Hard Exudate Detection Using Local Texture Analysis and Gaussian Processes. Colomer, A., Ruiz, P., Naranjo, V., Molina, R., & Katsaggelos, A. K. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10882 LNCS, pages 639–649, 2018. ![Hard Exudate Detection Using Local Texture Analysis and Gaussian Processes [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Exudates are the most noticeable sign in the first stage of diabetic retinopathy. This disease causes about five percent of world blindness. Making use of retinal fundus images, exudates can be detected, which helps the early diagnosis of the pathology. In this work, a novel method for automatic hard exudate detection is presented. After an exhaustive pre-processing step, Local Binary Patterns Variance (LBPV) histograms are used to locally extract texture information. We then use Gaussian Processes to distinguish between healthy and pathological retinal patches. The proposed methodology is validated using the E-OPHTA exudates database. The experimental results demonstrate that Gaussian Process classifiers outperform the current state of the art classifiers for this problem.
@inproceedings{Adrian2018,
abstract = {Exudates are the most noticeable sign in the first stage of diabetic retinopathy. This disease causes about five percent of world blindness. Making use of retinal fundus images, exudates can be detected, which helps the early diagnosis of the pathology. In this work, a novel method for automatic hard exudate detection is presented. After an exhaustive pre-processing step, Local Binary Patterns Variance (LBPV) histograms are used to locally extract texture information. We then use Gaussian Processes to distinguish between healthy and pathological retinal patches. The proposed methodology is validated using the E-OPHTA exudates database. The experimental results demonstrate that Gaussian Process classifiers outperform the current state of the art classifiers for this problem.},
author = {Colomer, Adri{\'{a}}n and Ruiz, Pablo and Naranjo, Valery and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-319-93000-8_73},
isbn = {9783319929996},
issn = {16113349},
keywords = {Bayesian modeling,Gaussian Processes,Hard exudate,Local Binary Patterns,Variational inference},
pages = {639--649},
title = {{Hard Exudate Detection Using Local Texture Analysis and Gaussian Processes}},
url = {http://link.springer.com/10.1007/978-3-319-93000-8_73},
volume = {10882 LNCS},
year = {2018}
}
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