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Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques
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DOI：10.1016/j.compbiomed.2018.11.028.png)
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Abstract
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Background and objective: Glaucoma is a ocular disorder which causes irreversible damage to the retinal nerve fibers. The diagnosis of glaucoma is important as it may help to slow down the progression. The available clinica methods and imaging techniques are manual and require skilled supervision. For the purpose of mass screening an automated system is needed for glaucoma diagnosis which is fast, accurate, and helps in reducing the burdei on experts. Methods: In this work, we present a bit-plane slicing (BPS) and local binary pattern (LBP) based novel approad for glaucoma diagnosis. Firstly, our approach separates the red (R), green (G), and blue (B) channels from th input color fundus image and splits the channels into bit planes. Secondly, we extract LBP based statistics features from each of the bit planes of the individual channels. Thirdly, these features from the individua channels are fed separately to three different support vector machines (SVMs) for classification. Finally, th. decisions from the individual SVMs are fused at the decision level to classify the input fundus image into norma or glaucoma class. Results: Our experimental results suggest that the proposed approach is effective in discriminating normal am glaucoma cases with an accuracy of 99.30% using 10-fold cross validation. Conclusions: The developed system is ready to be tested on large and diverse databases and can assist th ophthalmologists in their daily screening to confirm their diagnosis, thereby increasing accuracy of diagnosis.
Keywords:
Glaucoma
Bit-plane slicing
Local binary pattern
Support vector machine
Decision level fusion
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