TY - JOUR
T1 - Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding
AU - Liu, Yu Ying
AU - Chen, Mei
AU - Ishikawa, Hiroshi
AU - Wollstein, Gadi
AU - Schuman, Joel S.
AU - Rehg, James M.
N1 - Funding Information:
This research is supported in part by National Institutes of Health contracts R01-EY013178 and P30-EY008098, The Eye and Ear Foundation (Pittsburgh, PA) , unrestricted grants from Research to Prevent Blindness, Inc. (New York, NY) , and grants from Intel Labs Pittsburgh (Pittsburgh, PA) .
PY - 2011/10
Y1 - 2011/10
N2 - 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).
AB - 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).
KW - Computer-aided diagnosis (CAD)
KW - Local binary patterns (LBP)
KW - Macular pathology
KW - Multi-scale spatial pyramid (MSSP)
KW - Optical coherence tomography (OCT)
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U2 - 10.1016/j.media.2011.06.005
DO - 10.1016/j.media.2011.06.005
M3 - Article
C2 - 21737338
AN - SCOPUS:80052138068
SN - 1361-8415
VL - 15
SP - 748
EP - 759
JO - Medical Image Analysis
JF - Medical Image Analysis
IS - 5
ER -