SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising

Retinal fundus images are used for the diagnosis and treatment of various eye diseases such as diabetic retinopathy, glaucoma, exudates and so on. The retinal vasculature is difficult to investigate retinal conditions due to the presence of various noises in the retinal image during the capture of t...

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Vydané v:	IET image processing Ročník 13; číslo 14; s. 2778 - 2789
Hlavní autori:	Ghosh, Swarup Kr, Biswas, Biswajit, Ghosh, Anupam
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	The Institution of Engineering and Technology 12.12.2019
Predmet:	biomedical optical imaging blood vessels convolution deep learning denoising effect denoising images diabetic retinopathy diseases eye eye diseases image denoising image representation image segmentation learning (artificial intelligence) medical image processing neural nets noise level noisy fundus novel stack deep convolutional autoencoder patched base training restoring features retinal conditions retinal fundus images retinal image denoising retinal vasculature Special Section: Adversarial Learning in Image Processing standard fundus databases target image visibility retinal image denoising retinal fundus images noisy fundus deep learning eye diseases image segmentation retinal conditions convolution noise level standard fundus databases learning (artificial intelligence) medical image processing denoising images visibility blood vessels restoring features diseases diabetic retinopathy image denoising target image eye denoising effect patched base training image representation novel stack deep convolutional autoencoder retinal vasculature neural nets biomedical optical imaging
ISSN:	1751-9659, 1751-9667
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Popis
Shrnutí:	Retinal fundus images are used for the diagnosis and treatment of various eye diseases such as diabetic retinopathy, glaucoma, exudates and so on. The retinal vasculature is difficult to investigate retinal conditions due to the presence of various noises in the retinal image during the capture of the image. Removal of noise is an important aspect for better visibility and diagnosis of the noisy fundus in ophthalmology. This study represents a deep learning based approach to denoising images and restoring features using stack denoising convolutional autoencoder. The proposed scheme is implemented to restore the structural details of fundus as well as to decrease the noise level. Furthermore, the proposed model utilises shared layers with the optimal manner to reduce the noise level of the target image with minimal computational cost. To restore an image, the proposed model brings a patched base training on samples to suppress with one to one manner without any loss of information. To access the denoising effect of the proposed scheme, several standard fundus databases such as DRIVE, STARE and DIARETDB1 have been tested in this study. Comparing the efficiency of the suggested model with state-of-art methods, the proposed scheme gives better result in terms of qualitative and quantitative analysis.
ISSN:	1751-9659 1751-9667
DOI:	10.1049/iet-ipr.2018.6582