Xu Yanwu, Liu Jiang, Cheng Jun, Yin Fengshou, Tan Ngan Meng, Wong Damon Wing Kee, Baskaran Mani, Cheng Ching Yu, Wong Tien Yin
Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1430-3. doi: 10.1109/EMBC.2012.6346208.
We present a regional propagation approach based on retinal structure priors to localize the optic cup in 2D fundus images, which is the primary image component clinically used for identifying glaucoma. This method provides three major contributions. First, it proposes processing of the fundus images at the superpixel level, which leads to more descriptive and effective features than those employed by pixel based techniques, without additional computational cost. Second, the proposed approach does not need manually labeled training samples, but uses the structural priors on relative cup and disc positions. Third, a refinement scheme that utilizes local context information is adopted to further improve the accuracy. Tested on the ORIGA-light clinical dataset, which comprises of 325 images from a population-based study, the proposed method achieves a 34.9% non-overlap ratio with manually-labeled ground-truth and a 0.104 absolute cup-to-disc ratio (CDR) error. This level of accuracy is much higher than the state-of-the-art pixel based techniques, with a comparable or even less computational cost.
我们提出了一种基于视网膜结构先验的区域传播方法，用于在二维眼底图像中定位视杯，视杯是临床上用于识别青光眼的主要图像成分。该方法有三大贡献。首先，它提出在超像素级别处理眼底图像，这比基于像素的技术所采用的特征更具描述性和有效性，且无需额外的计算成本。其次，所提出的方法不需要手动标注的训练样本，而是使用视杯和视盘相对位置的结构先验。第三，采用了一种利用局部上下文信息的细化方案来进一步提高准确性。在ORIGA-light临床数据集上进行测试，该数据集包含来自一项基于人群研究的325张图像，所提出的方法与手动标注的真值的非重叠率达到34.9%，视杯与视盘比率（CDR）的绝对误差为0.104。这种准确性水平远高于基于像素的现有技术，且计算成本相当甚至更低。
