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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 5, Iss. 2 — Feb. 1, 2014
  • pp: 348–365

Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology

Pratul P. Srinivasan, Stephanie J. Heflin, Joseph A. Izatt, Vadim Y. Arshavsky, and Sina Farsiu  »View Author Affiliations

Biomedical Optics Express, Vol. 5, Issue 2, pp. 348-365 (2014)

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Accurate quantification of retinal layer thicknesses in mice as seen on optical coherence tomography (OCT) is crucial for the study of numerous ocular and neurological diseases. However, manual segmentation is time-consuming and subjective. Previous attempts to automate this process were limited to high-quality scans from mice with no missing layers or visible pathology. This paper presents an automatic approach for segmenting retinal layers in spectral domain OCT images using sparsity based denoising, support vector machines, graph theory, and dynamic programming (S-GTDP). Results show that this method accurately segments all present retinal layer boundaries, which can range from seven to ten, in wild-type and rhodopsin knockout mice as compared to manual segmentation and has a more accurate performance as compared to the commercial automated Diver segmentation software.

© 2014 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.2960) Image processing : Image analysis
(100.5010) Image processing : Pattern recognition
(110.4500) Imaging systems : Optical coherence tomography
(170.4470) Medical optics and biotechnology : Ophthalmology

ToC Category:
Image Processing

Original Manuscript: October 23, 2013
Revised Manuscript: December 13, 2013
Manuscript Accepted: December 17, 2013
Published: January 7, 2014

Pratul P. Srinivasan, Stephanie J. Heflin, Joseph A. Izatt, Vadim Y. Arshavsky, and Sina Farsiu, "Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology," Biomed. Opt. Express 5, 348-365 (2014)

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