title: Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections.
abstract: This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.
authors.forename: Khaled
authors.initials: K
authors.lastname: Alsaih
authors.forename: Guillaume
authors.initials: G
authors.lastname: Lemaitre
authors.forename: Join Massich
authors.initials: JM
authors.lastname: Vall
authors.forename: Mojdeh
authors.initials: M
authors.lastname: Rastgoo
authors.forename: Desire
authors.initials: D
authors.lastname: Sidibe
authors.forename: Tien Y
authors.initials: TY
authors.lastname: Wong
authors.forename: Ecosse
authors.initials: E
authors.lastname: Lamoureux
authors.forename: Dan
authors.initials: D
authors.lastname: Milea
authors.forename: Carol Y
authors.initials: CY
authors.lastname: Cheung
authors.forename: Fabrice
authors.initials: F
authors.lastname: Meriaudeau
mesh_terms: D016208:Databases, Factual; D003930:Diabetic Retinopathy / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008269:Macular Edema / Q000000981:diagnostic imaging*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D041623:Tomography, Optical Coherence / Q000379:methods*