Tan Ngan-Meng, Xu Yanwu, Goh Wooi Boon, Liu Jiang
iMED Ocular Programme, Institute for Infocomm Research, 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632, Singapore; School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
iMED Ocular Programme, Institute for Infocomm Research, 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632, Singapore.
Comput Med Imaging Graph. 2015 Mar;40:182-93. doi: 10.1016/j.compmedimag.2014.10.002. Epub 2014 Oct 13.
This paper presents an optimal model integration framework to robustly localize the optic cup in fundus images for glaucoma detection. This work is based on the existing superpixel classification approach and makes two major contributions. First, it addresses the issues of classification performance variations due to repeated random selection of training samples, and offers a better localization solution. Second, multiple superpixel resolutions are integrated and unified for better cup boundary adherence. Compared to the state-of-the-art intra-image learning approach, we demonstrate improvements in optic cup localization accuracy with full cup-to-disc ratio range, while incurring only minor increase in computing cost.
本文提出了一种最优模型集成框架，用于在眼底图像中稳健地定位视杯，以检测青光眼。这项工作基于现有的超像素分类方法，并做出了两项主要贡献。首先，它解决了由于训练样本的重复随机选择而导致的分类性能变化问题，并提供了更好的定位解决方案。其次，集成并统一了多个超像素分辨率，以更好地贴合视杯边界。与当前最先进的图像内学习方法相比，我们证明了在整个视杯与视盘比率范围内，视杯定位精度有所提高，而计算成本仅略有增加。
