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Open AccessArticle

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images

1
Institute of Research and Innovation in Bioengineering, I3B, Universitat Politècnica de València, 46022 Valencia, Spain
2
Departamento de Comunicaciones, ITEAM Research Institute, Universitat Politècnica de València, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 1005; https://doi.org/10.3390/s20041005 (registering DOI)
Received: 16 December 2019 / Revised: 30 January 2020 / Accepted: 10 February 2020 / Published: 13 February 2020
(This article belongs to the Special Issue Artificial Intelligence in Medical Sensors)
Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion.
Keywords: biomedical image processing; diabetic retinopathy; classification, granulometry-based descriptor, LBP, hand-driven learning, exudates, microaneurysms biomedical image processing; diabetic retinopathy; classification, granulometry-based descriptor, LBP, hand-driven learning, exudates, microaneurysms
MDPI and ACS Style

Colomer, A.; Igual, J.; Naranjo, V. Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images. Sensors 2020, 20, 1005.

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