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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Bibliographic Details
Published in:IET image processing Vol. 13; no. 14; pp. 2778 - 2789
Main Authors: Ghosh, Swarup Kr, Biswas, Biswajit, Ghosh, Anupam
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 12.12.2019
Subjects:
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
Online Access:Get full text
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Description
Summary: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