the blood vessel region is difficult to distinguish from the background region, and the ultrasonic image is required to be enhanced. If the enhancement is excessive, the needed blood vessel information is lost; if the enhancement is too weak, the vascular region cannot be distinguished from the background region.
Therefore, how to automatically measure the vessel diameter of an ultrasound image is a technical problem to be solved.
Disclosure of Invention
The technical problem to be solved by the invention is how to automatically measure the vessel diameter of an ultrasonic image.
To this end, according to a first aspect, an embodiment of the present invention discloses an ultrasound image vessel diameter automatic measurement method, including: acquiring a preprocessed ultrasonic image; obtaining a multi-threshold segmentation ultrasonic image according to the obtained preprocessed ultrasonic image; and according to the obtained multi-threshold segmentation ultrasonic image, automatically measuring the diameter of the blood vessel through ellipse fitting.
Optionally, the acquiring the preprocessed ultrasound image includes: carrying out fractional differential enhancement calculation on the ultrasonic image; and denoising the enhanced ultrasonic image to obtain a denoised ultrasonic image.
Optionally, the performing fractional differential enhancement calculation on the ultrasound image includes: giving out corresponding differential enhancement orders v, and obtaining gradient values of the ultrasonic image in the x-axis direction and the y-axis direction; and calculating the average value of gradient values in the x-axis direction and the y-axis direction of the ultrasonic image, and obtaining the enhancement factors for image enhancement.
Optionally, the denoising operation is performed on the enhanced ultrasound image, and obtaining the denoised ultrasound image includes: based on the differential enhancement order v, a diffusion threshold k is given, and anisotropic diffusion filtering is performed on the ultrasound image.
Optionally, the obtaining the multi-threshold segmentation ultrasound image according to the obtained preprocessed ultrasound image includes: dividing the denoised image into areas according to pixel gray values by using a particle swarm optimization algorithm, and dividing the ultrasonic image into four areas with different gray values; binarizing the segmented image, wherein the area with the minimum gray value is set to be 0, and the rest is set to be 1; acquiring all connected areas in the binary ultrasonic image by using a cavity filling method; calculating the area of each communication area, and reserving the area maximum area of the ultrasonic image; and acquiring the edge of the area maximum area of the ultrasonic image by an edge detection method and displaying the edge on the original ultrasonic image.
Optionally, the automatically measuring the diameter of the blood vessel by elliptical fitting according to the obtained multi-threshold segmentation ultrasonic image comprises: performing ellipse fitting on edge points of the division target by using a least square method; and displaying the fitting result on the original ultrasonic image, and automatically calculating the size of the blood vessel diameter according to the fitting result.
According to a second aspect, an embodiment of the present invention discloses an ultrasonic image blood vessel diameter automatic measurement device, which is characterized by comprising: the image preprocessing module is used for acquiring preprocessed ultrasonic images; the image segmentation module is used for obtaining a multi-threshold segmentation ultrasonic image according to the obtained preprocessed ultrasonic image; and the diameter measurement module is used for automatically measuring the diameter of the blood vessel through ellipse fitting according to the obtained multi-threshold segmentation ultrasonic image.
Optionally, the image preprocessing module includes: the image enhancement unit is used for carrying out fractional differential enhancement calculation on the ultrasonic image; and the image denoising unit is used for denoising the enhanced ultrasonic image to obtain a denoised ultrasonic image.
According to a third aspect, an embodiment of the present invention discloses a computer device, including a processor, where the processor is configured to execute a computer program stored in a memory to implement the method for automatically measuring a blood vessel diameter in an ultrasound image according to any one of the first aspect.
According to a fourth aspect, an embodiment of the present invention discloses a computer-readable storage medium, on which a computer program is stored, and a processor is configured to execute the computer program stored in the storage medium to implement the automatic measurement method for an ultrasound image vessel diameter according to any one of the above first aspects.
The invention has the following beneficial effects: by performing enhancement operation on an ultrasonic image, because the ultrasonic image is spread with a large amount of noise particles, noise smoothing is performed on the enhanced image, then multi-threshold image segmentation is performed on the smoothed image, the ultrasonic image is divided into four different areas according to pixel gray values, because the blood vessel is generally circular in shape, the pathologic blood vessel is generally elliptical, elliptical curve fitting is performed on the segmented blood vessel area, manual intervention is not needed in the whole algorithm, automatic measurement of the blood vessel diameter of the ultrasonic image is realized, and therefore an important clinical auxiliary diagnosis technology is provided for PICC or CVC operation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an automatic measuring method for the diameter of a blood vessel by using an ultrasonic image disclosed in the embodiment;
fig. 2 is a schematic structural view of an ultrasonic image vessel diameter automatic measuring device disclosed in this embodiment;
FIG. 3 is a schematic diagram showing steps of an automatic measuring method for blood vessel diameter by using ultrasonic images according to the present embodiment;
FIG. 4 is a graph showing the contrast effect of an ultrasonic image for an automatic measuring method for blood vessel diameter in an ultrasonic image according to the present embodiment; FIG. 4a is an original ultrasound image; FIG. 4b is an ultrasound vessel marker image;
FIG. 5 is a fractional differential enhancement template of an automatic measurement method for vessel diameter in ultrasound images disclosed in this example;
FIG. 6 is a fractional differential-based anisotropic diffusion filter template of an ultrasound image vessel diameter automatic measurement method disclosed in this embodiment;
FIG. 7 is a graph of the result of ultrasonic image preprocessing of an automatic measuring method for the vessel diameter of an ultrasonic image disclosed in the present embodiment; FIG. 7a is an original ultrasound image; FIG. 7b is an enhanced image; FIG. 7c is a filtered image;
FIG. 8 is a diagram showing an image segmentation process of an automatic ultrasonic image vessel diameter measurement method disclosed in the present embodiment; FIG. 8a is a segmentation threshold segmentation image; FIG. 8b is a binarized image; FIG. 8c is a connected region image; FIG. 8d is an edge image of a maximum connected region; fig. 8e is a segmented blood vessel region image.
Reference numerals: steps S100 to S130; 210. an image preprocessing module; 211. an image enhancement unit; 212. an image denoising unit; 220. an image segmentation module; 230. a diameter measurement module.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention discloses an automatic measuring method for the diameter of an ultrasonic image blood vessel, which is shown in fig. 1 and 3 and comprises the following steps:
step S110, acquiring a preprocessed ultrasonic image;
step S120, obtaining a multi-threshold segmentation ultrasonic image according to the obtained preprocessed ultrasonic image;
and step S130, according to the obtained multi-threshold segmentation ultrasonic image, automatically measuring the diameter of the blood vessel through ellipse fitting.
As shown in fig. 4, fig. 4 is an ultrasound image contrast effect diagram of an ultrasound image blood vessel diameter automatic measurement method disclosed in this embodiment, fig. 4a is an original ultrasound image, and fig. 4b is an ultrasound blood vessel marker image.
Compared with the prior art, the scheme disclosed by the embodiment of the invention has the advantages that the enhanced ultrasonic image is subjected to the enhancement operation, the enhanced image is subjected to noise smoothing because the ultrasonic image is spread with a large amount of noise particles, then the smoothed image is subjected to multi-threshold image segmentation, the ultrasonic image is divided into four different areas according to pixel gray values, the blood vessel is generally circular in shape, the pathologic blood vessel is generally elliptical, the segmented blood vessel areas are subjected to elliptical curve fitting, the whole algorithm does not need manual intervention, and the automatic measurement of the blood vessel diameter of the ultrasonic image is realized, so that an important clinical auxiliary diagnosis technology is provided for PICC or CVC operation.
In a specific embodiment, step S110 may specifically include:
step S111, carrying out fractional differential enhancement calculation on an ultrasonic image;
and step S112, denoising the enhanced ultrasonic image to obtain a denoised ultrasonic image.
In a specific embodiment, step S111 may specifically include:
giving out corresponding differential enhancement orders v, and obtaining gradient values of the ultrasonic image in the x-axis direction and the y-axis direction; in this embodiment, the image enhancement uses a fractional differential algorithm whose differential expression defined by Grunwld-Letnikov is as follows:
and constructing an enhanced template according to each coefficient on the right side of the equal sign, and setting the center point as the center of the mask, and expanding the enhanced template towards positive and negative directions of an x axis and a y axis and a diagonal direction respectively so as to ensure that the template has rotation invariance. As shown in fig. 5, fig. 5 is a fractional differential enhancement template of an automatic measuring method for a blood vessel diameter of an ultrasonic image according to the present embodiment.
And calculating the average value of gradient values in the x-axis direction and the y-axis direction of the ultrasonic image, and obtaining the enhancement factors for image enhancement. The calculation formula of the gradients in the x-axis direction and the y-axis direction is as follows:
obtaining a gradient image:
F＝Gx+Gy
gradient mean value:
avg＝sum(F(：))/(m*n)
where sum represents a summation function, sum (F (:)) is the sum of gray values of pixels of the image F, and m and n are the image sizes. The corresponding enhancement factors are obtained according to the calculation formula as follows:
in a specific embodiment, step S112 may specifically include: based on the differential enhancement order v, a diffusion threshold k is given, and anisotropic diffusion filtering is performed on the ultrasound image. In the embodiment, the image denoising adopts an anisotropic diffusion filtering algorithm (FAD algorithm) based on fractional differential, and the core idea of the algorithm is to introduce a fractional differential theory on the basis of anisotropic diffusion, so that the purposes of image denoising and edge protection are achieved through the mutual cooperation between a diffusion threshold k and a differential order v. The mathematical expression of the anisotropic diffusion is as follows:
where div is the divergence operator,.v is the gradient of the image, c (|i|) is the diffusion function for detecting the smooth intensity of the image, λ is typically set to 0.2. The expression of the diffusion function is as follows:
where k is the diffusion threshold. Combining fractional differential theory, the filtering template of the invention is shown in figure 6.
The blood vessel region can be independent from the background region after the image enhancement and the image denoising processes. Fig. 7 is a graph of the result of ultrasonic image preprocessing of an ultrasonic image blood vessel diameter automatic measurement method disclosed in this embodiment, fig. 7a is an original ultrasonic image, fig. 7b is an enhanced image, and fig. 7c is a filtered image.
In a specific embodiment, step 120 may specifically include:
step S121, dividing the denoised image into areas with different gray values according to the gray values of pixel points by using a particle swarm optimization algorithm, and dividing the ultrasonic image into four areas with different gray values;
step S122, binarizing the segmented image, wherein the area with the minimum gray value is set to 0, and the rest is set to 1;
step S123, acquiring all connected areas in the binary ultrasonic image by using a cavity filling method;
step S124, calculating the area of each communication area, and reserving the area maximum area of the ultrasonic image;
in step S125, the edge of the area of the ultrasound image with the largest area is obtained by the edge detection method and displayed on the original ultrasound image.
In this embodiment, a particle swarm optimization algorithm (PSO algorithm) obtains three optimal segmentation thresholds, the PSO algorithm is derived from behavioral studies of bird predation, i.e., initializing a population of particles in an image and giving the particles an initial velocity and position. The fitness value of each particle is calculated according to a fitness function, wherein the maximum inter-class variance is used as the fitness function, and the expression is as follows:
in the method, in the process of the invention,for the probability of occurrence of class j +.>For the mean value of class j>Is the average of all clusters. And calculating the fitness value of each particle through the formula to obtain an individual optimal value and a global optimal value. Then evolves according to a velocity and position formula, which is as follows:
wherein V is i t+1 For updated particle velocity, V i t Is the current particle velocity. w is an inertial weight coefficient, usually set to [ 0.8-1.2 ]]. If the value of w is larger, the global convergence capacity is strong, and the local convergence capacity is weak; if the value of w is smaller, the global convergence capacity is weak and the local convergence capacity is strong. In order to improve the global convergence capacity of the algorithm, w is set to be 1.2, and if the value is larger than 1.2, the local extremum is easy to trap. c1 and c2 are learning factors, also called acceleration constants. R1 and r2 are 0-1]Random numbers in between.
P i t For the extremum of individuals, nP i t As a global extremum value,for the current particle position +.>Is the updated particle position. And (3) carrying out iterative computation in a circulating way until the iteration times reach a preset value, ending the computation and outputting an optimal segmentation threshold. Because the blood vessel area returns a weak echo signal, the point set less than the minimum threshold value in the filtered image can be 1 and the rest is 0 to obtain a binarized image. And then removing the areas connected with the boundary by a hole filling method, calculating the area of each connected area, obtaining the edge curve of the largest connected area, and superposing the edge curve into the original image to finally realize the segmentation of the ultrasonic blood vessel. As shown in fig. 8, fig. 8 is a diagram of an image segmentation process of an automatic ultrasonic image vessel diameter measurement method disclosed in the present embodiment; FIG. 8a is a segmentation threshold segmentation image; FIG. 8b is a binarized image; FIG. 8c is a connected region image; FIG. 8d is an edge image of a maximum connected region; fig. 8e is a segmented blood vessel region image.
In a specific embodiment, step S130 may specifically include:
step S131, performing ellipse fitting on edge points of the division target by using a least square method;
and step S132, displaying the fitting result on the original ultrasonic image, and automatically calculating the size of the blood vessel diameter according to the fitting result.
In this embodiment, since the ultrasound image is covered with a large amount of noise, so that the finally segmented extravascular Zhou Maoci is more, and the normal blood vessel of the human body is generally changed into a circle, and the diseased blood vessel is in an elliptical shape, ellipse fitting is performed on the segmented edge point set, and a least square method is adopted here. And obtaining coordinate information of the ellipse centroid and the long and short axes and the ellipse inclination angle according to the fitting result, and calculating the diameter of the blood vessel. As shown in fig. 4b, fig. 4b is an ultrasound vessel labeling image, which is the final fitted vessel.
As shown in fig. 2, an embodiment of the present invention discloses an automatic measuring device for a blood vessel diameter by using an ultrasonic image, which is characterized by comprising: an image preprocessing module 210 for acquiring a preprocessed ultrasound image; the image segmentation module 220 is configured to obtain a multi-threshold segmented ultrasound image according to the obtained preprocessed ultrasound image; the diameter measurement module 230 is configured to automatically measure the diameter of the blood vessel by ellipse fitting according to the obtained multi-threshold segmentation ultrasound image.
Optionally, the image preprocessing module 210 includes: an image enhancement unit 211 for performing fractional differential enhancement calculation on the ultrasound image; the image denoising unit 222 is configured to denoise the enhanced ultrasound image, and obtain a denoised ultrasound image.
In addition, in the embodiment of the present invention, a computer device is further provided, where the processor executes computer instructions to implement the following method:
acquiring a preprocessed ultrasonic image; obtaining a multi-threshold segmentation ultrasonic image according to the obtained preprocessed ultrasonic image; and according to the obtained multi-threshold segmentation ultrasonic image, automatically measuring the diameter of the blood vessel through ellipse fitting.
It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, and the program may be stored in a computer readable storage medium, and the program may include the above-described embodiment method when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a Random Access Memory (RAM), or the like. The computer processor is configured to execute a computer program stored in a storage medium to implement the method of:
acquiring a preprocessed ultrasonic image; obtaining a multi-threshold segmentation ultrasonic image according to the obtained preprocessed ultrasonic image; and according to the obtained multi-threshold segmentation ultrasonic image, automatically measuring the diameter of the blood vessel through ellipse fitting.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (8)
1. An automatic measuring method for the diameter of a blood vessel by using an ultrasonic image is characterized by comprising the following steps:
acquiring a preprocessed ultrasound image, the acquiring the preprocessed ultrasound image comprising:
gives a corresponding toThe differential enhancement order v of the ultrasonic image is obtained, and the gradient values of the ultrasonic image in the x and y directions are obtained, wherein the gradient calculation formula of the x and y directions is as followsObtaining gradient image->；
Calculating the average value of gradient values in the x-axis direction and the y-axis direction of the ultrasonic image, wherein the average value of gradient values isWherein sum represents a summation function, sum (F (:)) is the sum of gray values of all pixel points of the image F, and m and n are the image sizes;
the corresponding enhancement factor is obtained according to the above formula:
；
the method comprises the steps of obtaining enhancement factors for image enhancement, wherein the image enhancement adopts a fractional differential algorithm, and a differential expression defined by Grunwld-Letnikov is as follows:
，
the method comprises the steps of constructing a reinforced template according to each coefficient on the right side of an equal sign, setting a center point as a mask center, and expanding the mask center to positive and negative directions of an x axis and a y axis and a diagonal direction respectively so as to ensure that the template has rotation invariance;
denoising the enhanced ultrasonic image to obtain a denoised ultrasonic image;
obtaining a multi-threshold segmentation ultrasonic image according to the obtained preprocessed ultrasonic image;
and according to the obtained multi-threshold segmentation ultrasonic image, automatically measuring the diameter of the blood vessel through ellipse fitting.
2. The method for automatically measuring the diameter of a blood vessel in an ultrasonic image according to claim 1, wherein the denoising operation is performed on the enhanced ultrasonic image, and obtaining the denoised ultrasonic image comprises:
based on differential enhancement ordervGiving a diffusion threshold k, and performing anisotropic diffusion filtering on the ultrasonic image.
3. The method of claim 1, wherein obtaining a multi-thresholded segmented ultrasound image from the acquired pre-processed ultrasound image comprises:
dividing the denoised image into areas according to pixel gray values by using a particle swarm optimization algorithm, and dividing the ultrasonic image into four areas with different gray values;
binarizing the segmented image, wherein the area with the minimum gray value is set to be 0, and the rest is set to be 1;
acquiring all connected areas in the binary ultrasonic image by using a cavity filling method;
calculating the area of each communication area, and reserving the area maximum area of the ultrasonic image;
and acquiring the edge of the area maximum area of the ultrasonic image by an edge detection method and displaying the edge on the original ultrasonic image.
4. The method according to claim 1, wherein said automatically measuring the vessel diameter by elliptical fitting based on said obtained multi-threshold segmented ultrasound image comprises:
performing ellipse fitting on edge points of the division target by using a least square method;
and displaying the fitting result on the original ultrasonic image, and automatically calculating the size of the blood vessel diameter according to the fitting result.
5. An ultrasound image blood vessel diameter automatic measurement apparatus for realizing the ultrasound image blood vessel diameter automatic measurement method according to any one of claims 1 to 4, comprising:
the image preprocessing module is used for acquiring preprocessed ultrasonic images;
the image segmentation module is used for obtaining a multi-threshold segmentation ultrasonic image according to the obtained preprocessed ultrasonic image;
and the diameter measurement module is used for automatically measuring the diameter of the blood vessel through ellipse fitting according to the obtained multi-threshold segmentation ultrasonic image.
6. The ultrasound image vessel diameter automatic measurement device according to claim 5, wherein the image preprocessing module comprises:
an image enhancement unit configured to:
giving out corresponding differential enhancement order v, and obtaining gradient values of the ultrasonic image in the x-axis direction and the y-axis direction, wherein the gradient calculation formula of the x-axis direction and the y-axis direction is as followsObtaining gradient image->；
Calculating the average value of gradient values in the x-axis direction and the y-axis direction of the ultrasonic image, wherein the average value of gradient values isWherein sum represents a summation function, sum (F (:)) is the sum of gray values of all pixel points of the image F, and m and n are the image sizes;
the corresponding enhancement factor is obtained according to the above formula:
；
the method comprises the steps of obtaining enhancement factors for image enhancement, wherein the image enhancement adopts a fractional differential algorithm, and a differential expression defined by Grunwld-Letnikov is as follows:
，
the method comprises the steps of constructing a reinforced template according to each coefficient on the right side of an equal sign, setting a center point as a mask center, and expanding the mask center to positive and negative directions of an x axis and a y axis and a diagonal direction respectively so as to ensure that the template has rotation invariance;
and the image denoising unit is used for denoising the enhanced ultrasonic image to obtain a denoised ultrasonic image.
7. A computer device comprising a processor for executing a computer program stored in a memory to implement the ultrasound image vessel diameter automatic measurement method according to any one of claims 1 to 4.
8. A computer-readable storage medium having stored thereon a computer program, wherein a processor is configured to execute the computer program stored in the storage medium to implement the ultrasound image vessel diameter automatic measurement method according to any one of claims 1 to 4.
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| CN112233122A (en) * | 2019-06-28 | 2021-01-15 | 深圳市理邦精密仪器股份有限公司 | Method and device for extracting and measuring object in ultrasonic image |
| CN110349203B (en) * | 2019-07-15 | 2023-05-09 | 深圳市威尔德医疗电子有限公司 | Ultrasonic equipment and method for measuring blood vessel diameter in ultrasonic image thereof |
| CN111388006B (en) * | 2020-02-21 | 2023-03-28 | 华东师范大学 | System and method for dynamically and automatically measuring inferior vena cava ultrasound |
| CN113643353B (en) * | 2020-09-04 | 2024-02-06 | 深圳硅基智能科技有限公司 | Measurement method for enhancing resolution of vascular caliber of fundus image |
| CN113344842A (en) * | 2021-03-24 | 2021-09-03 | 同济大学 | Blood vessel labeling method of ultrasonic image |
| CN113469941B (en) * | 2021-05-27 | 2022-11-08 | 武汉楚精灵医疗科技有限公司 | Method for measuring width of bile-pancreatic duct in ultrasonic bile-pancreatic duct examination |
| CN114119521A (en) * | 2021-11-17 | 2022-03-01 | 杭州医派智能科技有限公司 | Method, computer device and computer-readable storage medium for determining renal vascular stenosis |
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