Local configuration pattern features for age-related macular degeneration characterization and classification

Muthu Rama Krishnan Mookiah, U. Rajendra Acharya, Hamido Fujita, Joel E.W. Koh, Jen Hong Tan, Kevin Noronha, Sulatha V. Bhandary, Chua Kuang Chua, Choo Min Lim, Augustinus Laude, Louis Tong

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)208-218
Number of pages11
JournalComputers in Biology and Medicine
Volume63
DOIs
Publication statusPublished - 01-08-2015

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Macular Degeneration
Screening
Decision trees
Image analysis
Support vector machines
Classifiers
Neural networks
Retina
Geographic Atrophy
Choroidal Neovascularization
Decision Trees
Mass Screening
Public Sector
Disease Progression
India
Sensitivity and Specificity

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

Cite this

Mookiah, Muthu Rama Krishnan ; Acharya, U. Rajendra ; Fujita, Hamido ; Koh, Joel E.W. ; Tan, Jen Hong ; Noronha, Kevin ; Bhandary, Sulatha V. ; Chua, Chua Kuang ; Lim, Choo Min ; Laude, Augustinus ; Tong, Louis. / Local configuration pattern features for age-related macular degeneration characterization and classification. In: Computers in Biology and Medicine. 2015 ; Vol. 63. pp. 208-218.
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abstract = "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.",
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Mookiah, MRK, Acharya, UR, Fujita, H, Koh, JEW, Tan, JH, Noronha, K, Bhandary, SV, Chua, CK, Lim, CM, Laude, A & Tong, L 2015, 'Local configuration pattern features for age-related macular degeneration characterization and classification', Computers in Biology and Medicine, vol. 63, pp. 208-218. https://doi.org/10.1016/j.compbiomed.2015.05.019

Local configuration pattern features for age-related macular degeneration characterization and classification. / Mookiah, Muthu Rama Krishnan; Acharya, U. Rajendra; Fujita, Hamido; Koh, Joel E.W.; Tan, Jen Hong; Noronha, Kevin; Bhandary, Sulatha V.; Chua, Chua Kuang; Lim, Choo Min; Laude, Augustinus; Tong, Louis.

In: Computers in Biology and Medicine, Vol. 63, 01.08.2015, p. 208-218.

Research output: Contribution to journalArticle

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AU - Mookiah, Muthu Rama Krishnan

AU - Acharya, U. Rajendra

AU - Fujita, Hamido

AU - Koh, Joel E.W.

AU - Tan, Jen Hong

AU - Noronha, Kevin

AU - Bhandary, Sulatha V.

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AU - Lim, Choo Min

AU - Laude, Augustinus

AU - Tong, Louis

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AB - 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.

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