Healthcare Informatics Research

Korean Society of Medical Infomatics (대한의료정보학회)
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Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation

  • Septiarini, Anindita (Department of Computer Science, Faculty of Computer Science and Information Technology, Mulawarman University) ;
  • Harjoko, Agus (Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada) ;
  • Pulungan, Reza (Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada) ;
  • Ekantini, Retno (Faculty of Medicine, Universitas Gadjah Mada)
  • Received : 2018.02.27
  • Accepted : 2018.07.15
  • Published : 2018.10.31
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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.

Keywords

Acknowledgement

Supported by : RISTEKDIKTI Indonesia

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  1. Detection of glaucoma using retinal fundus images: A comprehensive review vol.18, pp.3, 2018, https://doi.org/10.3934/mbe.2021106