[go: up one dir, main page]

WO2018176319A1 - Procédé et dispositif d'analyse d'image ultrasonore - Google Patents

Procédé et dispositif d'analyse d'image ultrasonore Download PDF

Info

Publication number
WO2018176319A1
WO2018176319A1 PCT/CN2017/078782 CN2017078782W WO2018176319A1 WO 2018176319 A1 WO2018176319 A1 WO 2018176319A1 CN 2017078782 W CN2017078782 W CN 2017078782W WO 2018176319 A1 WO2018176319 A1 WO 2018176319A1
Authority
WO
WIPO (PCT)
Prior art keywords
ultrasound image
area
image
target
initial contour
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.)
Ceased
Application number
PCT/CN2017/078782
Other languages
English (en)
Chinese (zh)
Inventor
刘磊
秦文健
温铁祥
辜嘉
李凌
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.)
Shenzhen Institute of Advanced Technology of CAS
Original Assignee
Shenzhen Institute of Advanced Technology of CAS
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 Shenzhen Institute of Advanced Technology of CAS filed Critical Shenzhen Institute of Advanced Technology of CAS
Publication of WO2018176319A1 publication Critical patent/WO2018176319A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/149—Segmentation; Edge detection involving deformable models, e.g. active contour models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/155—Segmentation; Edge detection involving morphological operators
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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

Definitions

  • the present invention relates to the field of image processing technologies, and in particular, to a method and an apparatus for analyzing an ultrasound image.
  • CAD Computer-aided Diagnosis
  • the computer-aided diagnosis system is increasingly Used in clinical diagnosis.
  • diagnostic ultrasound results are typically provided by analyzing medical ultrasound images; for example, for ultrasound images of breast tumors, the ultrasound image is analyzed using a CAD system to provide diagnostic results for breast tumors, tumor location, or size.
  • CAD Computer-aided diagnosis systems
  • how to segment the lesion area (such as the tumor area) from the ultrasound image is a key link.
  • the current segmentation algorithm for ultrasound images is generally semi-automatic, that is, the clinician needs to manually select a representative point of the region of interest or the region of interest, and then perform segmentation by computer.
  • the semi-automatic segmentation method reduces the automation performance of the CAD system and cannot meet the needs of the massive medical image processing that is increasingly occurring in the clinic.
  • most of the automatic segmentation algorithms introduce some a priori constraint information such as shape, texture and spatial relative position to achieve fully automatic segmentation.
  • the results may be different, so it is difficult to accurately extract the prior constraint information of the breast tumor, thus affecting the analysis. The accuracy of the results.
  • the embodiment of the invention provides a method and a device for analyzing an ultrasonic image, which can improve the automation level and speed of the segmentation while ensuring the accuracy of the segmentation result.
  • An embodiment of the present invention provides an analysis method of an ultrasound image for segmenting a target region from the ultrasound image, the analysis method comprising: preprocessing the ultrasound image; and the pre-processed ultrasound An initial contour of the target area is extracted from the image; an initial contour of the target area is evolved to obtain an accurate boundary of the target area.
  • the step of pre-processing the ultrasound image includes: selecting an effective region of the ultrasound image; and performing denoising processing on the selected effective region.
  • the step of extracting the initial contour of the target region from the pre-processed ultrasound image comprises: pre-segmenting the pre-processed ultrasound image to obtain a pre-segmented image;
  • the pre-segmented image sequentially performs a series of processes, wherein the series of processes includes at least one of: morphological processing, void filling, and removing an area connected to the boundary; extracting through the series of processed images An enclosed area; determining an initial contour of the target area based on the size of the extracted closed area.
  • the step of pre-segmenting the pre-processed ultrasound image includes: processing the pre-processed ultrasound image by using a maximum inter-class variance method to obtain a first threshold; a threshold value, a foreground image is segmented from the pre-processed ultrasound image; the foreground image is processed by the maximum inter-class variance method to obtain a second threshold; and the second threshold is used
  • the pre-processed ultrasound image is pre-segmented to obtain the pre-segmented image.
  • the step of pre-segmenting the pre-processed ultrasound image includes: processing the pre-processed ultrasound image by using a maximum inter-class variance method to obtain a first threshold; a threshold value for pre-segmenting the pre-segmented ultrasound image to obtain the pre-segmented image; the step of sequentially performing a series of processing on the pre-segmented image includes: The shape-processed processing is performed a plurality of times on the pre-segmented image.
  • the step of determining an initial contour of the target region according to the size of the extracted closed region includes: for the extracted closed region, retaining n closed regions having the largest area, where n is an integer greater than 0 And determining an initial contour of the target area according to the size relationship of the n closed areas.
  • the step of evolving the initial contour of the target area comprises: using an area-based active contour model to evolve an initial contour of the target area.
  • the edge of the target away from the evolution curve is detected by the edge indication function to guide the evolution curve to stop at the boundary of the target contour, thereby improving the convergence speed;
  • the edge indication function is:
  • ⁇ (0,1) is the control coefficient of the image boundary intensity field versus evolution velocity
  • ⁇ is the proportional constant
  • R is the edge strength obtained by the exponentially weighted average ratio operator.
  • An embodiment of the present invention provides an ultrasound image analysis apparatus for segmenting a target area from the ultrasound image, the analysis apparatus comprising: a preprocessing module for preprocessing the ultrasound image; an initial contour An extraction module, configured to extract an initial contour of the target region from the pre-processed ultrasound image; and an evolution module configured to evolve an initial contour of the target region to obtain an accuracy of the target region boundary.
  • the automation level and speed of the segmentation can be improved while ensuring the accuracy of the segmentation result.
  • FIG. 1 is a flow chart showing an embodiment of an analysis method of an ultrasonic image of the present invention
  • FIG. 2a is a schematic flow chart of an embodiment of step 101 in FIG. 1;
  • Figure 2b is a schematic illustration of an embodiment of an original ultrasound image and an active area
  • FIG. 3 is a schematic flow chart of an embodiment of step 102 in FIG. 1;
  • Figure 4a and Figure 4b are schematic diagrams showing the evolution results of the conventional CV model and the improved CV model, respectively;
  • Fig. 5 is a view showing the configuration of an embodiment of an ultrasonic image analyzing apparatus of the present invention.
  • FIG. 1 is a schematic flow chart of an embodiment of an analysis method of an ultrasonic image of the present invention.
  • the method for analyzing the ultrasound image can be integrated into the CAD system for segmenting the target region from the ultrasound image, thereby facilitating the CAD system to provide auxiliary diagnostic data.
  • the ultrasound image may be, for example, a breast tumor ultrasound image, but the invention is not limited thereto.
  • the analysis method of the ultrasonic image includes the following steps:
  • Step 101 Preprocess the ultrasound image.
  • the ultrasound image can be generated, for example, by an ultrasound device and then input to a CAD system for processing.
  • Step 102 Extract an initial contour of the target area from the pre-processed ultrasound image.
  • the target area may be, for example, a tumor area in an ultrasound image of a breast tumor.
  • Step 103 Evolve the initial contour of the target area to obtain an accurate boundary of the target area.
  • the automation level and speed of the segmentation can be improved while ensuring the accuracy of the segmentation result.
  • FIG. 2a it is a schematic flowchart of an embodiment of step 101 in FIG. It includes the following steps:
  • Step 201 Select an effective area of the ultrasound image.
  • the manual cutting method may be used to select the effective area.
  • the so-called manual cutting method manually uses the mouse to draw a box to trim the original image, thereby removing the surrounding irrelevant area and retaining the center of the image. region.
  • the program setting method can also be used to select the effective area; because the image size and the frame size collected by the ultrasound device of the same model are fixed, it is possible to reserve only the pixels of a specific size area in the image by program setting, thereby ignoring the surrounding area. Irrelevant information.
  • the original ultrasound image and the image of the effective region obtained after the processing in step 201 wherein the left image in FIG. 2b is the original ultrasound image, and the image on the right is the image of the effective region.
  • Step 202 Perform denoising processing on the selected effective area.
  • the image may be filtered by a SRAD (Speckle Reducing Anisotropic Diffusion) model to remove speckle noise.
  • SRAD Styreducing Anisotropic Diffusion
  • the SRAD model can effectively filter out the ultrasonic image speckle noise, simplify the image structure, improve the image quality, and maintain the detail information at the edge of the image.
  • FIG. 3 it is a schematic flowchart of an embodiment of step 102 in FIG. 1, which includes the following steps:
  • Step 301 Perform pre-segmentation on the pre-processed ultrasound image to obtain a pre-segmented image.
  • the pre-segmented ultrasound image is pre-segmented by using two maximum inter-class variance methods. It should be noted that the details of the maximum inter-class variance method are familiar to those skilled in the art, and therefore are not described herein.
  • the pre-processed ultrasound image is first processed using a maximum inter-class variance method to obtain a first threshold (ie, a global threshold). Then, according to the first threshold, the preprocessed ultrasound image is divided into a background image and a foreground image, wherein the foreground image may include a target region. Then, the foreground image is processed using the maximum inter-class variance method to obtain a second threshold (optimum threshold). Finally, the pre-segmented ultrasound image is pre-segmented by using the second threshold to obtain a pre-segmented image. It should be noted that the pre-segmented image may be a binarized image.
  • the maximum inter-class variance method may be performed only once, that is, the pre-processed ultrasound image is first processed by using a maximum inter-class variance method to obtain a global threshold. Then use this The global threshold is used to pre-segment the pre-processed ultrasound image to obtain a pre-segmented image.
  • step 301 when the maximum inter-class variance method is used twice, the mis-segmentation can be reduced.
  • the maximum inter-class variance method in order to improve the accuracy, the number of morphological processing can be increased in a series of subsequent processing.
  • Step 302 Perform a series of processing on the pre-segmented image, wherein the series of processing includes at least one of the following: morphological processing, hole filling, and removing the area connected to the boundary.
  • the morphological processing may mean that the binarized image obtained after the pre-segmentation often contains some noise fragments caused by speckle noise. Therefore, in order to filter out small burrs and isolated points in the image, cut off the elongated connection, and smooth the edge of the lesion (such as a tumor), the binarized image (ie, the pre-segmented image) is subjected to morphological processing. At the same time, in order to achieve better results, larger structural elements can be used for corrosion and smaller structural elements for expansion.
  • the area connected to the boundary in the binarized image is often mis-segmented by artifacts, not the real lesion area, so in order to eliminate the influence, the area connected to the boundary in the binarized image is removed.
  • Step 303 Extract a closed area in the series of processed images.
  • some closed connected regions (generally black) appear in the image, which are tumor candidate regions.
  • all closed areas are extracted and can be sorted according to the size of each closed area.
  • Step 304 Determine an initial contour of the target area according to the size of the extracted closed area.
  • n closed areas having the largest area are reserved, wherein n is an integer greater than 0; and the target area is determined according to the size relationship of the n closed areas The initial outline of the field. It should be noted that if there is only one closed area, the closed area is directly used as the initial outline of the target area.
  • the two closed areas with the largest area are extracted. If the difference between the area of the larger closed area and the area of the smaller closed area is less than a preset threshold, the two closed areas are simultaneously determined as targets. The initial contour of the region; if the difference between the area of the larger enclosed area and the area of the smaller enclosed area is greater than a predetermined threshold, only the larger enclosed area is determined as the initial contour of the target area.
  • the preset threshold may be half of the area of the larger closed area or other values.
  • the initial contour of the target area can be automatically extracted without manually setting the initial contour, so that the automation level of the CAD system can be improved.
  • the region-based active contour model is mainly used to evolve the initial contour of the target region, thereby obtaining an accurate boundary of the target region.
  • the region-based active contour model also known as CV model
  • CV model is an energy model based on image global region information proposed by Chan and Vese in 2001.
  • the core idea is to use segmentation constants to approximate various parts of the image.
  • the variational method is used to introduce the level set to establish the equation, and the differential method is used for numerical calculation.
  • the evolution problem of the boundary contour is transformed into the energy minimization problem.
  • the CV model is improved to improve the convergence speed.
  • the embodiment of the present invention detects the far-away evolution by adding an edge indication function (specifically, replacing the Dirac function in the traditional CV mode with an edge indication function).
  • the target edge of the curve is used to guide the evolution curve to stop at the boundary of the target contour, thereby increasing the convergence speed.
  • the edge indication function can be expressed as:
  • ⁇ (0,1) is the control coefficient of the image boundary intensity field versus evolution rate
  • ⁇ is the proportional constant
  • R is the edge intensity obtained by the ratio of exponentially weighted averages (ROEWA) operator.
  • the gradient value adopted by the ROEWA operator instead of the general image segmentation algorithm is that the speckle noise in the ultrasound image is multiplicative noise, and the ROEWA operator can better adapt to the image in which the noise is a multiplicative model.
  • the constructed edge indication function is a monotonically decreasing function. When R ⁇ , the value of g(R) ⁇ 0, g tends to be 0, indicating that the closer to the true boundary contour of the lesion. Therefore, the edge indication function itself can also speed up the convergence of the CV model.
  • step 103 of FIG. 1 by improving the conventional CV model, the convergence speed of the CV model can be improved, thereby speeding up the processing.
  • the split time comparison table of the improved CV model and the conventional CV model at different iteration times It can be seen from the table that the segmentation time of the improved CV model is significantly faster than the traditional CV model.
  • the analysis device 500 can be used to segment a target region from an ultrasound image.
  • the analysis device 500 includes: a pre-processing module 501 for pre-processing the ultrasound image; and an initial contour extraction module 502, configured to extract an initial of the target region from the pre-processed ultrasound image An outline; and an evolution module 503, configured to evolve an initial contour of the target area to obtain an accurate boundary of the target area.
  • the ultrasonic image analyzing device of the embodiment of the invention can improve the automation level and speed of the segmentation while ensuring the accuracy of the segmentation result.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Ultra Sonic Daignosis Equipment (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

L'invention concerne un procédé et un dispositif d'analyse d'image ultrasonore permettant de segmenter une zone cible à partir d'une image ultrasonore. Le procédé d'analyse consiste à : prétraiter (101) l'image ultrasonore ; extraire un contour initial de la zone cible à partir de l'image ultrasonore prétraitée (102); et développer le contour initial de la zone cible afin d'acquérir une limite précise de la zone cible (103). Le procédé et le dispositif permettent d'amélioerr le niveau d'automatisation et la vitesse de la segmentation tout en garantissant la précision d'un résultat de segmentation.
PCT/CN2017/078782 2017-03-27 2017-03-30 Procédé et dispositif d'analyse d'image ultrasonore Ceased WO2018176319A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710186624.0A CN107169975B (zh) 2017-03-27 2017-03-27 超声图像的分析方法及装置
CN201710186624.0 2017-03-27

Publications (1)

Publication Number Publication Date
WO2018176319A1 true WO2018176319A1 (fr) 2018-10-04

Family

ID=59848781

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/078782 Ceased WO2018176319A1 (fr) 2017-03-27 2017-03-30 Procédé et dispositif d'analyse d'image ultrasonore

Country Status (2)

Country Link
CN (1) CN107169975B (fr)
WO (1) WO2018176319A1 (fr)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108013904B (zh) * 2017-12-15 2020-12-25 无锡祥生医疗科技股份有限公司 心脏超声成像方法
CN111093525A (zh) * 2018-08-07 2020-05-01 温州医科大学 光学相干断层图像处理方法
CN109602304A (zh) * 2018-11-30 2019-04-12 余姚市腾翔电子科技有限公司 人体参数解析系统
CN112233122B (zh) * 2019-06-28 2025-07-15 深圳市理邦精密仪器股份有限公司 超声图像中对象提取、测量方法及装置
CN112419222B (zh) * 2019-08-22 2024-12-31 深圳市理邦精密仪器股份有限公司 超声胎儿颈部透明层图像分割、检测方法及装置
CN119360048A (zh) * 2024-12-26 2025-01-24 深圳市睿达科技有限公司 一种轮廓的识别方法、系统、终端设备及介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1793350A1 (fr) * 2005-12-01 2007-06-06 Medison Co., Ltd. Système de formation d'image à ultrasons pour former une image en 3D à l'aide d'ultrasons d'un objet cible
CN101599174A (zh) * 2009-08-13 2009-12-09 哈尔滨工业大学 基于边缘和统计特征的水平集医学超声图像区域轮廓提取方法
CN101702236A (zh) * 2009-10-30 2010-05-05 无锡景象数字技术有限公司 一种多目标前景分割方法
CN106340022A (zh) * 2015-07-08 2017-01-18 中国科学院沈阳自动化研究所 一种基于区域相关性的图像分割方法

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104637047B (zh) * 2013-11-13 2018-07-06 北京慧眼智行科技有限公司 一种图像处理方法及装置

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1793350A1 (fr) * 2005-12-01 2007-06-06 Medison Co., Ltd. Système de formation d'image à ultrasons pour former une image en 3D à l'aide d'ultrasons d'un objet cible
CN101599174A (zh) * 2009-08-13 2009-12-09 哈尔滨工业大学 基于边缘和统计特征的水平集医学超声图像区域轮廓提取方法
CN101702236A (zh) * 2009-10-30 2010-05-05 无锡景象数字技术有限公司 一种多目标前景分割方法
CN106340022A (zh) * 2015-07-08 2017-01-18 中国科学院沈阳自动化研究所 一种基于区域相关性的图像分割方法

Also Published As

Publication number Publication date
CN107169975A (zh) 2017-09-15
CN107169975B (zh) 2019-07-30

Similar Documents

Publication Publication Date Title
CN107169975B (zh) 超声图像的分析方法及装置
US7756316B2 (en) Method and system for automatic lung segmentation
US10229488B2 (en) Method and system for determining a stage of fibrosis in a liver
US9940706B2 (en) Image processing apparatus, image processing method and storage medium for extracting a lesion having a ground glass opactiy
JP4767957B2 (ja) 結合空間強度尤度比検定を使用する体積腫瘍分断
US8831328B2 (en) Method and system for segmenting a brain image
US20080040083A1 (en) System and Method for Solid Component Evaluation in Mixed Ground Glass Nodules
CN112907581A (zh) 一种基于深度学习的mri多类脊髓肿瘤分割方法
US7480401B2 (en) Method for local surface smoothing with application to chest wall nodule segmentation in lung CT data
US8073217B2 (en) Structure segmentation via MAR-cut
CN113269764A (zh) 颅内动脉瘤自动分割方法、系统、样本处理及模型训练方法
Sahakyan et al. Segmentation of the breast region in digital mammograms and detection of masses
Khordehchi et al. Automatic lung nodule detection based on statistical region merging and support vector machines
CN116912823A (zh) 一种基于深度学习的肾脏病理切片细胞识别方法及系统
KR102332032B1 (ko) 복부장기 자동분할을 이용한 복부장기 볼륨 변화 분석에 기반한 복부장기 상태 판단 방법
CN112258449A (zh) 一种基于结节特征的快速结节匹配方法
KR101474162B1 (ko) 흉부 컴퓨터 단층 영상의 간유리음영 결절 자동 분할 시스템 및 그 분할 방법
CN106778793B (zh) 一种影像特征的可重复性测量方法及装置
WO2018098697A1 (fr) Procédé et dispositif de mesure de répétabilité de caractéristique d'image
US20070049840A1 (en) System and method for determining a size of an airway lumen and a thickness of an airway wall
RU2656761C1 (ru) Способ и система сегментации изображений очагов легких
US7835555B2 (en) System and method for airway detection
CN118071621A (zh) 基于导向滤波和梯度增强的多层荧光图像融合与信号点精确提取及计数方法及其应用
Macenko et al. Lesion detection using morphological watershed segmentation and modelbased inverse filtering
CN112634280B (zh) 基于能量泛函的mri图像脑肿瘤分割方法

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17904275

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17904275

Country of ref document: EP

Kind code of ref document: A1