Yin Fengshou, Liu Jiang, Wong Damon W K, Tan Ngan Meng, Cheng Jun, Cheng Ching-Yu, Tham Yih Chung, Wong Tien Yin
Institute for Infocomm Research, A*STAR, Singapore.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1454-7. doi: 10.1109/EMBC.2012.6346214.
The optic cup segmentation is critical for automated cup-to-disk ratio measurement, and hence computer-aided diagnosis of glaucoma. In this paper, we propose a novel sector-based method for optic cup segmentation. The method comprises two parts: intensity-based cup segmentation with shape constraints and blood vessel-based refinement. The initial estimation of the cup is obtained by applying a statistical deformable model on the vessel free image. At the same time, blood vessels within the optic disk are extracted, after which vessel bendings and vessel boundaries in the nasal side are located. Subsequently, these key points in the blood vessels are used to fine tune the cup. The algorithm is evaluated on 650 fundus images from the ORIGA(-light) database. Experimental results show that the Dice coefficient for the optic cup segmentation can be as high as 0.83, which outperforms other existing methods. The results demonstrate good potential for the proposed method to be used in automated optic cup segmentation and glaucoma diagnosis.
视杯分割对于自动杯盘比测量至关重要，因此对于青光眼的计算机辅助诊断也很关键。在本文中，我们提出了一种新颖的基于扇区的视杯分割方法。该方法包括两个部分：具有形状约束的基于强度的视杯分割和基于血管的细化。通过对无血管图像应用统计可变形模型来获得视杯的初始估计。同时，提取视盘内的血管，之后定位血管弯曲处和鼻侧的血管边界。随后，利用血管中的这些关键点对视杯进行微调。该算法在来自ORIGA（-light）数据库的650张眼底图像上进行了评估。实验结果表明，视杯分割的骰子系数可高达0.83，优于其他现有方法。结果表明该方法在自动视杯分割和青光眼诊断中具有良好的应用潜力。
