Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets
Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets
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DOI：
10.1117/1.2772658
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发表时间：
2007-07-01
影响因子：
3.5
通讯作者：
Werner, John S.
中科院分区：
文献类型：
--
作者：
Zawadzki, Robert J.;Fuller, Alfred R.;Werner, John S.
傅立叶域光学相干断层扫描（Fd-OCT）的最新发展已经充分提高了当前眼科Fd-OCT仪器的采集速度，以允许在临床环境中采集人类视网膜的体积数据集。这些数据集的大尺寸和三维（3D）性质要求使用智能数据处理、可视化和分析工具来充分利用可用信息。因此，我们结合了体积可视化和数据分析的方法，以支持更好的可视化和诊断Fd-OCT视网膜体积。定制设计的3D可视化和分析软件用于查看从配准的B扫描重建的视网膜体积。我们使用支持向量机（SVM）进行半自动分割的视网膜层和结构的后续分析，包括测量层厚度的比较。我们已经修改了支持向量机优雅地处理OCT散斑噪声，将其作为一个特征的体积数据。我们的软件已经在临床环境中成功地测试了其在评估健康和患病病例中3D视网膜结构的有效性。我们的工具有助于视网膜疾病的诊断和治疗监测。(C)2007年，由光学仪器工程师协会（Society of Photo-Optical Instrumentation Engineers）主办。
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. (C) 2007 Society of Photo-Optical Instrumentation Engineers.