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首页> 外文期刊>Computers in Biology and Medicine >Local configuration pattern features for age-related macular degeneration characterization and classification
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Local configuration pattern features for age-related macular degeneration characterization and classification

机译：年龄相关黄斑变性特征和分类的本地配置模式特征

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摘要

Age-related Macular Degeneration (AMD) is an irreversible and chronic medical condition characterized by drusen, Choroidal Neovascularization (CNV) and Geographic Atrophy (GA). AMD is one of the major causes of visual loss among elderly people. It is caused by the degeneration of cells in the macula which is responsible for central vision. AMD can be dry or wet type, however dry AMD is most common. It is classified into early, intermediate and late AMD. The early detection and treatment may help one to stop the progression of the disease. Automated AMD diagnosis may reduce the screening time of the clinicians. In this work, we have introduced LCP to characterize normal and AMD classes using fundus images. Linear Configuration Coefficients (CC) and Pattern Occurrence (PO) features are extracted from fundus images. These extracted features are ranked using p-value of the t-test and fed to various supervised classifiers viz. Decision Tree (DT), Nearest Neighbour (k-NN), Naive Bayes (NB), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to classify normal and AMD classes. The performance of the system is evaluated using both private (Kasturba Medical Hospital, Manipal, India) and public domain datasets viz. Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) using ten-fold cross validation. The proposed approach yielded best performance with a highest average accuracy of 97.78%, sensitivity of 98.00% and specificity of 97.50% for STARE dataset using 22 significant features. Hence, this system can be used as an aiding tool to the clinicians during mass eye screening programs to diagnose AMD. (C) 2015 Elsevier Ltd. All rights reserved.
机译：年龄相关的黄斑变性（AMD）是一种不可逆和慢性医学病症，其特征，其特征是德鲁森，脉络膜新生血管（CNV）和地理萎缩（GA）。 AMD是老年人视力损失的主要原因之一。它是由黄斑中的细胞退化引起的，这是负责中央视觉的。 AMD可以是干燥或湿型，但干燥的AMD是最常见的。它被分为早期，中间和晚期。早期的检测和治疗可能有助于一种阻止疾病的进展。自动化AMD诊断可以减少临床医生的筛选时间。在这项工作中，我们引入了LCP，以使用眼底图像表征正常和AMD类。线性配置系数（CC）和模式发生（PO）特征是从眼底图像中提取的。这些提取的特征在于使用T检验的p值并馈送到各种监督分类器viz。决策树（DT），最近邻居（K-NN），幼稚贝叶斯（NB），概率神经网络（PNN）和支持向量机（SVM）来分类正常和AMD类。使用私人（Kasturba医疗医院，Manipal，India）和公共域数据集viz来评估系统的性能。使用十倍交叉验证自动视网膜图像分析（ARIA）和视网膜（凝视）的结构化分析。该方法的性能最高，平均精度最高，灵敏度为97.78％，灵敏度为98.00％，特异性为22个显着特征的凝视数据集97.50％。因此，该系统可以用作临床医生期间诊断AMD的临床医生的辅助工具。 （c）2015 Elsevier Ltd.保留所有权利。

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