Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening
Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening
复制标题
DOI：
10.1109/tmi.2013.2247770
复制
发表时间：
2013-06-01
影响因子：
10.6
通讯作者：
Wong, Tien Yin
中科院分区：
文献类型：
--
作者：
Cheng, Jun;Liu, Jiang;Wong, Tien Yin
青光眼是一种导致视力丧失的慢性眼病。由于无法治愈，因此及时发现疾病很重要。目前使用眼压 (IOP) 的测试对于基于人群的青光眼筛查不够敏感。视网膜眼底图像中的视神经乳头评估更有前景且更优越。本文提出使用超像素分类进行视盘和视杯分割以进行青光眼筛查。在视盘分割中，直方图和中心环绕统计用于将每个超像素分类为盘或非盘。计算自我评估可靠性分数来评估自动视盘分割的质量。对于视杯分割，除了直方图和中心环绕统计之外，位置信息也包含在特征空间中以提高性能。所提出的分割方法已在包含 650 张图像的数据库中进行了评估，其中视盘和视杯边界由经过培训的专业人员手动标记。实验结果显示视盘和视杯分割的平均重叠误差分别为 9.5% 和 24.1%。结果还显示，随着可靠性分数的降低，重叠误差会增加，这证明了自我评估的有效性。然后使用分段的视盘和视杯来计算用于青光眼筛查的视杯与视盘的比率。我们提出的方法在两个数据集中实现了 0.800 和 0.822 的曲线下面积，这高于其他方法。该方法可用于分割和青光眼筛查。自我评估将作为误差较大病例的指标，加强自动分割和筛查的临床部署。
Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. This paper proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. In optic disc segmentation, histograms, and center surround statistics are used to classify each superpixel as disc or non-disc. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. For optic cup segmentation, in addition to the histograms and center surround statistics, the location information is also included into the feature space to boost the performance. The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals. Experimental results show an average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. Our proposed method achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods. The methods can be used for segmentation and glaucoma screening. The self-assessment will be used as an indicator of cases with large errors and enhance the clinical deployment of the automatic segmentation and screening.