Detection of keratoconus in anterior segment photographed images using corneal curvature features
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 th...
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| Main Authors: | , , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2019-03-01.
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| LEADER | 02903 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | ijeecs17002_10863 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Mat Daud, Marizuana |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Wan Zaki, Wan Mimi Diyana |e author |
| 700 | 1 | 0 | |a Hussain, Aini |e author |
| 700 | 1 | 0 | |a Mutalib, Haliza Abdul |e author |
| 245 | 0 | 0 | |a Detection of keratoconus in anterior segment photographed images using corneal curvature features |
| 260 | |b Institute of Advanced Engineering and Science, |c 2019-03-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/17002 | ||
| 520 | |a 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, , crossover point, , and trigonometric, , 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, . Additionally, classification with the proposed 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. | ||
| 540 | |a Copyright (c) 2018 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Keratoconus , Anterior Segment, Photographed Images, Corneal curvatu,re Template disc | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 13, No 3: March 2019; 1191-1198 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v13.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/17002/10863 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/17002/10863 |z Get fulltext |