Wong Damon W K, Liu Jiang, Tan Ngan Meng, Fengshou Yin, Cheung Carol, Baskaran Mani, Aung Tin, Wong Tien Yin
Institute For Infocomm Research, A*STAR, Singapore.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1426-9. doi: 10.1109/EMBC.2012.6346207.
Optic cup detection remains a challenging task in retinal image analysis, and is of particular importance for glaucoma evaluation, where disease severity is often assessed by the size of the optic cup. In this paper, we propose spatial heuristic ensembling (SHE), an approach which aims to fuse the advantages of each method based on the specific performance in each defined sector. In this way, we generate an ensembled optic cup which is obtained from the optimal combination of the component methods. We conduct experiments on the ORIGA data set of 650 retinal images and show that the ensemble approach performs better than the individual segmentations, reducing the relative overlap error, and CDR errors by as much as 0.04 CDR units. The results are promising for the continued development of such an approach for improving optic cup segmentation.
在视网膜图像分析中，视杯检测仍然是一项具有挑战性的任务，对视神经乳头评估尤为重要，青光眼严重程度通常通过视杯大小来评估。在本文中，我们提出了空间启发式集成（SHE）方法，该方法旨在根据每个定义扇区中的特定性能融合每种方法的优点。通过这种方式，我们生成了一个从各组成方法的最佳组合中获得的集成视杯。我们在包含650张视网膜图像的ORIGA数据集上进行了实验，结果表明集成方法比单独的分割方法表现更好，将相对重叠误差和杯盘比（CDR）误差降低了多达0.04个CDR单位。这些结果对于继续开发这种改进视杯分割的方法很有前景。
