Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features. (1st June 2016)

• Record Type: Journal Article
• Authors: Acharya, U. Rajendra; Mookiah, Muthu Rama Krishnan; Koh, Joel E.W.; Tan, Jen Hong; Noronha, Kevin; Bhandary, Sulatha V.; Rao, A. Krishna; Hagiwara, Yuki; Chua, Chua Kuang; Laude, Augustinus
• Is Part Of: Computers in biology and medicine. Volume 73(2016)
• Page Start: 131
• Page End: 140
• Publication Date: 2016-06-01
• Subjects: Fundus imaging -- Age-related macular degeneration -- Radon transform -- Discrete wavelet transform -- Locality sensitive discriminant analysis -- Computed aided diagnosis; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.04.009
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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 (DWT) 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. Highlights: Automated detection of normal and AMD classes using fundus images. Radon transform and discrete wavelet transform are used for feature extraction. Proposed method is evaluated using private and public (ARIA and STARE) datasets. Obtained highest classification accuracy of 100% for STARE dataset. AMD risk index is proposed to discriminate the two classes using a single number.