Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India.
Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India.
Comput Biol Med. 2017 Sep 1;88:142-149. doi: 10.1016/j.compbiomed.2017.06.017. Epub 2017 Jun 19.
Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.
青光眼是导致永久性视力丧失的主要原因之一。它是一种由眼内液体压力增加引起的眼部疾病。现有的青光眼临床诊断方法需要熟练的监督。这些方法是手动的，耗时的，并且超出了普通人的能力范围。因此，需要一种用于大规模筛查的自动化青光眼诊断系统。在本文中，我们提出了一种使用数字眼底图像自动诊断青光眼的新方法。变分模态分解 (VMD) 方法用于图像的迭代分解。从 VMD 分量中提取各种特征，如 Kapoor 摘 要、Renyi 摘 要、Yager 摘 要和分形维数。使用 ReliefF 算法选择判别特征，然后将这些特征输入最小二乘支持向量机 (LS-SVM) 进行分类。我们提出的方法在使用三折和十折交叉验证策略时，分别实现了 95.19%和 94.79%的分类准确率。该系统可以帮助眼科医生通过眼底图像确认他们对（青光眼或正常）类别的手动阅读。
