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Computer-Aided Diagnosis of Anterior Segment Eye Abnormalities using Visible Wavelength Image Analysis Based Machine

Mahesh Kumar S V1, Gunasundari R2

  • 1Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry, India. svmaheshkumar@pec.edu.

Journal of Medical Systems
|June 4, 2018
PubMed
Summary

This study introduces a computer-aided diagnosis (CAD) system for detecting anterior segment eye abnormalities in elderly individuals using visible wavelength (VW) images. The novel method achieves high accuracy, aiding in early detection and screening for conditions like cataracts.

Keywords:
CataractClassifierConfusion matrixCorneal arcusEyeFeature extractionVW eye imagesWavelet transform

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Area of Science:

  • Ophthalmology
  • Computer Science
  • Medical Imaging

Background:

  • Eye diseases, particularly cataracts and corneal arcus, significantly impact the elderly population.
  • Early detection and grading of anterior segment eye abnormalities are crucial for effective ophthalmological care.
  • Computer-aided diagnosis (CAD) systems offer potential for mass screening and objective assessment.

Purpose of the Study:

  • To develop and evaluate a multiclass computer-aided diagnosis (CAD) system for anterior segment eye abnormalities.
  • To utilize visible wavelength (VW) eye images for automated diagnosis.
  • To assess the system's accuracy, sensitivity, and specificity for clinical application.

Main Methods:

  • A CAD system was developed using visible wavelength (VW) eye images.
  • Image pre-processing included specular reflection removal.
  • Iris circle segmentation was performed using a circular Hough Transform (CHT).
  • Feature extraction involved first-order statistics and wavelet-based features.
  • Classification was conducted using a Support Vector Machine (SVM) with the Sequential Minimal Optimization (SMO) algorithm.

Main Results:

  • The system was tested on 228 VW eye images across three classes of anterior segment eye abnormalities.
  • The proposed method achieved a high predictive accuracy of 96.96%.
  • The system demonstrated excellent performance with 97% sensitivity and 99% specificity.

Conclusions:

  • The developed computer-aided diagnosis system shows significant potential for clinical applications in ophthalmology.
  • The method provides an accurate and efficient approach for screening and grading anterior segment eye abnormalities.
  • The use of VW images and advanced feature extraction/classification techniques contributes to the system's high diagnostic performance.