Derakhshani Reza, Saripalle Sashi K, Doynov Plamen
Department of Computer Science Electrical Engineering, University of Missouri at Kansas City, Kansas City, MO 64110-2499, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1490-3. doi: 10.1109/EMBC.2012.6346223.
Assessment of vascularity of conjunctiva has many diagnostic and prognostic applications, thus creation of computational methods for its fast and objective assessment is of importance. Here we provide two different methods for estimation of conjunctiva's vascularity from color digital images, with our best results showing a correlation coefficient of 0.89 between the predicted and ground truth values using a committee of artificial neural networks.
结膜血管评估有许多诊断和预后应用，因此创建用于其快速和客观评估的计算方法很重要。在这里，我们提供了两种从彩色数字图像估计结膜血管的不同方法，我们的最佳结果显示，使用人工神经网络委员会，预测值与真实值之间的相关系数为0.89。
