Paper Info
Title
A diagnostic information based framework for super-resolution and quality assessment of retinal OCT images
Abstract
High-resolution (HR) retinal optical coherence tomography (OCT) images are preferred by the ophthalmologists to diagnose retinal diseases. These images can be obtained by dense scanning of the target retinal region during acquisition. However, a dense scanning increases the image acquisition time and introduces motion artefacts, which corrupt diagnostic information. Therefore, researchers have a growing interest in developing image processing techniques to recover HR images from low-resolution (LR) OCT images. In this paper, we present an automated super-resolution (SR) scheme using diagnostic information weighted sparse representation framework to reconstruct HR images from LR OCT images. The proposed method performs fast and reliable reconstruction of the LR images. We also propose a 2D-variational mode decomposition (VMD) based OCT diagnostic distortion measure (Q(OCT)) to quantify diagnostic distortion in the reconstructed OCT images. The SR method is evaluated on clinical grade OCT images with the proposed diagnostic distortion measure along with the conventional non-diagnostic measures like the contrast to noise ratio (CNR), the equivalent number of looks (ENL) and the peak signal to noise ratio (PSNR). The results show an average CNR of 4.07, ENL of 58.96 and PSNR of 27.72 dB. An average score of 1.53 is obtained using the proposed diagnostic distortion measure. Experimental results quantify that the proposed Q(OCT) metric can effectively capture diagnostic distortion.
Year
DOI
Venue
2021
10.1016/j.compmedimag.2021.101997
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
Keywords
DocType
Volume
Optical coherence tomography, Diagnostic distortion, Sparse representation, Super-resolution, Variational mode decomposition
Journal
94
ISSN
Citations 
PageRank 
0895-6111
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Vineeta Das152.56
S. Dandapat226128.51
Prabin Kumar Bora352.22