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

Muthu Rama Krishnan Mookiah1, U Rajendra Acharya2, Hamido Fujita3

  • 1Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.

Computers in Biology and Medicine
|June 22, 2015
PubMed
Summary

This study introduces a new method using Linear Configuration Coefficients (LCP) and Pattern Occurrence (PO) features from fundus images for automated Age-related Macular Degeneration (AMD) diagnosis. The system achieved high accuracy, aiding clinicians in mass eye screening.

Keywords:
Age-related macular degenerationFundus imagingLocal configuration patternRetinaSupport vector machine

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Area of Science:

  • Ophthalmology and Medical Imaging

Background:

  • Age-related Macular Degeneration (AMD) is a leading cause of irreversible vision loss in the elderly, affecting central vision due to macular cell degeneration.
  • AMD presents in dry and wet forms, with dry AMD being more prevalent, and early detection is crucial to slow disease progression.

Purpose of the Study:

  • To develop an automated system for diagnosing Age-related Macular Degeneration (AMD) using fundus images, aiming to reduce clinician screening time.
  • To characterize normal and AMD classes by extracting and analyzing specific image features.

Main Methods:

  • Extracted Linear Configuration Coefficients (CC) and Pattern Occurrence (PO) features from fundus images.
  • Ranked features using p-value of t-test and employed various supervised classifiers including Decision Tree, k-NN, Naive Bayes, PNN, and SVM.
  • Evaluated performance on private and public datasets (ARIA, STARE) using ten-fold cross-validation.

Main Results:

  • The proposed approach achieved a highest average accuracy of 97.78%, sensitivity of 98.00%, and specificity of 97.50% on the STARE dataset.
  • The system effectively classified normal and AMD classes using 22 significant features.

Conclusions:

  • The developed automated AMD diagnosis system demonstrates high performance and reliability.
  • This system can serve as a valuable aiding tool for clinicians in large-scale eye screening programs for early AMD detection.