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

Khaled Alsaih; Guillaume Lemaitre; Join Massich Vall; Mojdeh Rastgoo; Desire Sidibe; Tien Y Wong; Ecosse Lamoureux; Dan Milea; Carol Y Cheung; Fabrice Meriaudeau

doi:10.1109/EMBC.2016.7590956

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

Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug:2016:1344-1347. doi: 10.1109/EMBC.2016.7590956.

Authors

Khaled Alsaih, Guillaume Lemaitre, Join Massich Vall, Mojdeh Rastgoo, Desire Sidibe, Tien Y Wong, Ecosse Lamoureux, Dan Milea, Carol Y Cheung, Fabrice Meriaudeau

PMID: 28268574
DOI: 10.1109/EMBC.2016.7590956

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.

MeSH terms

Databases, Factual
Diabetic Retinopathy / diagnostic imaging*
Humans
Image Processing, Computer-Assisted
Macular Edema / diagnostic imaging*
Sensitivity and Specificity
Support Vector Machine
Tomography, Optical Coherence / methods*