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Proceedings Article

Segmentation of retinal OCT images using a random forest classifier

[+] Author Affiliations
Andrew Lang, Aaron Carass, Jerry L. Prince

Johns Hopkins Univ. (United States)

Elias Sotirchos, Peter Calabresi

Johns Hopkins Univ. School of Medicine (United States)

Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690R (March 13, 2013); doi:10.1117/12.2006649
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From Conference Volume 8669

  • Medical Imaging 2013: Image Processing
  • Sebastien Ourselin; David R. Haynor
  • Lake Buena Vista (Orlando Area), Florida, USA | February 09, 2013

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. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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 (March 13, 2013); doi:10.1117/12.2006649; http://dx.doi.org/10.1117/12.2006649


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