Automated Keratoconus Detection by 3D Corneal Images Reconstruction

This paper presents a technique for the detection of keratoconus via the construction of a 3D eye images from 2D frontal and lateral eye images. Keratoconus is a disease that affects the cornea. Normal case eyes have a round-shaped cornea, while patients who suffer from keratoconus have a cone-shape...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-03, Vol.21 (7), p.2326
Main Authors: Mahmoud, Hanan A Hosni, Mengash, Hanan Abdullah
Format: Article
Language:English
Subjects:
3D eye construction
Accuracy
Algorithms
Automation
Construction
Cornea
Cornea - diagnostic imaging
Corneal Topography
Datasets
depth calculation
Diagnosis
Digital imaging
Disease
Eye (anatomy)
Humans
Iris
Keratoconus - diagnostic imaging
keratoconus detection
Literature reviews
Machine Learning
Medical imaging
Methods
Neural networks
Optimization techniques
Smartphones
Three dimensional imaging
Topography
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Summary:This paper presents a technique for the detection of keratoconus via the construction of a 3D eye images from 2D frontal and lateral eye images. Keratoconus is a disease that affects the cornea. Normal case eyes have a round-shaped cornea, while patients who suffer from keratoconus have a cone-shaped cornea. Early diagnosis can decrease the risk of eyesight loss. Our aim is to create a method of fully automated keratoconus detection using digital-camera frontal and lateral eye images. The presented technique accurately determines case severity. Geometric features are extracted from 2D images to estimate depth information used to build 3D images of the cornea. The proposed methodology is easy to implement and time-efficient. 2D images of the eyes (frontal and lateral) are used as input, and 3D images from which the curvature of the cornea can be detected are produced as output. Our method involves two main steps: feature extraction and depth calculation. Machine learning from the 3D images dataset Dataverse, specifically taken by the Cornea/Anterior Segment OCT SS-1000 (CASIA), was performed. Results show that the method diagnosed the four stages of keratoconus (severe, moderate, mild, and normal) with an accuracy of 97.8%, as compared to manual diagnosis done by medical experts.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21072326