用于ARGALI中青光眼检测的杯盘比测定方法的智能融合
Intelligent fusion of cup-to-disc ratio determination methods for glaucoma detection in ARGALI.
作者信息
Wong D W K, Liu J, Lim J H, Tan N M, Zhang Z, Lu S, Li H, Teo M H, Chan K L, Wong T Y
机构信息
Institute for Infocomm Research, A*STAR, Singapore.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5777-80. doi: 10.1109/IEMBS.2009.5332534.
Glaucoma is a leading cause of permanent blindness. ARGALI, an automated system for glaucoma detection, employs several methods for segmenting the optic cup and disc from retinal images, combined using a fusion network, to determine the cup to disc ratio (CDR), an important clinical indicator of glaucoma. This paper discusses the use of SVM as an alternative fusion strategy in ARGALI, and evaluates its performance against the component methods and neural network (NN) fusion in the CDR calculation. The results show SVM and NN provide similar improvements over the component methods, but with SVM having a greater consistency over the NN, suggesting potential for SVM as a viable option in ARGALI.
青光眼是导致永久性失明的主要原因。ARGALI是一种用于青光眼检测的自动化系统，它采用多种方法从视网膜图像中分割出视杯和视盘，并通过融合网络将这些方法结合起来，以确定杯盘比（CDR），这是青光眼的一项重要临床指标。本文讨论了在ARGALI中使用支持向量机（SVM）作为一种替代融合策略，并在CDR计算中针对组成方法和神经网络（NN）融合评估其性能。结果表明，SVM和NN相比组成方法都有类似的改进，但SVM比NN具有更高的一致性，这表明SVM在ARGALI中作为一种可行选择具有潜力。
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