Title:
Automated Macular Pathology Diagnosis in Retinal OCT Images Using Multi-Scale Spatial Pyramid and Local Binary Patterns in Texture and Shape Encoding
Automated Macular Pathology Diagnosis in Retinal OCT Images Using Multi-Scale Spatial Pyramid and Local Binary Patterns in Texture and Shape Encoding
Author(s)
Liu, Yu-Ying
Chen, Mei
Ishikawa, Hiroshi
Wollstein, Gadi
Schuman, Joel S.
Rehg, James M.
Chen, Mei
Ishikawa, Hiroshi
Wollstein, Gadi
Schuman, Joel S.
Rehg, James M.
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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 edema, macular
hole, 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 features
are dimension-reduced Local Binary Pattern histograms, which are capable
of encoding texture and shape information in retinal OCT images and
their edge maps, respectively. 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. To further discriminate sub-types within a
pathology, we also build a classifier to differentiate full-thickness holes from
pseudo-holes within the macular hole category. We conduct extensive experiments on a large dataset of 326 OCT scans from 136 subjects. The results
show that the proposed method is very effective (all AUC > 0:93).
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Date Issued
2011-10
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