Segmentation of retinal OCT images using a random forest classifier

Andrew Lang; Aaron Carass; Elias Sotirchos; Peter Calabresi; Jerry L. Prince

doi:10.1117/12.2006649

13 March 2013 Segmentation of retinal OCT images using a random forest classifier

Andrew Lang, Aaron Carass, Elias Sotirchos, Peter Calabresi, Jerry L. Prince

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86690R (2013) https://doi.org/10.1117/12.2006649
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Andrew Lang, Aaron Carass, Elias Sotirchos, Peter Calabresi, and Jerry L. Prince "Segmentation of retinal OCT images using a random forest classifier", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690R (13 March 2013); https://doi.org/10.1117/12.2006649

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