WO2022034955A1 - Appareil pour détecter un ulcère cornéen sur la base d'un traitement d'image et procédé associé - Google Patents

Appareil pour détecter un ulcère cornéen sur la base d'un traitement d'image et procédé associé Download PDF

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WO2022034955A1
WO2022034955A1 PCT/KR2020/011726 KR2020011726W WO2022034955A1 WO 2022034955 A1 WO2022034955 A1 WO 2022034955A1 KR 2020011726 W KR2020011726 W KR 2020011726W WO 2022034955 A1 WO2022034955 A1 WO 2022034955A1
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region
ulcer
pixel
interest
area
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PCT/KR2020/011726
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English (en)
Korean (ko)
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임진혁
김대원
조경진
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단국대학교 산학협력단
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    • 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
    • A61B3/0016—Operational features thereof
    • A61B3/0025—Operational features thereof characterised by electronic signal processing, e.g. eye models
    • 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
    • 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
    • A61B3/14—Arrangements specially adapted for eye photography
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30041—Eye; Retina; Ophthalmic
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30096—Tumor; Lesion

Definitions

  • the present invention relates to an apparatus and method for detecting corneal ulcers based on image processing, and more particularly, to a cornea for segmenting a region of interest from an original image captured by the cornea, and detecting the ulcer included in the region of interest in units of pixels It relates to a device for detecting an ulcer and a method therefor.
  • Image segmentation is recognized as an important process for early diagnosis and analysis of diseases in the medical field. It is used for volume measurement and diagnosis for the detection of diseases in areas that cannot be identified with the .
  • corneal ulcers are diseases that occur in the corneal epidermis and can be divided into bacterial and fungal types depending on the route of infection. Diagnosis of classification and subsequent treatment process is made. At this time, the medical staff diagnoses the size of the ulcer with the naked eye, and the problem is that since the ulcer is measured by subjective judgment, there is a problem that objective data accurately measuring the ulcer area cannot be presented to the patient.
  • An object of the present invention is to provide a corneal ulcer detection apparatus and method for segmenting a region of interest from an original image taken by the cornea and detecting the ulcer included in the region of interest in units of pixels.
  • an image input unit for receiving an ocular image obtained by photographing the cornea of a subject, a region of interest from the input ocular image
  • a preprocessor that performs image preprocessing so that the boundary between the region corresponding to the background and the region corresponding to the ulcer is distinguished by using the RGB values of each pixel included in the extracted region of interest; and pixels in the region of interest
  • An ulcer area detector that derives a threshold value according to the value distribution and detects the ulcer area by applying a flood-fill algorithm that expands the area corresponding to the ulcer around the reference pixel using the derived threshold value; and an output unit for generating a contour line for the detected ulcer region and displaying the generated contour line on the original of the eye image.
  • the preprocessor may convert each pixel corresponding to the region of interest to a gray scale form, and then perform histogram smoothing on the region of interest converted to the gray scale form.
  • the preprocessor may convert the grayscale form using the following equation.
  • the preprocessor applies a filter having an arbitrary size to the region of interest to which the histogram smoothing has been applied, arranges pixel values in the filter in ascending or descending order, and then uses an intermediate pixel value of a plurality of pixel values to reduce noise in the region of interest.
  • the boundary of the area corresponding to the ulcer can be simplified by removing and gamma-correcting it.
  • the ulcer region detection unit sets a threshold value according to the distribution of pixel values in the region of interest using the Otsu algorithm, and then acquires a reference point using coordinate information of each pixel included in the region of interest, and the obtained reference point A region corresponding to the ulcer may be expanded by comparing a pixel difference value between a reference pixel corresponding to , and an adjacent pixel and the threshold value.
  • the ulcer region detection unit clusters pixels included in the region of interest into a region corresponding to an ulcer and a region corresponding to a background according to the Otsu algorithm, and the dispersion value of the region corresponding to the ulcer and the dispersion of the region corresponding to the background A pixel value having a maximum difference in values may be set as the threshold value.
  • the variance value may be calculated using the following equation.
  • any pixel belongs to the area corresponding to the ulcer is the probability that a random pixel belongs to the region corresponding to the background, represents the average of pixel values in the area corresponding to the ulcer, represents the average of pixel values in the area corresponding to the background, represents the average of pixel values of the entire image.
  • the ulcer region detection unit generates an array storing coordinate information of each pixel included in the region of interest, and then performs a histogram to randomly select one pixel from among a plurality of pixels corresponding to the pixel value having the highest frequency. It can be selected and set as the reference pixel.
  • the output unit determines whether an undetected region is included in the detected ulcer region, and if the determination result includes an undetected region, generates an inverted image having the same size as the region of interest and inverted background color, A hole corresponding to an undetected area may be filled by applying a flood-fill algorithm having a threshold value of 0 at a point corresponding to each corner of the inverted image.
  • the output unit alternately performs an erosion operation and a dilation operation corresponding to a morphological operation on the ulcer region to remove noise included in the boundary portion of the ulcer region and smooth the boundary portion, and Canny Edge Detection
  • An algorithm can be used to generate the contour of the ulcer area.
  • the present invention it is possible to increase the objective and accuracy in detecting the ulcer area by expanding the area using the threshold value derived through the Otsu method around the reference point selected by the random reference point selection algorithm.
  • objective results can be derived by minimizing user intervention, and medical treatment results can be provided to medical staff and patients with improved reliability for the purpose of treatment.
  • FIG. 1 is a configuration diagram for explaining a corneal ulcer detection apparatus according to an embodiment of the present invention.
  • FIG. 2 is a flowchart illustrating a corneal ulcer detection method using the corneal ulcer detection apparatus according to an embodiment of the present invention.
  • FIG. 3 is a flowchart for explaining step S230 shown in FIG. 2 .
  • FIG. 4 is a diagram illustrating a state in which a region of interest is converted to grayscale in step S231 shown in FIG. 3 .
  • FIG. 5 is a diagram illustrating a state in which histogram smoothing is applied to a region of interest in step S232 shown in FIG. 3 .
  • FIG. 6 is a diagram illustrating a method of setting an intermediate pixel value in step S233 shown in FIG. 3 .
  • FIG. 7 is a diagram illustrating a state in which noise included in a region of interest is removed by using the intermediate value set in step S233 shown in FIG. 3 .
  • FIG. 8 is a diagram illustrating a state in which gamma correction is applied to a region of interest in step S234 shown in FIG. 3 .
  • step S240 shown in FIG. 9
  • FIG. 10 is a diagram for explaining the structure of an array for storing position information of pixels in step S242 shown in FIG. 9 .
  • 11 and 12 are diagrams for explaining a method of expanding an area according to a flood fill algorithm in step S243 shown in FIG. 9 .
  • FIG. 13 is a flowchart for explaining S250 illustrated in FIG. 2 .
  • FIG. 14 is a diagram illustrating a state in which a corrected image is output by filling an undetected area in step S251 shown in FIG. 13 .
  • 15 is a diagram illustrating a state in which a morphology operation is performed in step S252 shown in FIG. 13 .
  • 16 is a view showing the result of displaying the contour line detected in step S253 shown in FIG. 13 on the original image.
  • FIG. 1 a corneal ulcer detection apparatus according to an embodiment of the present invention will be described in more detail with reference to FIG. 1 .
  • FIG. 1 is a configuration diagram for explaining a corneal ulcer detection apparatus according to an embodiment of the present invention.
  • the corneal ulcer detection apparatus 100 includes an image input unit 110 , a preprocessor 120 , an ulcer region detection unit 130 , and an output unit 140 . .
  • the image input unit 110 receives an ocular image obtained by photographing the cornea of a test subject.
  • the preprocessor 120 simplifies the surface of the eyeball image in order to segment the ulcer region from the received eyeball image.
  • the preprocessor 120 extracts the ROI designated by the user.
  • the preprocessor 120 uses the RGB values of each pixel included in the extracted region of interest to be divided into a region corresponding to the background and an region corresponding to an ulcer in the order of grayscale conversion, histogram smoothing, median value filter, and gamma correction. Perform pre-processing.
  • the ulcer region detection unit 130 detects the ulcer region by applying a flood-fill algorithm to the region of interest on which the pre-processing has been performed.
  • the ulcer area detection unit 130 sets any one randomly selected pixel within the area corresponding to the ulcer as the reference pixel, and corresponds to the ulcer if the difference between the reference pixel and the adjacent pixel is within a preset threshold. expand to the area At this time, the threshold value is calculated by the Otsu algorithm.
  • the ulcer area detection unit 130 detects the ulcer area by repeatedly performing a flood-fill algorithm until the ulcer area is maximally expanded.
  • the output unit 140 generates a contour line for the detected ulcer region, and displays the generated contour line on the original eye image and outputs it. That is, the output unit 140 determines whether an undetected area is included in the detected ulcer area, and when the undetected area is included, the process of filling the undetected area is performed. Then, the ulcer area detection unit 130 smoothes the boundary portion of the ulcer area to generate a contour line, and displays the generated contour line on the original image of the eyeball and outputs it.
  • FIG. 2 is a flowchart illustrating a corneal ulcer detection method using the corneal ulcer detection apparatus according to an embodiment of the present invention.
  • the apparatus 100 for detecting corneal ulcers receives an eye image obtained by photographing the cornea of a test subject ( S210 ).
  • corneal ulcer is a disease that occurs in the epidermis of the cornea, and since the patient cannot directly observe the site of the disease, the diagnosis is made based on images taken with close-up equipment. Accordingly, the image input unit 110 receives the eyeball image photographed through the close-up photographing device.
  • the preprocessor 120 extracts the region set by the user as the region of interest ( S220 ).
  • the corneal ulcer detection method detects a corneal ulcer by performing image segmentation in a state where the user's intervention is minimized. do. Then, the preprocessor 120 extracts the ROI through a region designated by the user or a set threshold value.
  • the preprocessor 120 When the extraction of the region of interest is completed in step S220, the preprocessor 120 performs a preprocessing process on the region of interest (S230).
  • step S230 will be described in more detail with reference to FIGS. 3 to 8 .
  • FIG. 3 is a flowchart for explaining step S230 shown in FIG. 2
  • FIG. 4 is a view showing a state in which the region of interest is converted to grayscale in step S231 shown in FIG. 3
  • FIG. 5 is S232 shown in FIG.
  • It is a diagram showing a state in which the histogram smoothing is applied to the region of interest in step S233
  • FIG. 6 is a diagram showing a method of setting an intermediate pixel value in step S233 shown in FIG.
  • It is a diagram illustrating a state in which noise included in the region of interest is removed using an intermediate value
  • FIG. 8 is a diagram illustrating a state in which gamma correction is applied to the region of interest in step S234 shown in FIG. 3 .
  • the preprocessor 120 converts the region of interest into a gray scale form ( S231 ).
  • the preprocessor 120 converts the region of interest into grayscale using Equation 1 below.
  • the preprocessor 120 multiplies the R, G, and B values of the pixels included in the region of interest by a specific constant to map the pixel values to values between 0 and 255. Then, as shown in FIG. 4 , the region of interest having the color value has the same luminance as the original image, and is converted to grayscale while maintaining the shading of the color.
  • the preprocessor 120 performs a histogram smoothing process on the grayscale-converted region of interest (S232).
  • the value of the pixel included in the region of interest changed to grayscale represents the contrast value.
  • the preprocessor 120 applies histogram smoothing to clearly distinguish the region corresponding to the ulcer and the region corresponding to the background.
  • the preprocessor 120 generates a histogram using pixel values of the ROI. Then, the preprocessor 120 obtains an accumulated value, that is, the frequency count based on the acquired histogram, and normalizes the ROI using the obtained frequency count.
  • the preprocessor 120 uses a mapping function ( ) is calculated.
  • L means the size value of the normalized cumulative histogram range, it has a value of 256 in the grayscale image.
  • the preprocessor 120 maps the ROI to the ROI using the calculated mapping function.
  • the preprocessor 120 may obtain a mapping function of 133.5 by Equation (2). Next, the preprocessor 120 maps all pixels having a pixel value of 48 to a value of 133.5 by using the obtained mapping function. Then, as shown in FIG. 5 , the preprocessor 120 may acquire an image with high contrast.
  • step S232 the preprocessor 120 removes the noise included in the region of interest by using the intermediate pixel value (S233).
  • the region of interest on which the histogram smoothing has been performed may be divided into a region corresponding to an ulcer and a region corresponding to a background according to contrast.
  • the preprocessor 120 removes the noise by applying a median filter to the region of interest.
  • the preprocessor 120 applies a filter having an arbitrary size to the region of interest and sorts pixel values in the filter in ascending or descending order. Then, the preprocessor 120 removes noise in the ROI by using a pixel value located in the middle among the aligned pixel values.
  • the preprocessor 120 performs the pixel values included in the filter, that is, “4”, “4”, “5”, “4”, “3”, “6”, “3”, “1”, “2” " can be arranged in ascending or descending order to obtain a pixel value "4" located in the middle. Then, the preprocessor 120 maps the obtained intermediate pixel value to the region extracted by the filter.
  • the preprocessor 120 repeats the process of applying the intermediate pixel value to obtain an image from which noise has been removed from the contour line of the ulcer region.
  • the preprocessor 120 simplifies the boundary by performing a gamma correction process on the area corresponding to the ulcer ( S234 ).
  • the preprocessor 120 performs a correction operation using a gamma correction filter so that the difference between the region corresponding to the background and the region corresponding to the ulcer included in the region of interest can be more clearly indicated.
  • the preprocessor 120 calculates a gamma value according to Equation 3 below, and non-linearly transforms the line by using the calculated gamma value.
  • the preprocessor 120 simply outputs the bright part of the ROI, that is, the part corresponding to the ulcer, using the calculated gamma value.
  • the ulcer area detection unit detects the ulcer area by applying a flood-fill algorithm (S240).
  • step S240 will be described in more detail with reference to FIGS. 9 to 11B .
  • FIG. 9 is a flowchart for explaining step S240 shown in FIG. 2
  • FIG. 10 is a view for explaining the structure of an arrangement for storing pixel location information in step S242 shown in FIG. 9
  • FIGS. 11 and 12 are It is a diagram for explaining a method of expanding an area according to the flood fill algorithm in step S243 shown in FIG. 9 .
  • the ulcer region detection unit 130 sets a threshold value according to the distribution of pixel values in the region of interest using the Otsu algorithm ( S241 ).
  • the Otsu algorithm is an algorithm that can derive an appropriate threshold according to the distribution of pixel values in an image. Accordingly, the ulcer region detection unit 130 clusters pixels included in the region of interest into two regions according to the Otsu algorithm, and sets a threshold value using a variance value of each clustered region. In detail, the ulcer area detection unit 130 classifies the area corresponding to the ulcer and the area corresponding to the background by using a specific pixel value as a threshold based on the histogram. That is, the threshold value represents a reference value for dividing the area corresponding to the ulcer and the area corresponding to the background. is the optimal threshold.
  • the variance value is calculated using Equation 4 below.
  • any pixel belongs to the area corresponding to the ulcer is the probability that a random pixel belongs to the region corresponding to the background, represents the average of pixel values in the area corresponding to the ulcer, represents the average of pixel values in the area corresponding to the background, represents the average of pixel values of the entire image.
  • the variance value is is interpreted as That is, the ulcer region detection unit 130 sets the reference value when the difference between the average pixel value of the region corresponding to the ulcer and the pixel average value of the region corresponding to the background becomes the maximum and the dispersion value becomes the maximum as the optimal threshold value. .
  • the ulcer area detection unit 130 sets a reference pixel to expand the area corresponding to the ulcer using a flood-fill algorithm (S242).
  • the flood-fill algorithm is an area expansion algorithm that widens the area around a specified reference value in a two-dimensional or more array. It is an algorithm that expands an area, sets the expanded pixel as a pixel again as a reference point, and repeats until the area is no longer expanded. Therefore, in order to perform a flood-fill algorithm, it is necessary to set a starting reference pixel.
  • the ulcer region detector 130 obtains a histogram H in the region of interest.
  • the ulcer region detector 130 creates an array storing coordinate information of each pixel included in the region of interest.
  • the number of pixels stored in each array ( ) is the probability of each pixel value ( ) becomes In the region of interest, the value of the pixel belonging to the region corresponding to the ulcer has the highest probability, except for the region corresponding to the background with low brightness and the illuminated region with high brightness.
  • the ulcer region detection unit 130 is a region corresponding to the background. and lighting area to obtain a new histogram (H') as in Equation (5).
  • the ulcer area detector 130 obtains the pixel value with the highest frequency based on the acquired new histogram H' by using Equation (6).
  • the ulcer region detector 130 randomly selects one pixel from among a plurality of pixels corresponding to the acquired pixel value.
  • step S242 the ulcer area detector 130 expands the area corresponding to the ulcer by comparing the pixel difference value between the selected reference pixel and the adjacent pixel with the threshold value obtained in step S241 ( S243 ).
  • the ulcer region detection unit 130 performs flood-fill in four or eight directions, up, down, left, and right around the selected reference pixel.
  • the ulcer region detection unit 130 detects neighboring pixels with a pixel value of “1” as the center, that is, pixels having “6”, “2”, “3”, and “5”, respectively.
  • a difference value with respect to an adjacent pixel is calculated, and a region is extended in a direction of a pixel having a pixel difference value less than 3 from the reference pixel.
  • the extended pixels correspond to pixels having pixel values “2” and “3” in FIG. 11A .
  • the ulcer region detection unit 130 calculates a difference value from a neighboring pixel in all directions around a reference pixel having a pixel value of “1”, and expands the region in the direction of a pixel having a difference value less than 3.
  • the expanded pixel corresponds to pixels having pixel values “3”, “2” and “1” in FIG. 12 .
  • the ulcer area detection unit 130 repeatedly performs flood-fill until the area is no longer expanded, and detects the ulcer area in a state where the expansion is completed.
  • step S240 the output unit 140 generates a contour line for the detected ulcer region, and displays the generated contour line on the original eye image (S250).
  • step S250 will be described in more detail with reference to FIGS. 13 to 16 .
  • FIG. 13 is a flowchart for explaining S250 shown in FIG. 2
  • FIG. 14 is a view showing a state in which the corrected image is output by filling the undetected area in step S251 shown in FIG. 13
  • FIG. 15 is shown in FIG. 13 .
  • It is a view showing a state in which the morphology operation is performed in step S252 shown
  • FIG. 16 is a view showing the result of displaying the contour line detected in step S253 shown in FIG. 13 on the original image.
  • the output unit 130 first determines whether an undetected area is included in the detected ulcer area, and if the undetected area is included as a result of the determination, the hole corresponding to the undetected area is filled. (S251).
  • the output unit 130 determines whether an undetected area is included in the detected ulcer area.
  • the output unit 130 When it is determined that the undetected region is included, the output unit 130 generates an inverted image having the same size as the ROI and inverted background color. In addition, the output unit 130 expands the area by applying a flood-fill algorithm having a threshold value of 0 at points corresponding to corners of the inverted image. The output unit 130 fills the hole remaining inside the ulcer area by repeatedly performing flood-fill until the area is no longer expanded. Then, as shown in FIG. 14 , the output unit 130 fills in the hole remaining inside the ulcer area, so that a more accurate result image can be derived.
  • the output unit 130 removes noise included in the boundary portion of the ulcer region and performs a smoothing process (S252).
  • the output unit 130 performs a morphological operation of alternately performing an erosion operation for reducing the range of the ulcer area and a dilation operation for expanding the range of the ulcer area, thereby removing noise at the boundary without changing the size of the ulcer area.
  • the output unit 130 removes noise between the area corresponding to the background and the ulcer area by first performing an erosion operation and then an expansion operation. Then, the output unit 130 fills a small gap in the ulcer area by performing an erosion operation after performing an expansion operation. As shown in FIG. 15 , the output unit 130 provides a smoothing effect to the boundary portion of the ulcer region through repeated execution of the operation and fills in minute gaps therein, thereby deriving a result suitable for detecting the boundary of the ulcer region.
  • the output unit 130 detects a contour line from the ulcer region on which the morphological calculation has been completed, and displays the detected canal line on the original eye image (S253).
  • the output unit 130 detects the outline of the ulcer area using a Canny Edge Detection algorithm. Then, the output unit 130 displays the detected outline on the original of the eyeball image and outputs it.
  • the present invention it is possible to increase the objective and accuracy in detecting the ulcer area by expanding the area using the threshold value derived through the Otsu method around the reference point selected by the random reference point selection algorithm.
  • objective results can be derived by minimizing user intervention, and medical treatment results can be provided to medical staff and patients with improved reliability for the purpose of treatment.

Abstract

La présente invention concerne un appareil de détection d'un ulcère cornéen sur la base d'un traitement d'image et un procédé associé. Selon la présente invention, l'appareil comprend : une unité d'entrée d'image pour recevoir l'entrée d'une image de globe oculaire par photographie de la cornée d'une personne examinée ; une unité de prétraitement pour extraire une région d'intérêt à partir de l'image entrée de globe oculaire et pour prétraiter l'image à l'aide de la valeur RVB de chaque pixel inclus dans la région d'intérêt extraite, de sorte que la limite entre une région correspondant à un arrière-plan et une région correspondant à un ulcère est distinguée ; une unité de détection de région d'ulcère pour dériver une valeur seuil en fonction d'une distribution de valeurs de pixel dans la région d'intérêt et pour détecter une région d'ulcère par l'application d'un algorithme de remplissage par diffusion dans lequel la région correspondant à l'ulcère est étendue par rapport à un pixel de référence à l'aide de la valeur seuil dérivée ; et une unité de sortie pour générer un contour de la région d'ulcère détectée et pour marquer le contour généré sur une copie d'origine de l'image de globe oculaire pour l'émettre. Selon la présente invention, une valeur seuil dérivée par l'intermédiaire d'une méthode Otsu est utilisée de telle sorte qu'une région est étendue par rapport à un point de référence sélectionné par un algorithme de sélection de point de référence aléatoire et ainsi la précision de détection peut être augmentée lors de la détection d'une région d'ulcère. De plus, selon la présente invention, un résultat d'objectif peut être dérivé par réduction au minimum l'intervention de l'utilisateur et un résultat de traitement médical présentant une fiabilité améliorée par rapport à un objectif de traitement médical peut être fourni à une équipe médicale et à un patient.
PCT/KR2020/011726 2020-08-11 2020-09-01 Appareil pour détecter un ulcère cornéen sur la base d'un traitement d'image et procédé associé WO2022034955A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101735573B1 (ko) * 2009-11-16 2017-05-15 알콘 렌즈엑스 인코포레이티드 비선형 스캐닝에 의해 수술 타겟 조직을 이미지화하는 방법
JP6363275B2 (ja) * 2017-07-13 2018-07-25 株式会社トプコン 眼科装置
JP2019042172A (ja) * 2017-09-01 2019-03-22 株式会社ニデック 眼科装置、および白内障評価プログラム
JP2019213673A (ja) * 2018-06-12 2019-12-19 株式会社トーメーコーポレーション 眼科装置

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101735573B1 (ko) * 2009-11-16 2017-05-15 알콘 렌즈엑스 인코포레이티드 비선형 스캐닝에 의해 수술 타겟 조직을 이미지화하는 방법
JP6363275B2 (ja) * 2017-07-13 2018-07-25 株式会社トプコン 眼科装置
JP2019042172A (ja) * 2017-09-01 2019-03-22 株式会社ニデック 眼科装置、および白内障評価プログラム
JP2019213673A (ja) * 2018-06-12 2019-12-19 株式会社トーメーコーポレーション 眼科装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
IM JINHYUK, KIM DAEWON: "Corneal Ulcers Detection Using Random Seed Appointment Algorithm", JOURNAL OF THE INSTITUTE OF ELECTRONICS AND INFORMATION ENGINEERS, INSTITUTE OF ELECTRONICS AND INFORMATION ENGINEERS, KR, vol. 56, no. 9, 30 September 2019 (2019-09-30), KR , pages 53 - 66, XP055900426, ISSN: 2287-5026, DOI: 10.5573/ieie.2019.56.9.53 *

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