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Wang et al., 2020 - Google Patents

Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening

Wang et al., 2020

Document ID
17283036834250340999
Author
Wang H
Yuan G
Zhao X
Peng L
Wang Z
He Y
Qu C
Peng Z
Publication year
Publication venue
Computer methods and programs in biomedicine

External Links

Snippet

Background and objective: Diabetic retinopathy (DR), which is generally diagnosed by the presence of hemorrhages and hard exudates, is one of the most prevalent causes of visual impairment and blindness. Early detection of hard exudates (HEs) in color fundus …
Continue reading at www.sciencedirect.com (other versions)

Classifications

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    • 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
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    • G06K9/46—Extraction of features or characteristics of the image
    • G06K9/52—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
    • G06K9/527—Scale-space domain transformation, e.g. with wavelet analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06T2207/30004—Biomedical image processing
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
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    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • G—PHYSICS
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