Detection of keratoconus in anterior segment photographed images using corneal curvature features

Research output: Contribution to journalArticle

Abstract

Keratoconus is a corneal ectatic disorder with complex aetiology and may induce mild to severe visual impairment and consequently decrease the quality of life. This paper presents a new keratoconus detection method using corneal curvature features to differentiate normal and keratoconus cases. In this study, the eye images known as anterior segmented photographed images (ASPIs) are captured from side view using a smartphone’s camera. For the side-view images, the corneal curvature is segmented using spline function to measure the corneal curvature. A template disc method is implemented to quantitatively measure the steepening of the corneal curvature of the captured ASPIs. Parameters obtained from three different template disc methods, namely, nonlinear, C nl , crossover point, C cp , and trigonometric, C tr , are investigated to represent the most suitable curvature feature. SVM is then employed to classify normal and keratoconus eyes. Results reveal that a standalone nonlinear method gives a reliable parameter with 90% accuracy in classifying the data. However, the classification performance has increased to 99.5% accuracy with the use of all combined features known as a feature vector, f c =<C nl ,C cp , C tr <. Additionally, classification with the proposed f c has successfully distinguished normal and keratoconus cases with sensitivity and specificity rates of 99% and 100%, respectively. The results portray the bright potential of this method in assisting experts during ocular screening specifically to detect keratoconus disease.

Original languageEnglish
Pages (from-to)1191-1198
Number of pages8
JournalIndonesian Journal of Electrical Engineering and Computer Science
Volume13
Issue number3
DOIs
Publication statusPublished - 1 Mar 2019

Fingerprint

Curvature
Smartphones
Splines
Screening
Cameras
Template
Visual Impairment
Quality of Life
Spline Functions
Feature Vector
Differentiate
Specificity
Crossover
Disorder
Camera
Classify
Decrease

Keywords

  • Anterior segment
  • Corneal curvature
  • Keratoconus
  • Photographed images
  • Template disc

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization
  • Electrical and Electronic Engineering

Cite this

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title = "Detection of keratoconus in anterior segment photographed images using corneal curvature features",
abstract = "Keratoconus is a corneal ectatic disorder with complex aetiology and may induce mild to severe visual impairment and consequently decrease the quality of life. This paper presents a new keratoconus detection method using corneal curvature features to differentiate normal and keratoconus cases. In this study, the eye images known as anterior segmented photographed images (ASPIs) are captured from side view using a smartphone’s camera. For the side-view images, the corneal curvature is segmented using spline function to measure the corneal curvature. A template disc method is implemented to quantitatively measure the steepening of the corneal curvature of the captured ASPIs. Parameters obtained from three different template disc methods, namely, nonlinear, C nl , crossover point, C cp , and trigonometric, C tr , are investigated to represent the most suitable curvature feature. SVM is then employed to classify normal and keratoconus eyes. Results reveal that a standalone nonlinear method gives a reliable parameter with 90{\%} accuracy in classifying the data. However, the classification performance has increased to 99.5{\%} accuracy with the use of all combined features known as a feature vector, f c =<C nl ,C cp , C tr <. Additionally, classification with the proposed f c has successfully distinguished normal and keratoconus cases with sensitivity and specificity rates of 99{\%} and 100{\%}, respectively. The results portray the bright potential of this method in assisting experts during ocular screening specifically to detect keratoconus disease.",
keywords = "Anterior segment, Corneal curvature, Keratoconus, Photographed images, Template disc",
author = "Daud, {Marizuana Mat} and {Wan Zaki}, {Wan Mimi Diyana} and Aini Hussain and {Abdul Mutalib}, Haliza",
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N2 - Keratoconus is a corneal ectatic disorder with complex aetiology and may induce mild to severe visual impairment and consequently decrease the quality of life. This paper presents a new keratoconus detection method using corneal curvature features to differentiate normal and keratoconus cases. In this study, the eye images known as anterior segmented photographed images (ASPIs) are captured from side view using a smartphone’s camera. For the side-view images, the corneal curvature is segmented using spline function to measure the corneal curvature. A template disc method is implemented to quantitatively measure the steepening of the corneal curvature of the captured ASPIs. Parameters obtained from three different template disc methods, namely, nonlinear, C nl , crossover point, C cp , and trigonometric, C tr , are investigated to represent the most suitable curvature feature. SVM is then employed to classify normal and keratoconus eyes. Results reveal that a standalone nonlinear method gives a reliable parameter with 90% accuracy in classifying the data. However, the classification performance has increased to 99.5% accuracy with the use of all combined features known as a feature vector, f c =<C nl ,C cp , C tr <. Additionally, classification with the proposed f c has successfully distinguished normal and keratoconus cases with sensitivity and specificity rates of 99% and 100%, respectively. The results portray the bright potential of this method in assisting experts during ocular screening specifically to detect keratoconus disease.

AB - Keratoconus is a corneal ectatic disorder with complex aetiology and may induce mild to severe visual impairment and consequently decrease the quality of life. This paper presents a new keratoconus detection method using corneal curvature features to differentiate normal and keratoconus cases. In this study, the eye images known as anterior segmented photographed images (ASPIs) are captured from side view using a smartphone’s camera. For the side-view images, the corneal curvature is segmented using spline function to measure the corneal curvature. A template disc method is implemented to quantitatively measure the steepening of the corneal curvature of the captured ASPIs. Parameters obtained from three different template disc methods, namely, nonlinear, C nl , crossover point, C cp , and trigonometric, C tr , are investigated to represent the most suitable curvature feature. SVM is then employed to classify normal and keratoconus eyes. Results reveal that a standalone nonlinear method gives a reliable parameter with 90% accuracy in classifying the data. However, the classification performance has increased to 99.5% accuracy with the use of all combined features known as a feature vector, f c =<C nl ,C cp , C tr <. Additionally, classification with the proposed f c has successfully distinguished normal and keratoconus cases with sensitivity and specificity rates of 99% and 100%, respectively. The results portray the bright potential of this method in assisting experts during ocular screening specifically to detect keratoconus disease.

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