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Detection and Classification of Retinal Diseases in Spectral Domain Optical Coherence Tomography Images based on SURF descriptors

机译:基于SURF描述符的光谱域光学相干断层扫描图像中视网膜疾病的检测和分类

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Optical Coherence Tomography (OCT) is a non-invasive eye-imaging modality for detecting macular edema both in its early and advanced stages. The main aim of this work is to present the automatic detection of edema of the retinal layers particularly around the macula in diabetic patients. After detection and extracting certain features in the OCT retinal images a classification of the type of Diabetic Macular Edema is done. In this method during preprocessing stage we remove the speckle noise followed by flattening and cropping of the image is done. Then this is followed by Speeded up robust feature extraction. The extracted features are then classified using Support Vector Machine binary classifier as normal or abnormal and thus having Diabetic Macular Edema. This technique has been applied for 25 normal and 45 abnormal OCT images. The results show that this method accurately detected edema diseases in between the layers in the retinal. Then we could classify them using Support Vector Machine as normal or abnormal. Experimental results shows that an average retinal disease detection accuracy of 99% for Support Vector Machine (SVM) classifier. Thus, this algorithm can be used by ophthalmologists in early detection of Macular Edema.
机译:光学相干断层扫描(OCT)是用于在早期和晚期阶段检测黄斑水肿的一种非侵入性眼部成像方式。这项工作的主要目的是提出自动检测糖尿病患者视网膜层,特别是黄斑周围的水肿。在检测并提取了OCT视网膜图像中的某些特征后,对糖尿病性黄斑水肿进行了分类。在此方法的预处理阶段,我们去除了斑点噪声,然后对图像进行平整和裁剪。然后是加速鲁棒特征提取。然后使用支持向量机二进制分类器将提取的特征分类为正常还是异常,从而患有糖尿病性黄斑水肿。此技术已应用于25正常和45异常OCT图像。结果表明,该方法可准确检测出视网膜各层之间的水肿病。然后我们可以使用支持向量机将它们分类为正常还是异常。实验结果表明,支持向量机(SVM)分类器的平均视网膜疾病检测精度为99%。因此,眼科医生可以将该算法用于黄斑水肿的早期检测。

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