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Proceedings Paper

Segmentation of retinal OCT images using a random forest classifier
Author(s): Andrew Lang; Aaron Carass; Elias Sotirchos; Peter Calabresi; Jerry L. Prince
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Paper Abstract

Optical coherence tomography (OCT) has become one of the most common tools for diagnosis of retinal abnormalities. Both retinal morphology and layer thickness can provide important information to aid in the differential diagnosis of these abnormalities. Automatic segmentation methods are essential to providing these thickness measurements since the manual delineation of each layer is cumbersome given the sheer amount of data within each OCT scan. In this work, we propose a new method for retinal layer segmentation using a random forest classifier. A total of seven features are extracted from the OCT data and used to simultaneously classify nine layer boundaries. Taking advantage of the probabilistic nature of random forests, probability maps for each boundary are extracted and used to help refine the classification. We are able to accurately segment eight retinal layers with an average Dice coefficient of 0:79±0:13 and a mean absolute error of 1:21±1:45 pixels for the layer boundaries.

Paper Details

Date Published: 13 March 2013
PDF: 7 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690R (13 March 2013); doi: 10.1117/12.2006649
Show Author Affiliations
Andrew Lang, Johns Hopkins Univ. (United States)
Aaron Carass, Johns Hopkins Univ. (United States)
Elias Sotirchos, Johns Hopkins Univ. School of Medicine (United States)
Peter Calabresi, Johns Hopkins Univ. School of Medicine (United States)
Jerry L. Prince, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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