IEEE J Biomed Health Inform. 2017 May;21(3):803-813. doi: 10.1109/JBHI.2016.2544961. Epub 2016 Mar 22.
Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve and subsequently causes loss of vision. The available scanning methods are Heidelberg retinal tomography, scanning laser polarimetry, and optical coherence tomography. These methods are expensive and require experienced clinicians to use them. So, there is a need to diagnose glaucoma accurately with low cost. Hence, in this paper, we have presented a new methodology for an automated diagnosis of glaucoma using digital fundus images based on empirical wavelet transform (EWT). The EWT is used to decompose the image, and correntropy features are obtained from decomposed EWT components. These extracted features are ranked based on t value feature selection algorithm. Then, these features are used for the classification of normal and glaucoma images using least-squares support vector machine (LS-SVM) classifier. The LS-SVM is employed for classification with radial basis function, Morlet wavelet, and Mexican-hat wavelet kernels. The classification accuracy of the proposed method is 98.33% and 96.67% using threefold and tenfold cross validation, respectively.
青光眼是一种由于视神经内液体压力增加而引起的眼部疾病。它会损害视神经，进而导致视力丧失。现有的扫描方法有海德堡视网膜断层扫描仪、扫描激光偏振仪和光学相干断层扫描仪。这些方法昂贵，需要经验丰富的临床医生来使用。因此，需要用低成本准确地诊断青光眼。因此，在本文中，我们提出了一种使用基于经验小波变换（EWT）的数字眼底图像自动诊断青光眼的新方法。EWT 用于分解图像，并从分解的 EWT 分量中获得相关熵特征。这些提取的特征根据 t 值特征选择算法进行排序。然后，使用最小二乘支持向量机（LS-SVM）分类器对正常和青光眼图像进行分类。LS-SVM 采用径向基函数、Morlet 小波和墨西哥帽小波核进行分类。使用三折和十折交叉验证，该方法的分类准确率分别为 98.33%和 96.67%。
