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Age-Related Macular Degeneration Detection and Stage Classification Using Choroidal OCT Images

机译:使用脉络膜OCT图像的年龄相关性黄斑变性检测和分级

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Age-Related Macular Degeneration (AMD) is a progressive eye disease which damages the retina and causes visual impairment. Detecting those in the early stages at most risk of progression will allow more timely treatment and preserve sight. In this paper, we propose a machine learning based method to detect AMD and distinguish the different stages using choroidal images obtained from optical coherence tomography (OCT). We extract texture features using a Gabor filter bank and non-linear energy transformation. Then the histogram based feature descriptors are used to train the random forests, Support Vector Machine (SVM) and neural networks, which are tested on our choroid OCT image dataset with 21 participants. The experimental results show the feasibility of our method.
机译:与年龄有关的黄斑变性(AMD)是一种进行性眼病,会损害视网膜并导致视力障碍。在早期阶段发现那些进展风险最大的人将可以更及时地进行治疗,并保持视力。在本文中,我们提出了一种基于机器学习的方法来检测AMD并使用从光学相干断层扫描(OCT)获得的脉络膜图像区分不同阶段。我们使用Gabor滤波器组和非线性能量转换来提取纹理特征。然后,基于直方图的特征描述符用于训练随机森林,支持向量机(SVM)和神经网络,并在我们的脉络膜OCT图像数据集上对21位参与者进行了测试。实验结果表明了该方法的可行性。

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