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Special Section on Optical Coherence Tomography in Ophthalmology

Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets

[+] Author Affiliations
Robert J. Zawadzki

University of California, Davis, Vision Science and Advanced Retinal Imaging Laboratory (VSRI) and Dept. of Ophthalmology and Vision Science, 4860 Y Street, Suite 2400, Sacramento, California 95817

Alfred R. Fuller, David F. Wiley, Bernd Hamann

University of California, Davis, Institute for Data Analysis and Visualization (IDAV) and Dept. of Computer Science, One Shields Avenue, Davis, California, 95616

Stacey S. Choi, John S. Werner

University of California, Davis, Vision Science and Advanced Retinal Imaging Laboratory (VSRI) and Dept. of Ophthalmology and Vision Science, 4860 Y Street, Suite 2400, Sacramento, California 95817

J. Biomed. Opt. 12(4), 041206 (August 21, 2007). doi:10.1117/1.2772658
History: Received November 03, 2006; Revised March 06, 2007; Accepted March 09, 2007; Published August 21, 2007
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Recent developments in Fourier domain—optical coherence tomography (Fd-OCT) have increased the acquisition speed of current ophthalmic Fd-OCT instruments sufficiently to allow the acquisition of volumetric data sets of human retinas in a clinical setting. The large size and three-dimensional (3D) nature of these data sets require that intelligent data processing, visualization, and analysis tools are used to take full advantage of the available information. Therefore, we have combined methods from volume visualization, and data analysis in support of better visualization and diagnosis of Fd-OCT retinal volumes. Custom-designed 3D visualization and analysis software is used to view retinal volumes reconstructed from registered B-scans. We use a support vector machine (SVM) to perform semiautomatic segmentation of retinal layers and structures for subsequent analysis including a comparison of measured layer thicknesses. We have modified the SVM to gracefully handle OCT speckle noise by treating it as a characteristic of the volumetric data. Our software has been tested successfully in clinical settings for its efficacy in assessing 3D retinal structures in healthy as well as diseased cases. Our tool facilitates diagnosis and treatment monitoring of retinal diseases.

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© 2007 Society of Photo-Optical Instrumentation Engineers

Citation

Robert J. Zawadzki ; Alfred R. Fuller ; Stacey S. Choi ; John S. Werner ; David F. Wiley, et al.
"Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets", J. Biomed. Opt. 12(4), 041206 (August 21, 2007). ; http://dx.doi.org/10.1117/1.2772658


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