Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections.

Published

Conference Paper

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.

Full Text

Duke Authors

Cited Authors

  • Alsaih, K; Lemaitre, G; Vall, JM; Rastgoo, M; Sidibe, D; Wong, TY; Lamoureux, E; Milea, D; Cheung, CY; Meriaudeau, F

Published Date

  • August 2016

Published In

Volume / Issue

  • 2016 /

Start / End Page

  • 1344 - 1347

PubMed ID

  • 28268574

Pubmed Central ID

  • 28268574

International Standard Serial Number (ISSN)

  • 1557-170X

Digital Object Identifier (DOI)

  • 10.1109/EMBC.2016.7590956

Conference Location

  • United States