WO2020211174A1 - Procédé basé sur la vision artificielle pour traiter une image de photoréfraction excentrique - Google Patents

Procédé basé sur la vision artificielle pour traiter une image de photoréfraction excentrique Download PDF

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Publication number
WO2020211174A1
WO2020211174A1 PCT/CN2019/089777 CN2019089777W WO2020211174A1 WO 2020211174 A1 WO2020211174 A1 WO 2020211174A1 CN 2019089777 W CN2019089777 W CN 2019089777W WO 2020211174 A1 WO2020211174 A1 WO 2020211174A1
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WO
WIPO (PCT)
Prior art keywords
pupil
image
area
processing
present
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PCT/CN2019/089777
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English (en)
Chinese (zh)
Inventor
陈浩
于航
黄锦海
郑晓波
梅晨阳
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温州医科大学
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Publication of WO2020211174A1 publication Critical patent/WO2020211174A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • 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
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G06V10/245—Aligning, centring, orientation detection or correction of the image by locating a pattern; Special marks for positioning
    • 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
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • 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
    • G06V40/19—Sensors therefor

Definitions

  • the invention belongs to an image processing method for ophthalmology, and in particular relates to a method for processing eccentric photographic optometry image based on machine vision.
  • Retinoscopy is the gold standard for refractive errors, with an accuracy of 0.25D. But for children, retinoscopy optometry has its limitations.
  • the handheld vision screener is an instrument specially designed and produced for infants and young children's vision hoof examination in recent years. Its characteristic is: the detection can be carried out while keeping a certain distance from the examinee, without the examinee's high coordination. This makes it not only suitable for people with strong coordination as the previous inspection methods, but also for vision screening for infants and young children and people with poor coordination.
  • the camera uses an infrared light source to project to the retina, and the light reflected by the retina presents different patterns in different refractive states.
  • the camera records the patterns and calculates data such as spherical lens, cylindrical lens and axis position. It can obtain the refractive status, pupil diameter, interpupillary distance, and eye position of both eyes in one measurement, which is convenient for doctors to quickly screen and fully understand the patient's vision development.
  • the principle of eccentric photography refraction using near-infrared light-emitting diodes to form a light source array, the light enters the retina at a specific angle to the pupil of a certain distance, and is reflected by the retina. After being refracted), it is emitted from the pupil area and captured by the camera. Therefore, the refractive state and adjustment state of the examined eye determine the shape and brightness of the light and shadow in the pupil area of the examined eye. Through the processing and analysis of pupil light and shadow images, the corresponding vision detection results are obtained.
  • the image information acquisition device (camera or video camera) collects the eye image, because both eyes are taken at the same time, there are many unwanted interference information in the image besides the eye, which affects the accuracy of the vision detection result.
  • the purpose of the present invention is to overcome the shortcomings and shortcomings of the prior art, and provide a method for processing eccentric photographic refraction images based on machine vision.
  • a method for processing eccentric photographic refraction images based on machine vision is characterized by including the following steps:
  • the present invention first uses the Adaboost strong classifier self-learning method based on the Harr-like rectangle feature to locate the pupil area.
  • the principle of eccentric photography makes the pupils with myopia or hyperopia have uneven brightness, and the wallis uniform light is adopted.
  • the algorithm can maximize the uniform pupil gray value and enhance pupil edge information.
  • perform binarization blob analysis to remove noise and interference areas, and only retain the area where the pupil is, and then use the gray difference method to obtain the precise pupil boundary at the edge of the pupil after binarization, and finally perform a least squares method Ellipse fitting, output pupil area parameters.
  • the invention eliminates interference information, accurately obtains pupil area parameters, and helps to improve the accuracy of optometry for infants and young children and people with poor coordination.
  • Figure 1 is a schematic flow diagram of the present invention
  • Figure 2 is a schematic diagram of the Adaboost learning algorithm training Harr features.
  • a machine vision-based eccentric photorefractive image processing method includes the following steps:
  • Acquire eye images use the camera to continuously collect eye images, and perform image processing on each image according to the following steps;
  • the Haar feature value reflects the gray changes of the image.
  • Haar features are divided into three categories: edge features, linear features, central features and diagonal features, which are combined into feature templates. There are white and black rectangles in the feature template, and the feature value of the template is defined as the sum of white rectangle pixels and minus black rectangle pixels.
  • Integral map is a fast algorithm that can find the sum of pixels in all areas of the image only once through the image, which greatly improves the calculation efficiency of Harr features.
  • the main idea is to store the sum of pixels in the rectangular area formed by the image from the starting point to each point as an element of an array in memory. When calculating the sum of pixels in a certain area, you can directly index the elements of the array without recalculating The sum of pixels in this area speeds up the calculation.
  • the way the integral graph is constructed is the value at position (i, j) Is the original image The sum of all pixels in the upper left corner: .
  • Adaboost learning algorithm uses the Adaboost learning algorithm to roughly locate the human eye area:
  • the basic idea of the Adaboost learning algorithm is to train the model separately, each round of training a new model, at the end of each round, the wrong sample will be calibrated and added to the next round The weights in the new training set, and then the next round of learning to get a new model.
  • the main idea is to compensate for the errors of the previous models based on later models, and to achieve integration by adding new models through continuous iterations. Each time a model is learned, make sure that its classification accuracy is greater than 0.5, which can be misclassified, but cannot be missed. Drop.
  • the gray average value of a gray image reflects its brightness
  • the variance reflects its dynamic range of grayscale changes. Due to ambient light Different from the subject, the pupil brightness and variance of each frame of image are different, and if the subject’s eyes have myopia or hyperopia, the brightness in the same pupil will be different, and the uneven gray value will affect the subsequent The pupil segmentation affects, so the uniform light algorithm can be used to minimize uneven illumination.
  • Wallis filter maps the gray mean and variance of the image to a fixed value, and makes the gray variance and gray mean of different images approximately equal. It is mainly used to transform the gray mean value and standard deviation between different images or at different positions within the image to have approximately equal values, and to enhance the brightness and contrast of the dark areas in the unevenly illuminated images.
  • the specific algorithm formula is as follows:
  • the precise through hole boundary is obtained by the gray difference method.
  • the precise pupil boundary is obtained by the gray difference method.
  • step (7) Using the precise boundary of the pupil obtained in step 6, perform an ellipse fitting based on the least square method to obtain the pupil area parameters.
  • a person of ordinary skill in the art can understand that all or part of the steps in the method of the foregoing embodiments can be implemented by a program instructing related hardware.
  • the program can be stored in a computer readable storage medium.
  • Media such as ROM/RAM, magnetic disk, optical disk, etc.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Human Computer Interaction (AREA)
  • General Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Ophthalmology & Optometry (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Image Analysis (AREA)
  • Eye Examination Apparatus (AREA)
  • Image Processing (AREA)

Abstract

La présente invention concerne un procédé de traitement d'images médicales ophtalmologiques, et concerne spécifiquement un procédé basé sur la vision artificielle pour traiter des images de photoréfraction excentriques. La présente invention utilise un procédé d'auto-apprentissage de classificateur fort Adaboost basé sur des caractéristiques rectangulaires pseudo-Haar pour localiser la zone de la pupille, un principe de photographie excentrique amenant une pupille myope ou une pupille hypermétrope à produire une luminosité irrégulière, et utilise l'algorithme de Wallis pour homogénéiser la valeur d'échelle de gris de la pupille dans la plus grande mesure, ce qui améliore ainsi les informations de contour de la pupille. Ensuite, un analyse de taches de binarisation est effectuée, des points de bruit et des régions d'interférence sont éliminés, retenant uniquement la région où se trouve la pupille, puis une limite précise de la pupille est obtenue à l'aide d'un procédé de différence de niveaux de gris au niveau du contour binarisé de la pupille, et enfin un ajustement elliptique est effectué sur la base du procédé des moindres carrés pour fournir des paramètres de la zone de la pupille. La présente invention élimine les informations d'interférence et obtient avec précision des paramètres de la zone de la pupille, ce qui aide à améliorer la précision d'optométrie pour des nourrissons et des personnes qui ne sont pas coopératives.
PCT/CN2019/089777 2019-04-18 2019-06-03 Procédé basé sur la vision artificielle pour traiter une image de photoréfraction excentrique WO2020211174A1 (fr)

Applications Claiming Priority (2)

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CN201910313589.3 2019-04-18
CN201910313589.3A CN110096978A (zh) 2019-04-18 2019-04-18 基于机器视觉的偏心摄影验光图像处理的方法

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CN112022081B (zh) * 2020-08-05 2023-08-25 广东小天才科技有限公司 一种检测视力的方法、终端设备以及计算机可读存储介质
CN113627231B (zh) * 2021-06-16 2023-10-31 温州医科大学 一种基于机器视觉的视网膜oct图像中液体区域自动分割方法

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