Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid with local binary patterns.
We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular hole, macular edema, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local descriptors are dimension-reduced Local Binary Pattern histograms, which are capable of encoding texture information from OCT images of the retina. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class Support Vector Machine classifiers to identify the presence of normal macula and each of the three pathologies. We conducted extensive experiments on a large dataset consisting of 326 OCT scans from 136 patients. The results show that the proposed method is very effective.
Liu
Yu-Ying
YY
School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA.
Chen
Mei
M
Ishikawa
Hiroshi
H
Wollstein
Gadi
G
Schuman
Joel S
JS
Rehg
James M
JM
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008266:Macula Lutea / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012164:Retinal Diseases / Q000473:pathology*; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence / Q000379:methods*