Healthc Inform Res.  2018 Jan;24(1):53-60. 10.4258/hir.2018.24.1.53.

Automatic Glaucoma Detection Method Applying a Statistical Approach to Fundus Images

• Septiarini A
• Khairina D
• Kridalaksana AH
• Hamdani H

• KMID: 2403296
• DOI: http://doi.org/10.4258/hir.2018.24.1.53

Abstract

OBJECTIVES
Glaucoma is an incurable eye disease and the second leading cause of blindness in the world. Until 2020, the number of patients of this disease is estimated to increase. This paper proposes a glaucoma detection method using statistical features and the k-nearest neighbor algorithm as the classifier.
METHODS
We propose three statistical features, namely, the mean, smoothness and 3rd moment, which are extracted from images of the optic nerve head. These three features are obtained through feature extraction followed by feature selection using the correlation feature selection method. To classify those features, we apply the k-nearest neighbor algorithm as a classifier to perform glaucoma detection on fundus images.
RESULTS
To evaluate the performance of the proposed method, 84 fundus images were used as experimental data consisting of 41 glaucoma image and 43 normal images. The performance of our proposed method was measured in terms of accuracy, and the overall result achieved in this work was 95.24%, respectively.
CONCLUSIONS
This research showed that the proposed method using three statistics features achieves good performance for glaucoma detection.

Keyword

Glaucoma; Retinal Degeneration; Optic Neuropathy; Fundus; Classification

MeSH Terms

Blindness
Classification
Eye Diseases
Glaucoma*
Humans
Methods*
Optic Disk
Optic Nerve Diseases
Retinal Degeneration

Figure

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  Figure 1 Examples of original image (A) and region of interest (ROI) image of optic nerve head (B). OHN: optic nerve head, PPA: parapapillary atrophy, RNFL: retinal nerve fiber layer.

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  Figure 2 Main stages of our proposed method. ROI: region of interest, ONH: optic nerve head.

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  Figure 3 Features extraction results of several images.

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