Paper Info
Title
Local configuration pattern features for age-related macular degeneration characterization and classification
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. HighlightsAutomated detection of age-related macular degeneration (AMD) using fundus images.Features are extracted using local configuration pattern (LCP) method.Ranked features are subjected to various classifiers.Proposed method classifies two classes with 97.80% accuracy
Year
DOI
Venue
2015
10.1016/j.compbiomed.2015.05.019
Computers in Biology and Medicine
Keywords
Field
DocType
Retina,Age-related macular degeneration,Fundus imaging,Local configuration pattern,Support vector machine
Computer vision,Choroidal neovascularization,Naive Bayes classifier,Pattern recognition,Computer science,Support vector machine,Fundus (eye),Drusen,Probabilistic neural network,Macular degeneration,Artificial intelligence,Cross-validation
Journal
Volume
Issue
ISSN
63
C
0010-4825
Citations 
PageRank 
References 
15
0.59
31
Authors
11
Name
Order
Citations
PageRank
Muthu Rama Krishnan Mookiah12359.94
Rajendra Acharya U24666296.34
Hamido Fujita32644185.03
Joel E. W. Koh426619.06
Jen-Hong Tan574532.04
Kevin Noronha61014.69
Sulatha V. Bhandary727113.76
Chua Kuang Chua854526.57
Choo Min Lim944628.35
Augustinus Laude1022111.99
Louis Tong111658.22