Modeling and Enhancing Low-Quality Retinal Fundus Images
- 9 December 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Medical Imaging
- Vol. 40 (3), 996-1006
- https://doi.org/10.1109/tmi.2020.3043495
Abstract
Retinal fundus images are widely used for the clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experience have a large variation in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. However, due to the special optical beam of fundus imaging and structure of the retina, natural image enhancement methods cannot be utilized directly to address this. In this paper, we first analyze the ophthalmoscope imaging system and simulate a reliable degradation of major inferior-quality factors, including uneven illumination, image blurring, and artifacts. Then, based on the degradation model, a clinically oriented fundus enhancement network (cofe-Net) is proposed to suppress global degradation factors, while simultaneously preserving anatomical retinal structures and pathological characteristics for clinical observation and analysis. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details. Moreover, we also show that the fundus correction method can benefit medical image analysis applications, e.g., retinal vessel segmentation and optic disc/cup detection.Keywords
This publication has 47 references indexed in Scilit:
- Sparse and low rank decomposition based batch image alignment for speckle reduction of retinal OCT imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Retinal vessel enhancement based on multi-scale top-hat transformation and histogram fitting stretchingOptics & Laser Technology, 2014
- Context-Based Automatic Local Image EnhancementLecture Notes in Computer Science, 2012
- A Total Variation Model for RetinexSIAM Journal on Imaging Sciences, 2011
- The impact of the Health Technology Board for Scotland's grading model on referrals to ophthalmology servicesBritish Journal of Ophthalmology, 2005
- Luminosity and contrast normalization in retinal imagesMedical Image Analysis, 2005
- An Iterative Regularization Method for Total Variation-Based Image RestorationMultiscale Modeling & Simulation, 2005
- Ridge-Based Vessel Segmentation in Color Images of the RetinaIEEE Transactions on Medical Imaging, 2004
- Contrast Limited Adaptive Histogram EqualizationPublished by Elsevier BV ,1994
- Restoration of retinal images obtained through cataractsIEEE Transactions on Medical Imaging, 1989