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Abstract #78779 Published in IGR 20-1
Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation
Septiarini A; Harjoko A; Pulungan R; Ekantini RHealthcare informatics research 2018; 24: 335-345
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.
Department of Computer Science, Faculty of Computer Science and Information Technology, Mulawarman University, Samarinda, Indonesia.
Full articleClassification:
6.8.2 Posterior segment (Part of: 6 Clinical examination methods > 6.8 Photography)
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
2.13 Retina and retinal nerve fibre layer (Part of: 2 Anatomical structures in glaucoma)
