Abstract
Diabetic Retinopathy (DR) is a severe and widely spread eye disease. Exudates are one of the most prevalent signs during the early stage of DR and an early detection of these lesions is vital to prevent the patient’s blindness. Hence, detection of exudates is an important diagnostic task of DR, in which computer assistance may play a major role. In this paper, a system based on local feature extraction and Support Vector Machine (SVM) classification is used to develop and compare different strategies for automated detection of exudates. The main novelty of this work is allowing the detection of exudates using non-regular regions to perform the local feature extraction. To accomplish this objective, different methods for generating superpixels are applied to the fundus images of E-OPHTA database and texture and morphological features are extracted for each of the resulting regions. An exhaustive comparison among the proposed methods is also carried out.
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Acknowledgements
This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT-2016-2017, 732613]. The work of Adrián Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
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Pereira, J., Colomer, A., Naranjo, V. (2018). Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_19
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