Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation.
10.4258/hir.2018.24.4.335
- Author:
Anindita SEPTIARINI
1
;
Agus HARJOKO
;
Reza PULUNGAN
;
Retno EKANTINI
Author Information
1. Department of Computer Science, Faculty of Computer Science and Information Technology, Mulawarman University, Samarinda, Indonesia. anindita@unmul.ac.id
- Publication Type:Original Article
- Keywords:
Retinal Degeneration;
Glaucoma;
Fundus;
Nerve Fibers;
Optic Neuropathy
- MeSH:
Blindness;
Glaucoma*;
Methods;
Nerve Fibers*;
Optic Nerve Diseases;
Retinal Degeneration;
Retinaldehyde*
- From:Healthcare Informatics Research
2018;24(4):335-345
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: The retinal nerve fiber layer (RNFL) is a site of glaucomatous optic neuropathy whose early changes need to be detected because glaucoma is one of the most common causes of blindness. This paper proposes an automated RNFL detection method based on the texture feature by forming a co-occurrence matrix and a backpropagation neural network as the classifier. METHODS: We propose two texture features, namely, correlation and autocorrelation based on a co-occurrence matrix. Those features are selected by using a correlation feature selection method. Then the backpropagation neural network is applied as the classifier to implement RNFL detection in a retinal fundus image. RESULTS: We used 40 retinal fundus images as testing data and 160 sub-images (80 showing a normal RNFL and 80 showing RNFL loss) as training data to evaluate the performance of our proposed method. Overall, this work achieved an accuracy of 94.52%. CONCLUSIONS: Our results demonstrated that the proposed method achieved a high accuracy, which indicates good performance.
