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

Mookiah MR 1 , Acharya UR 2 , Fujita H 3 , Koh JE 4 , Tan JH 4 , Noronha K 5 Show all authors , Bhandary SV 6 , Chua CK 4 , Lim CM 7 , Laude A 8 , Tong L 9

Affiliations 

  • 1 Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore. Electronic address: mkm2@np.edu.sg
  • 2 Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
  • 3 Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate 020-0693, Japan
  • 4 Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
  • 5 Department of Electronics and Telecommunication, St. Francis Institute of Technology, Mumbai 400103, India
  • 6 Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India
  • 7 School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
  • 8 National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
  • 9 Singapore National Eye Center, Singapore 168751, Singapore; Ocular Surface Research Group, Singapore Eye Research Institute, Singapore 168751, Singapore; Duke-NUS Graduate Medical School, Singapore 169857, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
Comput Biol Med, 2015 Aug;63:208-18.
PMID: 26093788 DOI: 10.1016/j.compbiomed.2015.05.019

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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