Paper Info
Title
Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid with local binary patterns.
Abstract
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.
Year
Venue
Keywords
2010
MICCAI
macular hole,normal macula,age-related macular degeneration,local binary pattern,local descriptors,macular edema,multiple macular pathology,multi-scale spatial pyramid,oct image,retinal oct image,macular pathology,automated macular pathology diagnosis,support vector machine,machine learning,spatial scale
Field
DocType
Volume
Computer vision,Histogram,Optical coherence tomography,Pattern recognition,Macula Lutea,Macular edema,Computer science,Support vector machine,Local binary patterns,Macular degeneration,Artificial intelligence,Macular hole
Conference
13
Issue
ISSN
ISBN
Pt 1
0302-9743
3-642-15704-1
Citations 
PageRank 
References 
2
0.45
9
Authors
6
Name
Order
Citations
PageRank
Yu-Ying Liu1353.04
Mei Chen241836.25
ishikawa351530.86
Gadi Wollstein4576.02
Joel S Schuman5638.75
James M. Rehg65259474.66