• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Chinese Journal of Electronics >An Ensemble Retinal Vessel Segmentation Based on Supervised Learning in Fundus Images
【24h】

An Ensemble Retinal Vessel Segmentation Based on Supervised Learning in Fundus Images

机译：基于眼底图像监督学习的整体视网膜血管分割

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

An ensemble method based on supervised learning for segmenting the retinal vessels in color fundus images is proposed on the basis of previous work of Zhu et al. For each pixel, a 36 dimensional feature vector is extracted, including local features, morphological transformation with multi-scale and multi-orientation, and divergence of vector field which is firstly used to extract feature of retinal image pixels. Then the feature vector is used as input data set to train the weak classifiers by the Classification and regression tree (CART). Finally, an AdaBoost classifier is constructed by iteratively training for the retinal vessels segmentation. The experimental results on the public Digital retinal images for vessel extraction (DRIVE) database demonstrate that the proposed method is efficient and robust on the fundus images with lesions when compared with the other methods. Meanwhile, the proposed method also exhibits high robustness on a new Retinal images for screening (RIS) database. The average accuracy, sensitivity, and specificity of improved method are 0.9535, 0.8319 and 0.9607, respectively. It has potential applications for computer-aided diagnosis and disease screening.
机译：在Zhu等人以前的工作的基础上，提出了一种基于监督学习的彩色眼底图像视网膜血管分割方法。对于每个像素，提取一个36维特征向量，包括局部特征，具有多尺度和多方向的形态转换以及向量场的发散度，该向量场首先用于提取视网膜图像像素的特征。然后将特征向量用作输入数据集，以通过分类和回归树（CART）训练弱分类器。最后，通过迭代训练视网膜血管分段来构造AdaBoost分类器。在公共数字视网膜图像血管提取数据库上的实验结果表明，与其他方法相比，该方法对有病变的眼底图像有效且鲁​​棒。同时，该方法在新的用于筛选的视网膜图像（RIS）数据库上也表现出很高的鲁棒性。改进方法的平均准确度，灵敏度和特异性分别为0.9535、0.8319和0.9607。它具有计算机辅助诊断和疾病筛查的潜在应用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号