Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features
Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features
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DOI：
10.1016/j.compbiomed.2016.04.009
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发表时间：
2016-06-01
影响因子：
7.7
通讯作者：
Laude, Augustinus
中科院分区：
文献类型：
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作者：
Acharya, U. Rajendra;Mookiah, Muthu Rama Krishnan;Laude, Augustinus
老年性黄斑变性（AMD）影响老年人的中心视力。由于眼底图像中出现结节、地理萎缩（GA）和脉络膜新生血管（CNV），可以诊断。对于眼科医生来说，筛查这些图像是一项费时费力的工作。基于眼底摄影的自动数字筛选系统可以克服这些缺点。这种安全、非接触式、性价比高的平台可以作为干性AMD的筛选系统。本文提出了一种基于Radon变换（RT）、离散小波变换（DINT）和局部敏感判别分析（LSDA）的AMD自动诊断算法。首先对图像进行RT，然后进行DWT。提取的特征使用LSDA进行降维，使用t检验进行排序。各种监督分类器的性能，即决策树（DT），支持向量机（SVM），概率神经网络（PNN）和k-近邻（k-NN）进行比较，自动区分正常和AMD类使用排名的LSDA组件。使用私有和公共数据集（如ARIA和STARE）对所提出的方法进行了评估。private、ARIA和STARE数据集的分类准确率分别为99.49%、96.89%和100%。同时，利用两个LSDA分量设计AMD指数，准确区分两类。因此，该系统可以扩展到大规模AMD筛查。(C) 2016 Elsevier Ltd.版权所有。
Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DINT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening. (C) 2016 Elsevier Ltd. All rights reserved.