WO2019168381A1 - Appareil de classification automatique de maladie de la peau, et procédé de classification automatique de maladie de la peau - Google Patents

Appareil de classification automatique de maladie de la peau, et procédé de classification automatique de maladie de la peau Download PDF

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WO2019168381A1
WO2019168381A1 PCT/KR2019/002438 KR2019002438W WO2019168381A1 WO 2019168381 A1 WO2019168381 A1 WO 2019168381A1 KR 2019002438 W KR2019002438 W KR 2019002438W WO 2019168381 A1 WO2019168381 A1 WO 2019168381A1
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image
skin
pixel values
pixel
glcm
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PCT/KR2019/002438
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English (en)
Korean (ko)
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김명남
조진호
구정모
나승대
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경북대학교 산학협력단
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    • 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
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/40—Image enhancement or restoration using histogram techniques
    • 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
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/698—Matching; Classification
    • 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
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • 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
    • G06T2207/30088—Skin; Dermal

Definitions

  • the present invention relates to an apparatus and method for automatically classifying skin diseases, and more particularly, from a pixel characteristic of an image photographing a skin disease, a gray degree coexistence matrix (GLCM) and a gray degree continuous length matrix (GLRLM) converted from an image.
  • the present invention relates to an automatic skin disease classification device and an automatic skin disease classification method that can automatically classify skin diseases such as melanoma based on the extracted GLCM and GLRLM features.
  • senile skin disease is difficult to detect early, and when the treatment is missed progress to a malignant disease may be difficult to treat.
  • Melanoma is one of the most common diseases of the senile skin disease, and initially has a modality similar to nevus. Because of this, melanoma is difficult to detect early in the onset and is often mistaken for nevus.
  • Melanoma is a disease in which malignant tumors spread from the dermal layer of the skin to the muscles. Unlike nevus, the symptoms appear in the affected areas such as vascular malformation, melanin pigmentation and malformation. The most malignant malignant melanoma has no noticeable symptoms such as itching or pain, and it is characterized by ordinary black spots.
  • skins such as melanoma and the like are based on pixel features of an image of a skin disease image, a GLCM feature extracted from a gray degree coexistence matrix (GLCM) and a gray degree continuous length matrix (GLRLM) converted from an image, and a GLRLM feature.
  • An apparatus and method for automatically classifying skin diseases capable of automatically classifying diseases with high accuracy, and a recording medium.
  • a method for automatically classifying skin diseases comprising: calculating a histogram of brightness values of pixels in an image of a skin lesion, extracting pixel characteristics by statistically analyzing the histogram; Converting pixel values of the image into a gray-level co-occurrence matrix (GLCM) representing adjacency between the pixel values; Extracting a GLCM feature based on pixel values of the gray degree co-occurrence matrix; Converting pixel values of the image into a gray level run length matrix (GLRLM) representing a continuous length of the pixel values; Extracting a GLRLM feature based on pixel values of the gray continuous length matrix; And classifying the skin disease by applying the pixel feature, the GLCM feature, and the GLRLM feature to a machine learning model.
  • GLCM gray-level co-occurrence matrix
  • GLRLM gray level run length matrix
  • the pixel characteristic may include an average, standard deviation, skew, kottosis, entropy, and root mean square of pixel values of the image.
  • the GLCM feature is characterized by auto-correlation, contrast, correlation, dissimilarity, energy, and homogeneity of pixel values of the grayscale co-occurrence matrix. It may include.
  • the GLRLM features include short run emphasis (SRE), long run emphasis (LRE), gray level non-uniformity (GLNU), run percentage (RPN), and RLNU (run) extracted based on pixel values of the gray-scale continuous length matrix. Length Non-Uniformity) and High Gray Level Run Emphasis (HGLRE).
  • the machine learning model may include a support vector machine (SVM) model.
  • SVM support vector machine
  • a method for automatically classifying skin diseases may include: obtaining a dermatoscope image by photographing the skin lesion using dermoscopy; Converting the dermal image into a binary image and detecting an object of a smaller size than the skin lesion in the binary image; Removing noise including hair and skin keratin based on the size difference between the skin lesion and the object; And extracting a region of interest including only skin lesions by converting pixel values of normal skin to 0 in the dermal image.
  • the machine learning model may be acquired by learning the pixel feature, the GLCM feature, and the GLRLM feature based on learning image data including melanoma images and nevus images.
  • the method may further include generating.
  • the step of classifying the skin disease may include classifying the skin lesion into any one of melanoma and nevus based on the learned machine learning model.
  • a computer-readable recording medium having a program recorded thereon for executing the method for automatically classifying skin diseases is provided.
  • the at least one processor calculates a histogram of the brightness values of the pixels in the image of the skin lesion, the histogram Statistical analysis to extract pixel features; Converting pixel values of the image into a Gray-Level Co-occurrence Matrix (GLCM) representing adjacency between the pixel values; Extract a GLCM feature based on pixel values of the gray degree co-occurrence matrix; Converting pixel values of the image into a gray level run length matrix (GLRLM) representing a continuous length of the pixel values; Extract a GLRLM feature based on pixel values of the gray continuous length matrix; And apply the pixel feature, the GLCM feature, and the GLRLM feature to a machine learning model to classify skin diseases.
  • GLCM Gray-Level Co-occurrence Matrix
  • GLRLM gray level run length matrix
  • the at least one processor is further configured to convert a dermoscopic image of the skin lesion by a dermoscopy into a binary image; Detecting an object of a smaller size than the skin lesion in the binary image; Removing noise including hair and dead skin cells based on the size difference between the skin lesion and the object; The pixel value of the normal skin may be converted to 0 in the dermoscopic image to extract an ROI including only skin lesions.
  • the at least one processor generates the machine learning model by learning the pixel feature, the GLCM feature and the GLRLM feature using training image data including melanoma images and nevus images;
  • the skin lesion may be classified into one of melanoma and nevus based on the machine learning model trained using the training image data.
  • a GLCM feature and a GLRLM feature extracted from a gray degree coexistence matrix (GLCM) and a gray degree continuous length matrix (GLRLM) converted from an image
  • GLCM gray degree coexistence matrix
  • GLRLM gray degree continuous length matrix
  • FIG. 1 is a schematic flowchart of a method for automatically classifying skin diseases according to an exemplary embodiment of the present invention.
  • FIG. 2 is a block diagram of an automatic skin disease classification apparatus according to an embodiment of the present invention.
  • FIG. 3 is a flowchart of a method for automatically classifying skin diseases according to an exemplary embodiment of the present invention.
  • step S30 of FIG. 3 is a detailed flowchart of step S30 of FIG. 3.
  • 5 is an exemplary diagram of size distribution diagrams of objects extracted from a dermoscopic image.
  • FIG. 6 is an exemplary view of a process of preprocessing a dermoscopic image according to an embodiment of the present invention.
  • FIG. 7 is a block diagram of a skin disease classification unit constituting an automatic skin disease classification apparatus according to an embodiment of the present invention.
  • GLCM gray degree co-occurrence matrix
  • FIG 9 is an exemplary diagram of a gray scale continuous length matrix (GLRLM) converted from an image according to an embodiment of the present invention.
  • GLRLM gray scale continuous length matrix
  • FIG. 10 is an exemplary diagram of melanoma images (a) and nevus images (b) used in machine learning according to an embodiment of the present invention.
  • FIG. 11 is a diagram showing skin disease classification accuracy performance of the automatic skin disease classification method according to an embodiment of the present invention.
  • ' ⁇ part' is a unit for processing at least one function or operation, and may mean, for example, a hardware component such as software, FPGA, or ASIC. However, ' ⁇ ' is not meant to be limited to software or hardware. ' ⁇ Portion' may be configured to be in an addressable storage medium or may be configured to play one or more processors.
  • ' ⁇ ' means components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, procedures, and subs. Routines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables.
  • the functions provided by the component and the ' ⁇ ' may be performed separately by the plurality of components and the ' ⁇ ', or may be integrated with other additional components.
  • FIG. 1 is a schematic flowchart of a method for automatically classifying skin diseases according to an exemplary embodiment of the present invention.
  • S1 dermoscopy image
  • ROI region of interest
  • Extract S3
  • image features related to skin diseases such as melanoma are extracted from the region of interest of the image (S4 to S5).
  • Image features include pixel features extracted by first-order statistical analysis using information of the pixel itself, and gray-level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM) converted from an image into a matrix form. It may include GLCM features and GLRLM features extracted from.
  • the extracted features may be applied to machine learning discrimination algorithms such as a Support Vector Machine and used to classify skin diseases. According to an embodiment of the present invention, in particular, it is possible to accurately classify nevus and melanoma correctly.
  • FIG. 2 is a block diagram of an automatic skin disease classification apparatus according to an embodiment of the present invention.
  • the automatic skin disease classification apparatus 100 according to an embodiment of the present invention, the control unit 110, the learning unit 120, the image acquisition unit 130, the image preprocessor 140, skin disease classification The unit 150 and the storage unit 160 may be included.
  • the controller 110 includes at least one processor and controls the learner 120, the image acquirer 130, the image preprocessor 140, the skin disease classifier 150, and the storage 160. Execute the function (program) for automatic disease classification.
  • the learner 120 may learn features of skin disease using learning image data including melanoma images and nevus images (S10).
  • the learner 120 may extract features of melanoma using an image of a recognized skin disease database and generate a machine learning model for classifying melanoma and nevus.
  • the learner 120 may calculate a histogram from pixel values of an image of a skin disease database, and extract pixel features by statistically analyzing the histogram.
  • the learner 120 may convert the images of the skin disease database into GLCM and GLRLM, respectively, and then extract GLCM features and GLRLM features from GLCM and GLRLM.
  • the learner 120 learns the extracted pixel features, GLCM features, and GLRLM features to generate a machine learning model.
  • the machine learning model generated by the learner 120 may be stored in the storage 160 to be used for classification of skin diseases.
  • the image acquirer 130 may acquire an image by photographing a skin lesion part (S20).
  • the image acquirer 130 may include dermoscopy for acquiring a dermoscopic image by photographing a skin lesion suspected of melanoma.
  • the image acquired by the image acquirer 130 may be stored in the storage 160.
  • the focus of the image and the location of the disease may be different within the image for each specialist.
  • various noises except the disease are generated in the image. This noise can cause errors in extracting features from the image.
  • the image preprocessing unit 140 converts the dermal image to a binary image, removes noise such as hair and keratin (S30), and a region of interest including only skin lesions in the dermal image. Can be extracted.
  • the image preprocessed by the image preprocessor 140 may be stored in the storage 160.
  • the image preprocessor 140 converts the dermoscopic image into a binary image (S32).
  • the Otsu technique may extract a set of objects having similar values from the histogram of pixel values of the dermal image, thereby creating a threshold value for maximizing variance between divided regions.
  • the total variance may be expressed as the sum of the variance in the class and the variance between the classes, and may be expressed as in Equations (1) to (3) below.
  • ⁇ ⁇ 2 is the variance in the class
  • ⁇ c 2 is the variance between the classes
  • ⁇ i is the weight of the probability that the pixel is included in the class i
  • ⁇ is the average value of the class.
  • Binary images may include noise, which is unnecessary information that reduces the accuracy of skin disease classification.
  • the noise is mainly caused by skin conditions such as hair or keratin of the patient. This noise causes errors in extracting the features of the skin lesions and causes problems of deterioration of accuracy.
  • the image preprocessor 140 removes noise based on the binary image obtained in the preprocessing process. Since the noise information is characterized in that the size information is relatively smaller than the skin disease in the image, the image preprocessing unit 140 removes the noise by using the size of the noise, and the region of interest based on the image from which the noise is removed Can be extracted.
  • FIG. 5 is an exemplary diagram of size distribution diagrams of objects extracted from a dermoscopic image.
  • the size of hair, keratin, etc. is smaller than the size of the skin component (e.g., melanoma), so that small-sized objects whose size difference from the skin lesion exceeds the set value are obtained.
  • the skin component e.g., melanoma
  • the image preprocessor 140 may compare the sizes of the components (objects) based on a binary image obtained in the preprocessing process to remove noise such as hair, normal skin, and keratin other than the skin lesion. That is, the image preprocessor 140 may detect objects (eg, hair, keratin, etc.) having a smaller size than the skin lesion in the binary image (S34). The image preprocessor 140 removes noise including hair and skin keratin based on the size difference between the skin lesion and the object (S36).
  • objects eg, hair, keratin, etc.
  • 6 is an exemplary view of a process of preprocessing a dermoscopic image according to an embodiment of the present invention.
  • 6A shows a binary image converted from a dermal image
  • B a noise-removed binary image
  • c a skin lesion boundary
  • d shows a region of interest extraction.
  • Figure 6 (b) it can be confirmed that the noise caused by hair or keratin, etc. is effectively removed.
  • the image preprocessor 140 extracts information on the skin lesion area by using the image from which the noise is removed, and proceeds with image reconstruction to minimize information on normal skin based on the extracted information. do.
  • the image preprocessor 140 may extract the ROI including only the skin lesion by converting the pixel value of the normal skin to 0 in the dermal image (S38).
  • the image preprocessor 140 may extract a boundary line for the skin lesion area of the image and calculate a size of the skin lesion area based on the extracted boundary line.
  • the image preprocessor 140 removes the information on the normal skin from the lesion area based on the calculated size of the skin lesion area, and removes the skin lesion area to prevent the feature from being extracted from the area except the skin lesion area.
  • the region of interest may be extracted by converting the pixel value of the region (normal skin) to 0, as shown in FIG. Accordingly, it is possible to minimize the error of features caused by normal skin and noise.
  • an embodiment of the present invention utilizes all of the pixel information in the dermal image, generates two transformation matrices based on the similarity of pixel clusters, and classifies skin diseases by extracting features from the transformation matrices. do.
  • the skin disease classifying unit 150 may use two transformation matrices GLCM and GLRLM generated from brightness values, histograms, and image information of pixels of the dermal image.
  • various features related to skin disease pixel features, GLCM features, and GLRLM features
  • the extracted features are machine learning models learned by the learning unit 120 (eg, For example, it may be applied to a support vector machine model) to classify skin diseases such as melanoma (S100).
  • the skin disease classifier 150 may include a histogram calculator 151, a pixel feature extractor 152, a GLCM converter 153, a GLCM feature extractor 154, a GLRLM converter 155, and a GLRLM.
  • the feature extractor 156 and the classifier 157 may be included.
  • the histogram calculator 151 calculates a histogram of brightness values of pixels in the ROI of the image of the skin lesion (S40).
  • the pixel feature extractor 152 extracts pixel features by first performing statistical analysis on a histogram of brightness values of pixels (S50). Pixel features using only pixel information are features related to histograms using pixel-specific information and frequency of pixel information, and may be used as important features representing overall information of an image.
  • the pixel feature extractor 152 includes a pixel including an average, standard deviation, skew, kutosis, entropy, and root mean square of pixel values of an image. Features can be extracted. Equations (4) to (7) are equations of pixel features used for feature extraction for skin lesions.
  • Equations (4) to (7) X is pixel information (pixel value) in the region of interest, P is a histogram for pixels in the region of interest, N is the number of pixels, Ent is the entropy of the histogram, and Kur is the keratos of the pixel values. , RMS is the root mean square of the pixel values, and STD is the standard deviation of the pixel values.
  • Entropy in Equation (4) is a measure of disorder, which is a feature of the frequency of histograms of pixels in an image.
  • Kurtosis of Equation (5) represents a measure of probability distribution indicating how much the distribution of pixel values in an image is distributed at a specific value.
  • the root mean square and standard deviation of Eqs. (6) and (7) can be used as a statistical measure of the magnitude of change in pixel values and as a pixel feature related to the scatter of values in the matrix.
  • the GLCM converter 153 converts the pixel values in the ROI of the image into a gray-level co-occurrence matrix (GLCM) representing the adjacency between the pixel values.
  • the conversion is made (S60). 8 is an exemplary diagram of a gray degree co-occurrence matrix (GLCM) converted from an image according to an embodiment of the present invention.
  • GLCM represents the frequency of pixel values of adjacent pixels, and is a matrix having an N ⁇ N size (N is the total number of pixel values).
  • the GLCM conversion unit 153 generates an image of (X, Y) pixels whenever an adjacent pixel group corresponding to the X th (X is an integer less than or equal to N) pixel value and the Y th (Y is an integer less than or equal to N) pixel value is found. You can create GLCM by increasing the value by 1. In the example of FIG.
  • the GLCM feature extractor 154 extracts the GLCM features based on pixel values of the gray degree co-generation matrix GLCM (S70).
  • the GLCM feature extractor 154 may include auto-correlation, contrast, correlation and analogy of pixel values of a gray-level co-occurrence matrix (GLCM).
  • GLCM features can be extracted including dissimilarity, energy and homogeneity.
  • the GLCM feature extractor 154 may extract GLCM features from GLCM according to Equations (8) to (10) below.
  • Equations (8) to (10) Cont is the contrast of pixel values of GLCM, Corr is the correlation of GLCM, Homo is the homogeneity of GLCM, i and j are the positions of the matrix, and P (i, j) is the GLCM Pixel value, N g is the pixel value of the image before conversion to GLCM, ⁇ x is the mean value of p x , P x (i) is the probability for the row in GLCM, ⁇ y is the mean value of p y , and P y (i) Is the probability of heat in GLCM, ⁇ x is the standard deviation of p x , and ⁇ y is the standard deviation of p y .
  • Contrast of Equation (8) is used as a feature of the measure of the contrast of the pixels in the image.
  • the correlation of Equation 9 is used as a feature of the measure of how similar the pixel values in the image are to each other.
  • the homogeneity of equation (10) is used as a feature of the measure of how similar pixel values are distributed in the pixels of an image.
  • the GLRLM converter 155 converts the pixel values of the image into a gray level run length matrix (GLRLM) representing a continuous length of the pixel values (S80).
  • GLRLM gray level run length matrix
  • S80 a continuous length of the pixel values
  • 9 is an exemplary diagram of a gray scale continuous length matrix (GLRLM) converted from an image according to an embodiment of the present invention.
  • the GLRLM is a matrix of how long pixels having a specific brightness value are continuously maintained at the same value (grouping).
  • the GLRLM may be generated by accumulating the frequency of successive pixel values in an image in the ROI.
  • the rows of GLRLM are pixel values, and the columns are the number of successive pixel values.
  • the GLRLM converter 153 may generate the GLRLM by incrementing the value of the (x, y) pixel by 1 each time the x-th (x is an integer less than or equal to N) pixel appears consecutively y times.
  • the frequency of the pixel having the pixel value '1' one row of GLRLMs
  • the one-row, two-column component value is one
  • the pixel value is one.
  • the frequency of the pixel having '4' four rows of GLRLMs
  • appears three times consecutively three columns of GLRLMs
  • the GLRLM feature extractor 156 extracts GLRLM features based on the pixel values of the gray continuous length matrix (S90).
  • the GLRLM feature extractor 156 may include short run emphasis (SRE), long run emphasis (LRE), gray level non-uniformity (GLNU), and RP based on pixel values of a gray scale continuous length matrix (GLRLM).
  • GLRLM features can be extracted including Run Percentage, Run Length Non-Uniformity (RLNU), and High Gray Level Run Emphasis (HGLRE).
  • the classifier 157 classifies skin diseases by applying pixel features, GLCM features, and GLRLM features to a machine learning model (S100).
  • the machine learning model can include a support vector machine (SVM) model.
  • SVM support vector machine
  • Table 1 shows the feature values (Pixel features, GLCM features, GLRLM features) for melanoma and Nevus used in the training data. From Table 1, it can be seen that differences that were not visible to the naked eye in pixel information due to the characteristics of melanoma and nevus appear in feature values extracted from the transformation matrices GLCM and GLRLM. Skin characteristic classification was performed through the SVM classifier using any test image using these feature values.
  • FIG. 11 is a diagram illustrating skin disease classification accuracy performance of an automatic skin disease classification method according to an exemplary embodiment of the present invention. In FIG.
  • Alpha represents classification accuracy when melanoma and nevus are classified using only ABCD method
  • Beta represents classification accuracy using ABCD method and pixel-based feature
  • Theta represents ABCD method, pixel feature, and GLCM feature. Is the classification accuracy when the ABCD method, pixel features, GLCM features, and GLRLM features are applied.
  • melanoma may be applied by applying micromatrix information (pixel features) that are difficult to visually identify and transformation matrixes based on similarity of adjacent pixels and GLCM / GLRLM features extracted from transformation matrices. It can be seen that the classification accuracy is improved.
  • the method for automatically classifying skin diseases according to an embodiment of the present invention may be used for quantitative and objective analysis and diagnosis in determining skin diseases, and may be particularly useful for an early detection system for skin diseases such as melanoma.
  • the automatic skin disease classification method according to an embodiment of the present invention can be utilized to effectively provide information about the disease before the invasive diagnosis such as biopsy for other skin diseases other than melanoma.
  • the method for automatically classifying skin diseases may be implemented by, for example, a computer executable program, and may be implemented in a general-purpose digital computer operating the program using a computer readable recording medium.
  • the computer-readable recording medium may be volatile memory such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), Nonvolatile memory, such as electrically erasable and programmable ROM (EEPROM), flash memory device, phase-change RAM (PRAM), magnetic RAM (MRAM), resistive RAM (RRAM), ferroelectric RAM (FRAM), floppy disk, hard disk, or Optical reading media may be, for example, but not limited to, a storage medium in the form of CD-ROM, DVD, and the like.

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Abstract

L'invention concerne un appareil et un procédé de classification automatique d'une maladie de la peau et un support d'enregistrement, l'appareil et le procédé étant capables de classifier automatiquement une maladie de la peau, telle que le mélanome, avec un niveau élevé de précision sur la base des caractéristiques de pixels d'une image capturée de la maladie de la peau, et de caractéristiques d'une matrice de co-occurrence de niveaux de gris (GLCM) et de caractéristiques d'une matrice de longueur d'exécution de niveau de gris (GLRLM) extraites d'une GLCM et d'une GLRLM converties à partir de l'image. Le procédé de classification automatique d'une maladie de la peau, selon un mode de réalisation de la présente invention, comprend les étapes consistant à : produire un histogramme des valeurs de luminosité de pixels dans une image capturée d'une région de peau, et extraire des caractéristiques de pixels au moyen d'une analyse statistique de l'histogramme ; convertir les valeurs de pixels de l'image en une matrice de co-occurrence de niveaux de gris (GLCM) représentant la contiguïté entre les valeurs de pixels ; extraire des caractéristiques GLCM sur la base des valeurs de pixels de la matrice de co-occurrence de niveaux de gris ; convertir les valeurs de pixels de l'image en une matrice de longueurs de niveaux de gris (GLRLM) représentant la longueur des valeurs de pixels ; extraire des caractéristiques GLRLM sur la base des valeurs de pixels de la matrice de longueurs de niveaux de gris ; et classifier la maladie de la peau en appliquant les caractéristiques de pixels, les caractéristiques GLCM et les caractéristiques GLRLM à un modèle d'apprentissage automatique.
PCT/KR2019/002438 2018-02-28 2019-02-28 Appareil de classification automatique de maladie de la peau, et procédé de classification automatique de maladie de la peau WO2019168381A1 (fr)

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