A novel color space of fundus images for automatic exudates detection

被引：16

作者：

Khojasteh, Parham ^{[1
]}

Aliahmad, Behzad ^{[1
]}

Kumar, Dinesh Kant ^{[1
]}

机构：

[1] RMIT Univ, Sch Engn, Biosignal Lab, Melbourne, Vic, Australia

来源：

BIOMEDICAL SIGNAL PROCESSING AND CONTROL | 2019年 / 49卷

关键词：

Exudate detection; Convolutional neural networks; Color space representation; Machine learning; Retinal image analysis; Deep learning; DIABETIC-RETINOPATHY; RETINAL IMAGES; NEURAL-NETWORKS; SEGMENTATION; IDENTIFICATION; PHOTOGRAPHS; DIAGNOSIS;

D O I：

10.1016/j.bspc.2018.12.004

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：240 / 249

页数：10

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