Nothing Special   »   [go: up one dir, main page]

Ren et al., 2018 - Google Patents

Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning

Ren et al., 2018

View HTML @Full View
Document ID
10414198891274796406
Author
Ren F
Cao P
Zhao D
Wan C
Publication year
Publication venue
Technology and health care

External Links

Snippet

BACKGROUND: Diabetic macular edema (DME) is one of the severe complication of diabetic retinopathy causing severe vision loss and leads to blindness in severe cases if left untreated. OBJECTIVE: To grade the severity of DME in retinal images. METHODS: Firstly …
Continue reading at content.iospress.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; 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; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Ren et al. Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning
Wang et al. Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening
Amin et al. A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions
Li et al. Computer‐assisted diagnosis for diabetic retinopathy based on fundus images using deep convolutional neural network
US10430946B1 (en) Medical image segmentation and severity grading using neural network architectures with semi-supervised learning techniques
Mo et al. Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks
Akram et al. Automated detection of exudates and macula for grading of diabetic macular edema
Schlegl et al. Predicting semantic descriptions from medical images with convolutional neural networks
Habib et al. Detection of microaneurysms in retinal images using an ensemble classifier
Platania et al. Automated breast cancer diagnosis using deep learning and region of interest detection (bc-droid)
Akram et al. Detection and classification of retinal lesions for grading of diabetic retinopathy
Mapayi et al. Adaptive thresholding technique for retinal vessel segmentation based on GLCM‐energy information
Decenciere et al. TeleOphta: Machine learning and image processing methods for teleophthalmology
Panda et al. New binary Hausdorff symmetry measure based seeded region growing for retinal vessel segmentation
Xiao et al. Improving lesion segmentation for diabetic retinopathy using adversarial learning
Melo et al. Microaneurysm detection in color eye fundus images for diabetic retinopathy screening
Xia et al. A multi-scale segmentation-to-classification network for tiny microaneurysm detection in fundus images
Mapayi et al. Comparative study of retinal vessel segmentation based on global thresholding techniques
Chudzik et al. Exudate segmentation using fully convolutional neural networks and inception modules
Valizadeh et al. Presentation of a segmentation method for a diabetic retinopathy patient’s fundus region detection using a convolutional neural network
Kaur et al. Novel approach for brain tumor detection based on Naïve Bayes classification
Zulfira et al. Segmentation technique and dynamic ensemble selection to enhance glaucoma severity detection
Díaz-Pernil et al. Fully automatized parallel segmentation of the optic disc in retinal fundus images
Waheed et al. Hybrid features and mediods classification based robust segmentation of blood vessels
Gao et al. An effective retinal blood vessel segmentation by using automatic random walks based on centerline extraction