A novel color space of fundus images for automatic exudates detection

Khojasteh, P, Aliahmad, B and Kumar, D 2019, 'A novel color space of fundus images for automatic exudates detection', Biomedical signal processing and control, vol. 49, pp. 240-249.


Document type: Journal Article
Collection: Journal Articles

Title A novel color space of fundus images for automatic exudates detection
Author(s) Khojasteh, P
Aliahmad, B
Kumar, D
Year 2019
Journal name Biomedical signal processing and control
Volume number 49
Start page 240
End page 249
Total pages 10
Publisher Elsevier
Abstract This paper has compared the performance of different color spaces of fundus images for automatic detection of exudates. A convolutional neural network was employed to assess the performances of different color spaces generated by orthogonal transformation of the original colors in red/green/blue (RGB) space. Experiments were conducted on two publicly available databases: (1) DIARETDB1 and (2) e-Ophtha. Based on the experimental results, this study has proposed a new color space of fundus images with three channels: (i) second eigenchannel of the RGB space, (ii) hue and (iii) saturation channels of Hue/Saturation and Intensity (HSI) space. This achieved an accuracy, sensitivity and specificity of 98.2%, 0.99 and 0.98, respectively. Twenty times 20-fold cross validation technique confirmed that proposed color space obtained higher replicability compared with conventional color spaces.
Subject Biomedical Engineering not elsewhere classified
Signal Processing
Keyword(s) Color space representation
Convolutional neural networks
Deep learning
Exudate detection
Machine learning
Retinal image analysis
DOI - identifier 10.1016/j.bspc.2018.12.004
Copyright notice © 2018 Elsevier Ltd. All rights reserved.
ISSN 1746-8094
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