Local configuration pattern features for age-related macular degeneration characterization and classification
Local configuration pattern features for age-related macular degeneration characterization and classification
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DOI：
10.1016/j.compbiomed.2015.05.019
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发表时间：
2015-08-01
影响因子：
7.7
通讯作者：
Tong, Louis
中科院分区：
文献类型：
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作者：
Mookiah, Muthu Rama Krishnan;Acharya, U. Rajendra;Tong, Louis
年龄相关性黄斑变性（AMD）是一种不可逆的慢性疾病，其特征是黄斑变性、脉络膜新生血管（CNV）和地理萎缩（GA）。黄斑变性是老年人视力丧失的主要原因之一。它是由负责中央视力的黄斑细胞退化引起的。AMD可以是干型或湿型，但干型AMD最常见。AMD分为早期、中期和晚期。早期发现和治疗可能有助于阻止疾病的发展。自动诊断AMD可减少临床医生的筛查时间。在这项工作中，我们使用眼底图像引入LCP来表征正常和AMD类别。从眼底图像中提取线性配置系数（CC）和模式发生（PO）特征。这些提取的特征使用t检验的p值进行排序，并馈送到各种监督分类器，即决策树（DT），最近邻（k-NN），朴素贝叶斯（NB），概率神经网络（PNN）和支持向量机（SVM）来分类正常和AMD类。该系统的性能使用私人（印度马尼帕尔Kasturba医疗医院）和公共领域数据集进行评估，即使用十倍交叉验证的自动视网膜图像分析（ARIA）和视网膜结构化分析（STARE）。该方法在22个显著特征的STARE数据集上获得了最高的平均准确率97.78%、灵敏度98.00%和特异性97.50%的最佳性能。因此，该系统可作为临床医生在大规模眼部筛查项目中诊断AMD的辅助工具。(C) 2015 Elsevier Ltd.版权所有。
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.