WO2020154904A1 - Procédé de mesure automatique du diamètre de vaisseau sanguin dans une image ultrasonore - Google Patents

Procédé de mesure automatique du diamètre de vaisseau sanguin dans une image ultrasonore Download PDF

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Publication number
WO2020154904A1
WO2020154904A1 PCT/CN2019/073736 CN2019073736W WO2020154904A1 WO 2020154904 A1 WO2020154904 A1 WO 2020154904A1 CN 2019073736 W CN2019073736 W CN 2019073736W WO 2020154904 A1 WO2020154904 A1 WO 2020154904A1
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WIPO (PCT)
Prior art keywords
ultrasound image
blood vessel
image
ultrasound
vessel diameter
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PCT/CN2019/073736
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English (en)
Chinese (zh)
Inventor
李聪
王兴红
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深圳市科曼医疗设备有限公司
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Priority to PCT/CN2019/073736 priority Critical patent/WO2020154904A1/fr
Publication of WO2020154904A1 publication Critical patent/WO2020154904A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof

Definitions

  • the invention relates to the technical field of medical image analysis, in particular to a method for automatically measuring blood vessel diameters in ultrasound images.
  • PICC central venous catheterization via peripheral venous puncture
  • CVC central venous catheter
  • the measurement of blood vessel diameter is realized by segmentation and morphological processing based on Otsu.
  • This method requires detection based on HAAR features (ie linear features, edge features, point features, diagonal features) and Adaboost (Adaptive boosting) classifiers.
  • HAAR features ie linear features, edge features, point features, diagonal features
  • Adaboost Adaptive boosting
  • the blood vessel area appears as low echo in the image, and the color is dark, making it difficult to distinguish the blood vessel area from the background area, so the ultrasound image must be enhanced. If the enhancement is too high, the necessary blood vessel information will be lost; if the enhancement is too weak, the blood vessel area cannot be distinguished from the background area.
  • the technical problem to be solved by the present invention is how to automatically measure the blood vessel diameter of the ultrasound image.
  • an embodiment of the present invention discloses an automatic measurement method of ultrasound image blood vessel diameter, including: acquiring a pre-processed ultrasound image; obtaining a multi-threshold segmentation ultrasound image according to the acquired pre-processed ultrasound image; The ultrasound image is segmented according to the obtained multi-threshold value, and the blood vessel diameter is automatically measured through ellipse fitting.
  • the obtaining the preprocessed ultrasound image includes: performing a fractional differential enhancement calculation on the ultrasound image; performing a denoising operation on the enhanced ultrasound image to obtain a denoised ultrasound image.
  • the performing fractional differential enhancement calculation on the ultrasound image includes: giving the corresponding differential enhancement order v, obtaining the ultrasound image x and y axis gradient values; calculating the ultrasound image x and y axis gradient value average, Obtain enhancement factors for image enhancement.
  • the denoising operation on the enhanced ultrasound image to obtain the denoised ultrasound image includes: based on the differential enhancement order v, giving a diffusion threshold k, and performing anisotropic diffusion filtering on the ultrasound image.
  • the obtaining the multi-threshold segmented ultrasound image according to the pre-processed ultrasound image includes: using a particle swarm optimization algorithm to divide the denoised image according to the gray value of the pixel point, and divide the ultrasound image into four A region with different gray values; binarize the segmented image, set the region with the smallest gray value to 0, and set the rest to 1; use the hole filling method to obtain all connected regions in the binary ultrasound image; calculate The area of each connected area retains the largest area of the ultrasound image; through the edge detection method, the edge of the largest area of the ultrasound image is obtained and displayed on the original ultrasound image.
  • the segmentation of the ultrasound image according to the obtained multi-threshold value, and the automatic measurement of the diameter of the blood vessel through ellipse fitting includes: performing ellipse fitting on the edge points of the segmentation target by the least square method; and displaying the fitting result in the original ultrasound On the image, and automatically calculate the size of the blood vessel diameter according to the fitting result.
  • an embodiment of the present invention discloses an automatic measurement device for blood vessel diameter in ultrasound images, which is characterized in that it comprises: an image preprocessing module for acquiring preprocessed ultrasound images; an image segmentation module for The preprocessed ultrasound image is obtained to obtain a multi-threshold segmentation ultrasound image; the diameter measurement module is used to segment the ultrasound image according to the obtained multi-threshold value, and automatically measure the diameter of the blood vessel through ellipse fitting.
  • the image preprocessing module includes: an image enhancement unit for performing fractional differential enhancement calculation on the ultrasound image; an image denoising unit for performing a denoising operation on the enhanced ultrasound image to obtain denoising Ultrasound image.
  • an embodiment of the present invention discloses a computer device, including a processor, configured to execute a computer program stored in a memory to implement the method for automatically measuring blood vessel diameter in an ultrasound image according to any one of the above-mentioned first aspects.
  • an embodiment of the present invention discloses a computer-readable storage medium on which a computer program is stored, and the processor is configured to execute the computer program stored in the storage medium to implement the ultrasound image blood vessel diameter of any one of the above-mentioned first aspects. Automatic measurement method.
  • the present invention has the following beneficial effects: by performing an enhancement operation on the ultrasound image, since the ultrasound image is filled with a large number of noise particles, the enhanced image is noise smoothed, and then the smoothed image is segmented with multiple thresholds, and the ultrasound image Divided into four different areas according to the pixel gray value. Since the shape of blood vessels is generally circular, and sick blood vessels are generally elliptical, the segmented blood vessel area is fitted with elliptic curve. The entire algorithm does not require manual intervention and realizes the ultrasound image of blood vessels. The automatic measurement of diameter provides an important clinical auxiliary diagnostic technique for PICC or CVC surgery.
  • FIG. 1 is a schematic flowchart of an automatic method for measuring blood vessel diameter in ultrasound images disclosed in this embodiment
  • FIG. 2 is a schematic structural diagram of an automatic measurement device for blood vessel diameter in ultrasound images disclosed in this embodiment
  • FIG. 3 is a schematic diagram of the steps of a method for automatically measuring blood vessel diameter in ultrasound images disclosed in this embodiment
  • Fig. 4 is an ultrasound image comparison effect diagram of a method for automatically measuring blood vessel diameter in an ultrasound image disclosed in this embodiment;
  • Fig. 4a is an original ultrasound image;
  • Fig. 4b is an ultrasound blood vessel labeling image;
  • FIG. 5 is a fractional differential enhancement template of a method for automatically measuring blood vessel diameter in ultrasound images disclosed in this embodiment
  • FIG. 6 is an anisotropic diffusion filter template based on fractional differentiation of an automatic method for measuring blood vessel diameter in ultrasound images disclosed in this embodiment
  • Fig. 7 is an ultrasound image preprocessing result diagram of an ultrasonic image blood vessel diameter automatic measurement method disclosed in this embodiment;
  • Fig. 7a is the original ultrasound image;
  • Fig. 7b is the enhanced image;
  • Fig. 7c is the filtered image;
  • Fig. 8 is an image segmentation process diagram of a method for automatically measuring blood vessel diameter in ultrasound images disclosed in this embodiment;
  • Fig. 8a is a segmentation threshold segmented image;
  • Fig. 8b is a binarized image;
  • Fig. 8c is a connected region image;
  • Fig. 8d is an acquisition The edge image of the largest connected area;
  • Figure 8e is the image of the blood vessel area after segmentation.
  • the embodiment of the present invention discloses a method for automatically measuring blood vessel diameter in ultrasound images, as shown in Fig. 1 and Fig. 3, including:
  • Step S110 acquiring a preprocessed ultrasound image
  • Step S120 Obtain a multi-threshold segmented ultrasound image according to the acquired preprocessed ultrasound image
  • step S130 the ultrasound image is segmented according to the obtained multi-threshold value, and the blood vessel diameter is automatically measured through ellipse fitting.
  • FIG. 4 is an ultrasound image comparison effect diagram of an ultrasound image blood vessel diameter automatic measurement method disclosed in this embodiment.
  • FIG. 4a is an original ultrasound image
  • FIG. 4b is an ultrasound blood vessel mark image.
  • the solution disclosed in the embodiments of the present invention performs an enhancement operation on the ultrasound image. Since the ultrasound image is filled with a large number of noise particles, the enhanced image is noise smoothed, and then the smoothed image is multi-threshold image Segmentation, and divide the ultrasound image into four different regions according to the pixel gray value. Since the shape of blood vessels is generally circular, the diseased blood vessels are generally elliptical, and the elliptic curve fitting is performed on the segmented blood vessel area. The entire algorithm does not require manual intervention , To realize the automatic measurement of the diameter of the blood vessel in the ultrasound image, thereby providing an important clinical auxiliary diagnostic technology for PICC or CVC surgery.
  • step S110 may specifically include:
  • Step S111 performing fractional differential enhancement calculation on the ultrasound image
  • Step S112 Perform a denoising operation on the enhanced ultrasound image to obtain a denoised ultrasound image.
  • step S111 may specifically include:
  • the image enhancement adopts the fractional differential algorithm, and the differential expression defined by Grünwld–Letnikov is as follows:
  • Fig. 5 is a fractional differential enhancement template of a method for automatically measuring blood vessel diameter in ultrasound images disclosed in this embodiment.
  • sum represents the sum function
  • sum(F(:)) is the sum of the gray values of each pixel of the image F
  • m and n are the image size.
  • step S112 may specifically include: based on the differential enhancement order v, giving a diffusion threshold k, and performing anisotropic diffusion filtering on the ultrasound image.
  • image denoising uses an anisotropic diffusion filtering algorithm based on fractional differential (FAD algorithm).
  • FAD algorithm fractional differential
  • the core idea of the algorithm is to introduce fractional differential theory on the basis of anisotropic diffusion, and pass the diffusion threshold k
  • the mutual cooperation with the differential order v achieves the purpose of image denoising and edge preservation.
  • the mathematical expression of anisotropic diffusion is as follows:
  • div is the divergence operator, Is the gradient of the image, It is the spread function, used to detect the smooth intensity of the image, ⁇ is usually set to 0.2.
  • the expression of the diffusion function is as follows:
  • k is the diffusion threshold.
  • FIG. 7 is an ultrasound image preprocessing result diagram of a method for automatically measuring blood vessel diameter in an ultrasound image disclosed in this embodiment.
  • FIG. 7a is an original ultrasound image
  • FIG. 7b is an enhanced image
  • FIG. 7c is a filtered image.
  • step 120 may specifically include:
  • Step S121 using a particle swarm optimization algorithm to divide the denoised image according to the gray value of the pixel point, and divide the ultrasound image into four areas with different gray values;
  • Step S122 Binarize the divided image, set the area with the smallest gray value to 0, and set the rest to 1;
  • Step S123 using the hole filling method to obtain all connected areas in the binary ultrasound image
  • Step S124 Calculate the area of each connected area, and reserve the area with the largest area of the ultrasound image
  • step S125 the edge of the area with the largest area of the ultrasound image is obtained by the edge detection method and displayed on the original ultrasound image.
  • the particle swarm optimization algorithm obtains three optimal segmentation thresholds.
  • the PSO algorithm is derived from the study of bird predation behavior, that is, initializing a group of particles in the image and giving the particles an initial velocity and position.
  • V i t+1 is the updated particle velocity
  • V i t is the current particle velocity
  • w is the inertia weight coefficient, usually set to [0.8 ⁇ 1.2]. If the value of w is selected too large, the global convergence ability is strong, and the local convergence ability is weak; if the value of w is selected too small, the global convergence ability is weak and the local convergence ability is strong. In order to improve the global convergence ability of the algorithm, set w to 1.2. If it is greater than 1.2, it is easy to fall into a local extreme.
  • c1 and c2 are learning factors, also called acceleration constants.
  • r1 and r2 are random numbers between [0 ⁇ 1].
  • FIG. 8 is an image segmentation process diagram of an ultrasonic image blood vessel diameter automatic measurement method disclosed in this embodiment; Fig. 8a is a segmentation threshold segmented image; Fig. 8b is a binarized image; Fig. 8c is a connected region Image; Figure 8d is to obtain the edge image of the largest connected area; Figure 8e is the image of the blood vessel area after segmentation.
  • step S130 may specifically include:
  • Step S131 ellipse fitting is performed on the edge points of the segmentation target by using the least square method
  • Step S132 displaying the fitting result on the original ultrasound image, and automatically calculating the size of the blood vessel diameter according to the fitting result.
  • an embodiment of the present invention discloses an automatic measurement device for the diameter of an ultrasound image blood vessel, which is characterized in that it includes: an image preprocessing module 210 for acquiring preprocessed ultrasound images; an image segmentation module 220 for According to the acquired pre-processed ultrasound image, a multi-threshold segmentation ultrasound image is obtained; the diameter measurement module 230 is configured to segment the ultrasound image according to the obtained multi-threshold value, and automatically measure the diameter of the blood vessel through ellipse fitting.
  • the image preprocessing module 210 includes: an image enhancement unit 211, configured to perform a fractional differential enhancement calculation on the ultrasound image; an image denoising unit 222, configured to perform a denoising operation on the enhanced ultrasound image to obtain Denoised ultrasound image.
  • an embodiment of the present invention also provides a computer device, and the processor executes computer instructions to implement the following methods:
  • the program can be stored in a computer readable storage medium. At this time, it may include the procedures of the above-mentioned method embodiments.
  • the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc.
  • the computer processor is used to execute the computer program stored in the storage medium to implement the following methods:

Abstract

La présente invention concerne un procédé de mesure automatique d'un diamètre de vaisseau sanguin dans une image ultrasonore, consistant : à acquérir une image ultrasonore prétraitée (S100); à obtenir une image ultrasonore segmentée à seuils multiples selon l'image ultrasonore prétraitée acquise (S110); et à mesurer automatiquement d'une taille de diamètre de vaisseau sanguin par ajustement d'ellipse selon l'image ultrasonore segmentée à seuils multiples obtenue (S120). Après l'amélioration de l'image ultrasonore, un grand nombre de bruits sont visibles tous sur l'image ultrasonore. La réduction de bruit est effectuée sur l'image améliorée, et la segmentation d'image à seuils multiples est effectuée sur l'image à bruit réduit, ensuite l'image ultrasonore est divisée en quatre régions différentes selon les valeurs d'échelle de gris de pixel. Dans la mesure où la forme d'un vaisseau sanguin est généralement circulaire, tandis que la forme d'un vaisseau sanguin malade est généralement elliptique, lorsque les régions de vaisseau sanguin segmentées sont soumises à l'ajustement de courbe elliptique, l'algorithme ne nécessite aucune intervention manuelle et atteint la mesure automatique du diamètre du vaisseau sanguin dans l'image ultrasonore, fournissant ainsi une technique clinique importante de diagnostic auxiliaire pour la chirurgie PICC ou CVC.
PCT/CN2019/073736 2019-01-29 2019-01-29 Procédé de mesure automatique du diamètre de vaisseau sanguin dans une image ultrasonore WO2020154904A1 (fr)

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CN102800089A (zh) * 2012-06-28 2012-11-28 华中科技大学 基于颈部超声图像的主颈动脉血管提取和厚度测量方法
CN103479399A (zh) * 2013-10-11 2014-01-01 华北电力大学（保定） 一种血管内超声图像序列中钙化斑块帧的自动检索方法
CN105072980A (zh) * 2012-12-12 2015-11-18 光学实验室成像公司 用于自动确定血管内腔轮廓的方法及装置
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Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1917576A (zh) * 2006-08-30 2007-02-21 蒲亦非 数字图像的分数阶微分滤波器
CN101802871A (zh) * 2007-09-17 2010-08-11 皇家飞利浦电子股份有限公司 用于测量图像中对象的测径器
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