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基于随机森林的频谱域光学相干层析技术的图像视网膜神经纤维层分割

陈强 徐军 牛四杰

陈强, 徐军, 牛四杰. 基于随机森林的频谱域光学相干层析技术的图像视网膜神经纤维层分割[J]. 电子与信息学报, 2017, 39(5): 1101-1108. doi: 10.11999/JEIT160663
引用本文: 陈强, 徐军, 牛四杰. 基于随机森林的频谱域光学相干层析技术的图像视网膜神经纤维层分割[J]. 电子与信息学报, 2017, 39(5): 1101-1108. doi: 10.11999/JEIT160663
CHEN Qiang, XU Jun, NIU Sijie. Retinal Nerve Fiber Layer Segmentation of Spectral Domain Optical Coherence Tomography Images Based on Random Forest[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1101-1108. doi: 10.11999/JEIT160663
Citation: CHEN Qiang, XU Jun, NIU Sijie. Retinal Nerve Fiber Layer Segmentation of Spectral Domain Optical Coherence Tomography Images Based on Random Forest[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1101-1108. doi: 10.11999/JEIT160663

基于随机森林的频谱域光学相干层析技术的图像视网膜神经纤维层分割

doi: 10.11999/JEIT160663
基金项目: 

国家自然科学基金(61671242),中央高校基本科研业务费专项资金(30920140111004),六大人才高峰(2014-SWYY-024),福建省信息处理与智能控制重点实验室(闽江学院)开放课题基(MJUKF201706)

Retinal Nerve Fiber Layer Segmentation of Spectral Domain Optical Coherence Tomography Images Based on Random Forest

Funds: 

The National Natural Science Foundation of China (61671242), The Special Funds of Fundamental Research for the Central Universities (30920140111004), Six Big Talent Peals (2014-SWYY-024), The Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University)(MJUKF201706)

  • 摘要: 频谱域光学相干层析技术是一种广泛应用于眼科疾病诊断的成像技术,而视网膜层分割对青光眼的诊断有很好的参考价值。该文利用随机森林分类器寻找视网膜层间单像素宽的边界,随机森林分类器由12个特征训练产生,其中相对灰度特征和邻域特征较好地解决灰度不均匀的分割误差大问题。对10组带有青光眼病变的视网膜图像进行分割,并与传统算法和Iowa软件进行比较,平均边界绝对误差为9.202.57 m, 11.332.99 m和10.273.01 m。实验结果表明,改进算法可以较好地分割视网膜神经纤维层。
  • OJIMA T, TANABE T, HANGAI M, et al. Measurement of retinal nerve fiber layer thickness and macular volume forglaucoma detection using optical coherence tomography[J]. Japanese Journal of Ophthalmology, 2007, 51(3): 197-203. doi: 10.1111/cxo.12366.
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    NIU Sijie, CHEN Qiang, LU Shengtao, et al. SD-OCT image layer segmentation using multi-scale 3-D graph search method[J]. Computer Science, 2015, 42(9): 272-277. doi: 10. 11896/j.issn.1002-137X.2015.9.053.
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    CHEN Q, FAN W, NIU S, et al. Automated choroid segmentation based on gradual intensity distance in HD-OCT images[J]. Optics Express, 2015, 23(7): 8974-8994. doi: 10. 1364/oe.23.008974.
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出版历程
  • 收稿日期:  2016-06-24
  • 修回日期:  2017-03-23
  • 刊出日期:  2017-05-19

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