Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features

U. Rajendra Acharya, Muthu Rama Krishnan Mookiah, Joel E.W. Koh, Jen Hong Tan, Kevin Noronha, Sulatha V. Bhandary, A. Krishna Rao, Yuki Hagiwara, Chua Kuang Chua, Augustinus Laude

Research output: Contribution to journalArticle

30 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)131-140
Number of pages10
JournalComputers in Biology and Medicine
Volume73
DOIs
Publication statusPublished - 01-06-2016

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Wavelet Analysis
Radon
Discrete wavelet transforms
Macular Degeneration
Discriminant analysis
Discriminant Analysis
Screening
Mathematical transformations
Photography
Decision trees
Geographic Atrophy
Support vector machines
Choroidal Neovascularization
Decision Trees
Classifiers
Personnel
Neural networks
Costs
Costs and Cost Analysis

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

Cite this

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. / Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features. In: Computers in Biology and Medicine. 2016 ; Vol. 73. pp. 131-140.
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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.",
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Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features. / 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.

In: Computers in Biology and Medicine, Vol. 73, 01.06.2016, p. 131-140.

Research output: Contribution to journalArticle

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AU - Mookiah, Muthu Rama Krishnan

AU - Koh, Joel E.W.

AU - Tan, Jen Hong

AU - Noronha, Kevin

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AU - Laude, Augustinus

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