Department of Electronics and Communication Engineering, IES College of Technology, Bhopal, 462044, MP, India.
Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal, 462003, MP, India.
J Digit Imaging. 2022 Oct;35(5):1283-1292. doi: 10.1007/s10278-022-00648-1. Epub 2022 May 17.
One of the most prevalent causes of visual loss and blindness is glaucoma. Conventionally, instrument-based tools are employed for glaucoma screening. However, they are inefficient, time-consuming, and manual. Hence, computerized methodologies are needed for fast and accurate diagnosis of glaucoma. Therefore, we proposed a Computer-Aided Diagnosis (CAD) method for the classification of glaucoma stages using Image Empirical Mode decomposition (IEMD). In this study, IEMD is applied to decompose the preprocessed fundus photographs into different Intrinsic Mode Functions (IMFs) to capture the pixel variations. Then, the significant texture-based descriptors have been computed from the IMFs. A dimensionality reduction approach called Principal Component Analysis (PCA) has been employed to pick the robust descriptors from the retrieved feature set. We used the Analysis of Variance (ANOVA) test for feature ranking. Finally, the LS-SVM classifier has been employed to classify glaucoma stages. The proposed CAD system achieved a classification accuracy of 94.45% for the binary classification on the RIM-ONE r12 database. Our approach demonstrated better glaucoma classification performance than the existing automated systems.
青光眼是导致视力丧失和失明的最常见原因之一。传统上，使用基于仪器的工具进行青光眼筛查。然而，它们效率低下、耗时且手动。因此，需要计算机化的方法来快速准确地诊断青光眼。因此，我们提出了一种使用图像经验模态分解 (IEMD) 对青光眼阶段进行分类的计算机辅助诊断 (CAD) 方法。在这项研究中，IEMD 被应用于将预处理的眼底照片分解为不同的固有模态函数 (IMF) 以捕捉像素变化。然后，从 IMF 中计算出基于纹理的重要描述符。采用主成分分析 (PCA) 这种降维方法从检索到的特征集中选择稳健的描述符。我们使用方差分析 (ANOVA) 测试进行特征排序。最后，使用最小二乘支持向量机 (LS-SVM) 分类器对青光眼阶段进行分类。在所提出的 CAD 系统中，在 RIM-ONE r12 数据库上进行的二进制分类中达到了 94.45%的分类准确率。我们的方法在青光眼分类性能方面优于现有的自动化系统。
