Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images

BioMedical Engineering Online

Volumn 16, Issue 1, 2017, Pages

  Alsaih, Khaled ^a,b   Lemaitre, Guillaume ^a   Rastgoo, Mojdeh ^a   Massich, Joan ^a   Sidibé, Désiré ^a   Meriaudeau, Fabrice ^a,b  

^a UNIVERSITÉ BOURGOGNE FRANCHE COMTÉ   (France)

^b DEPARTMENT OF CHEMICAL ENGINEERING   (Malaysia)

Author keywords

BoW; Classification; DME detection; HoG; LBP; SD OCT

Indexed keywords

CLASSIFICATION (OF INFORMATION); COHERENT LIGHT; IMAGE CLASSIFICATION; LEARNING SYSTEMS; OPHTHALMOLOGY; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES;

AUTOMATIC CLASSIFICATION; FEATURE REPRESENTATION; HISTOGRAM OF ORIENTED GRADIENTS; LINEAR SUPPORT VECTOR MACHINES; MACHINE LEARNING TECHNIQUES; MULTI-RESOLUTION APPROACH; SD-OCT; SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHIES;

OPTICAL TOMOGRAPHY;

ARTICLE; B SCAN; CLASSIFIER; DIABETIC MACULAR EDEMA; DISEASE CLASSIFICATION; HISTOGRAM; HUMAN; IMAGE QUALITY; MACHINE LEARNING; OPTICAL COHERENCE TOMOGRAPHY DEVICE; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RETINA EXUDATE; RETINAL THICKNESS; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY; SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY; SUBRETINAL FLUID; SUPPORT VECTOR MACHINE; DIABETIC RETINOPATHY; DIAGNOSTIC IMAGING; IMAGE PROCESSING; MACULAR EDEMA; OPTICAL COHERENCE TOMOGRAPHY; PROCEDURES; SIGNAL NOISE RATIO;

DIABETIC RETINOPATHY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MACULAR EDEMA; SIGNAL-TO-NOISE RATIO; TOMOGRAPHY, OPTICAL COHERENCE;

EID: 85020164886     PISSN: None     EISSN: 1475925X     Source Type: Journal    
DOI: 10.1186/s12938-017-0352-9     Document Type: Article

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