WO2020103289A1 - Méthode et système d'analyse de données de caractéristique de changement de vaisseau sanguin rétinien hypertendu - Google Patents

Méthode et système d'analyse de données de caractéristique de changement de vaisseau sanguin rétinien hypertendu

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Publication number
WO2020103289A1
WO2020103289A1 PCT/CN2018/124503 CN2018124503W WO2020103289A1 WO 2020103289 A1 WO2020103289 A1 WO 2020103289A1 CN 2018124503 W CN2018124503 W CN 2018124503W WO 2020103289 A1 WO2020103289 A1 WO 2020103289A1
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WIPO (PCT)
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blood vessel
fundus
fundus image
retinal
patient
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PCT/CN2018/124503
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English (en)
Chinese (zh)
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余轮
薛岚燕
王丽纳
林嘉雯
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福州依影健康科技有限公司
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Priority to GB2104436.7A priority Critical patent/GB2591919A/en
Publication of WO2020103289A1 publication Critical patent/WO2020103289A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A61B3/12—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4887—Locating particular structures in or on the body
    • A61B5/489—Blood vessels
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/18—Eye characteristics, e.g. of the iris
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2576/00—Medical imaging apparatus involving image processing or analysis
    • A61B2576/02—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/14—Vascular patterns
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • FIG. 6 is a schematic block diagram of a system for analyzing characteristic data of hypertensive retinal blood vessel changes according to a specific embodiment.
  • the preprocessing includes: green channel selection, median filtering, limited contrast enhancement, and grayscale normalization. The details are as follows: normalization of green channel selection, median filtering, limited contrast enhancement, and grayscale for the fundus image to be examined.
  • a green channel is selected for the color fundus image to be examined, so as to retain and highlight the fundus vessels to the greatest extent.
  • median filtering is performed on the fundus image under the green channel to achieve denoising; to obtain a better blood vessel extraction effect, the denoised image Perform contrast enhancement.
  • a limited contrast enhancement method CLAHE is used in this embodiment.
  • the "analysis and comparison of the retinal blood vessel change characteristic data of the patient at different periods to obtain the patient's fundus screening characteristic data change situation at this time” also includes the step of: preprocessing the fundus image, the The preprocessing includes: green channel selection, median filtering, limited contrast enhancement, and grayscale normalization; establishing a morphological filter to determine the macular fovea and optic disc in the pretreated fundus image; segmenting the preprocessed fundus The retinal vascular network of the image and the main blood vessel; align the fundus image according to the fundus structure parameters and correct the identification of the retinal blood vessel change characteristic data.
  • the fundus structure parameters include: macular fovea, optic disc, and main blood vessel information (the fundus structure parameter is The structural parameters of the fundus image mentioned above); automatically analyze the changes in the retinal abnormal characteristic data.
  • the two fundus images to be analyzed and compared are overlapped, and according to the detection and positioning results of the positions of the optic disc and the macula, the macula and the optic disc are basically overlapped. Then calculate the correlation coefficient of the two based on the divided main vessel binarized image information, and adjust the relative position of the two fundus images appropriately. When the correlation coefficient is the largest, the two fundus images achieve definite alignment. details as follows:
  • the "retinal vascular network segmentation of preprocessed fundus image” further includes the step of segmenting the fundus blood vessel of the fundus image by a saliency model and region optimization method to obtain a fundus blood vessel network And segment the arteries and veins according to the segmented fundus vascular network.
  • the following methods may be used: the following methods may be used: segmentation of the fundus blood vessel of the fundus image through a saliency model and region optimization method to obtain a fundus blood vessel network, and segmentation of arteriovenous veins based on the segmented fundus blood vessel network.
  • Step 1 Color is the most important feature in analyzing the saliency of the image.
  • the quantized parameters of the temporal disc and the fovea of the optic disc may be calculated according to the calibrated optic disc and the macula. Since the absolute distance values of the two of normal people are almost the same, then based on the absolute distance from the temporal side of the optic disc to the fovea of the macula and the diameter of the optic disc, the parameters for subsequent quantitative analysis are obtained, and the obtained data is expressed from the absolute representation Convert to a relative representation, and normalize to form meaningful and comparable data.
  • the feature extraction of fundus blood vessel changes is based on evidence-based medicine to determine the retinal blood vessel change feature data related to the effect of hypertension treatment, that is, FN.
  • the specific description is the same as the above method embodiment, and will not be repeated here.
  • the fundus image analysis and comparison module 6022 is also used to: preprocess the fundus image, the preprocessing includes: green channel selection, median filtering, limited contrast enhancement, and grayscale normalization processing Build a morphological filter to determine the macular fovea and optic disc in the pretreated fundus image; segment the retinal vascular network and main vessels of the preprocessed fundus image; align the fundus image according to the fundus structure parameters and correct the retinal blood vessel changes Identification of feature data.
  • the fundus structure parameters include: macular fovea, optic disc, and main blood vessel information; and automatically analyze changes in the retinal abnormal feature data.
  • the preprocessed fundus image, the macula has extremely low brightness, the shape of the two tends to be round, and the relative distance and position of the two are fixed, so as to realize the morphological filter to detect the circle with extremely low brightness in the fundus image Shaped area, use it as a candidate area of the macula, filter out the wrong candidate area according to the distance and position of the two, and then determine the center position of the macula.
  • the preprocessed fundus image, the main fundus of the fundus has similar grayscale information, and has a high contrast with the background.
  • the main vessel is segmented by using a threshold segmentation method.

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  • Ophthalmology & Optometry (AREA)
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Abstract

La présente invention concerne les domaines de l'analyse d'image, du service de soins de santé et des technologies de traitement de données, et concerne une méthode d'analyse de données de caractéristique de changement de vaisseau sanguin rétinien hypertendu. La méthode consiste : à acquérir une image de fond d'œil d'un patient, à extraire et à identifier des données de caractéristique de changement du vaisseau sanguin rétinien à partir de l'image de fond d'œil, la caractéristique de changement de vaisseau sanguin rétinien comprenant : la limitation du rétrécissement de l'artère rétinienne ; l'analyse et la comparaison des données de caractéristiques de changement de vaisseau sanguin rétinien dans différentes périodes temporelles du patient ; et à acquérir également la situation de changement des données de caractéristiques de criblage de fond d'œil du patient, et à analyser des situations de contrôle de la pression artérielle et d'effets de prévention et thérapeutiques associés de l'hypertension du patient dans la période récente. La présente invention concerne un mécanisme d'excitation puissant permettant d'améliorer le respect par le patient soumis à une intervention d'une thérapie sous-jacente grâce au mode de vie, permettant à un utilisateur d'effectuer périodiquement une détection de tension artérielle et de contrôler sa tension consciencieusement, ce qui présente une grande importance dans l'évaluation de l'effet thérapeutique de l'hypertension et la gestion de maladies chroniques.
PCT/CN2018/124503 2018-11-23 2018-12-27 Méthode et système d'analyse de données de caractéristique de changement de vaisseau sanguin rétinien hypertendu WO2020103289A1 (fr)

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GB2104436.7A GB2591919A (en) 2018-11-23 2018-12-27 Method and system for analyzing hypertensive retinal blood vessel change feature data

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CN201811408427.XA CN111222361B (zh) 2018-11-23 2018-11-23 一种高血压视网膜血管改变特征数据分析的方法和系统
CN201811408427.X 2018-11-23

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CN111899272A (zh) * 2020-08-11 2020-11-06 上海海事大学 基于耦合神经网络和线连接器的眼底图像血管分割方法
CN112330610A (zh) * 2020-10-21 2021-02-05 郑州诚优成电子科技有限公司 一种基于微血管位置角膜内皮细胞计数采集精确定位方法
CN112862804A (zh) * 2021-03-01 2021-05-28 河南科技大学第一附属医院 一种眼底视网膜血管图像处理系统及方法
CN113010722A (zh) * 2021-04-18 2021-06-22 南通大学 一种融合眼底图像的慢病临床研究队列查询系统及方法
CN113222927A (zh) * 2021-04-30 2021-08-06 汕头大学·香港中文大学联合汕头国际眼科中心 一种早产儿视网膜病变附加性病变的自动检查方法
CN115482933A (zh) * 2022-11-01 2022-12-16 北京鹰瞳科技发展股份有限公司 用于对驾驶员的驾驶风险进行评估的方法及其相关产品

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Publication number Priority date Publication date Assignee Title
CN111899272A (zh) * 2020-08-11 2020-11-06 上海海事大学 基于耦合神经网络和线连接器的眼底图像血管分割方法
CN111899272B (zh) * 2020-08-11 2023-09-19 上海海事大学 基于耦合神经网络和线连接器的眼底图像血管分割方法
CN112330610A (zh) * 2020-10-21 2021-02-05 郑州诚优成电子科技有限公司 一种基于微血管位置角膜内皮细胞计数采集精确定位方法
CN112330610B (zh) * 2020-10-21 2024-03-29 郑州诚优成电子科技有限公司 一种基于微血管位置角膜内皮细胞计数采集精确定位方法
CN112862804A (zh) * 2021-03-01 2021-05-28 河南科技大学第一附属医院 一种眼底视网膜血管图像处理系统及方法
CN112862804B (zh) * 2021-03-01 2023-04-07 河南科技大学第一附属医院 一种眼底视网膜血管图像处理系统及方法
CN113010722A (zh) * 2021-04-18 2021-06-22 南通大学 一种融合眼底图像的慢病临床研究队列查询系统及方法
CN113010722B (zh) * 2021-04-18 2023-10-03 南通大学 一种融合眼底图像的慢病临床研究队列查询系统及方法
CN113222927A (zh) * 2021-04-30 2021-08-06 汕头大学·香港中文大学联合汕头国际眼科中心 一种早产儿视网膜病变附加性病变的自动检查方法
CN115482933A (zh) * 2022-11-01 2022-12-16 北京鹰瞳科技发展股份有限公司 用于对驾驶员的驾驶风险进行评估的方法及其相关产品
CN115482933B (zh) * 2022-11-01 2023-11-28 北京鹰瞳科技发展股份有限公司 用于对驾驶员的驾驶风险进行评估的方法及其相关产品

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