Abstract
Recently, Glaucoma has become one of the major retinal diseases. In order to detect such retinal diseases, cup to disc ratio measurement is a vital index of Glaucoma, as the Glaucomatous neuropathy increases the cup to disc ratio when the excavation of the optic cup is increased. In this paper, a semi-automated system to detect both of optic cup and optic disc and to measure cup to disc ratio has been proposed. The proposed system firstly, uses an object detection function from red channel of the retinal images. Then further using threshold values, the optic cup and optic disc are detected. Although, for several images manual tuning is needed as the object detection function as well as the threshold value fail to detect the optic cup and optic disc correctly. The manually tuned images and the automatically detected images are further used to determine the error in the system which leads to the categorizing of the images. These images are later post-processed using Haralick texture features. Haralick texture features’ obtained values are trained using back propagation neural network to determine the system’s accuracy. The proposed system was evaluated using RIM-ONE database. By increasing the absolute error, system’s accuracy is evaluated. The proposed system’s accuracy is 86.43 % at 0.5 error value.
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Acknowledgments
This paper has been elaborated in the framework of the project “New creative teams in priorities of scientific research”, reg. no. CZ.1.07/2.3.00/30.0055, supported by Operational Programme Education for Competitiveness and co-financed by the European Social Fund and the state budget of the Czech Republic and supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme and by Project SP2015/146 Parallel processing of Big data 2 of the Student Grant System, VSB Technical University of Ostrava.
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Kotyk, T., Chakraborty, S., Dey, N., Gaber, T., Hassanien, A.E., Snasel, V. (2016). Semi-automated System for Cup to Disc Measurement for Diagnosing Glaucoma Using Classification Paradigm. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Advances in Intelligent Systems and Computing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-29504-6_60
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