Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure

Shao, Feng; Yang, Yupei; Jiang, Qiuping; Jiang, Gangyi; Ho, Yo‐Sung

doi:10.1109/access.2017.2776126

Cited by 34 publications

(40 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…employed features based on the human visual system, with a support vector machine (SVM) or a decision tree to identify high-quality images [16]. A fundus image quality classifier that analyzes illumination, naturalness, and structure was also provided to assess quality [14]. Recently, deep learning techniques that integrate multi-level representations have been shown to obtain significant performances in a wide variety of medical imaging tasks.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces

Wang

Shen

et al. 2019

Lecture Notes in Computer Science

126

145

View full text Add to dashboard Cite

Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated analysis systems. Existing RIQA methods focus on the RGB color-space and are developed based on small datasets with binary quality labels (i.e., 'Accept' and 'Reject'). In this paper, we first re-annotate an Eye-Quality (EyeQ) dataset with 28,792 retinal images from the EyePACS dataset, based on a three-level quality grading system (i.e., 'Good', 'Usable' and 'Reject') for evaluating RIQA methods. Our RIQA dataset is characterized by its large-scale size, multi-level grading, and multi-modality. Then, we analyze the influences on RIQA of different color-spaces, and propose a simple yet efficient deep network, named Multiple Color-space Fusion Network (MCF-Net), which integrates the different color-space representations at both a feature-level and prediction-level to predict image quality grades. Experiments on our EyeQ dataset show that our MCF-Net obtains a state-of-the-art performance, outperforming the other deep learning methods. Furthermore, we also evaluate diabetic retinopathy (DR) detection methods on images of different quality, and demonstrate that the performances of automated diagnostic systems are highly dependent on image quality.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces

Wang

Shen

et al. 2019

Lecture Notes in Computer Science

126

145

View full text Add to dashboard Cite

show abstract

“…Several automated techniques for evaluating fundus image quality have been published. Shao et al 31 developed a fundus image quality classifier by the analysis of illumination, naturalness, and structure using three secondary indices. Their model achieved a sensitivity of 94.69% and a specificity of 92.29% in 80 images.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, those AI diagnostic systems exhibited better performances when dealing with good-quality images than with poor-quality images for both external datasets (ZOC and XOH), indicating that the AI diagnostic systems developed based on good-quality images cannot be readily applied to poor-quality images. However, poor-quality images are inevitable in clinical practice due to various factors, such as a dirty camera lens, head/eye movement, eyelid obstruction, operator error, patient noncompliance and obscured optical media 31 , 36 . Therefore, we propose that the systems developed using good-quality images for detecting retinal diseases in real-world settings (e.g., LDRB, retinal detachment, and retinitis pigmentosa) 18 – 28 need to be integrated with the DLIFS to initially discern and filter out poor-quality images, to ensure their optimum performance.…”

Section: Discussionmentioning

confidence: 99%

“…In real clinical scenarios, many factors can compromise image quality, such as patient noncompliance, operator error, hardware imperfections, and obscured optical media 29 , 30 . Insufficient image quality will result in the loss of diagnostic information and compromise downstream analysis 31 – 33 . To address this, in the real-world clinic, it is necessary to filter out poor-quality images to ensure that the subsequent AI diagnostic analyses can be based on good-quality images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning from “passive feeding” to “selective eating” of real-world data

Guo

Nie

et al. 2020

npj Digit. Med.

View full text Add to dashboard Cite

Artificial intelligence (AI) based on deep learning has shown excellent diagnostic performance in detecting various diseases with good-quality clinical images. Recently, AI diagnostic systems developed from ultra-widefield fundus (UWF) images have become popular standard-of-care tools in screening for ocular fundus diseases. However, in real-world settings, these systems must base their diagnoses on images with uncontrolled quality (“passive feeding”), leading to uncertainty about their performance. Here, using 40,562 UWF images, we develop a deep learning–based image filtering system (DLIFS) for detecting and filtering out poor-quality images in an automated fashion such that only good-quality images are transferred to the subsequent AI diagnostic system (“selective eating”). In three independent datasets from different clinical institutions, the DLIFS performed well with sensitivities of 96.9%, 95.6% and 96.6%, and specificities of 96.6%, 97.9% and 98.8%, respectively. Furthermore, we show that the application of our DLIFS significantly improves the performance of established AI diagnostic systems in real-world settings. Our work demonstrates that “selective eating” of real-world data is necessary and needs to be considered in the development of image-based AI systems.

show abstract

“…Two proprietary data-sets, namely: LOCAL1 and LOCAL2, and two public data-sets (DRIMDB and DRIVE [53,55]) have been used for the 536 fundus images for the experiment purpose. F. Shao, Y. Yang, Q. Jiang, G. Jiang, and Y. S. Ho [101] presented a retinal IQA method based on the idea similar to [100]. All the steps involved in [100] and the proposed method are the same except that the features are used as quality parameters.…”

Section: Feature Extraction Based On Generic Image Statisticsmentioning

confidence: 99%

Fundus image quality assessment: survey, challenges, and future scope

Raj

Tiwari

Martini

2019

IET Image Processing

View full text Add to dashboard Cite

Various ocular diseases, such as cataract, diabetic retinopathy, and glaucoma have affected a large proportion of the population worldwide. In ophthalmology, fundus photography is used for the diagnosis of such retinal disorders. Nowadays, the setup of fundus image acquisition has changed from a fixed position to portable devices, making acquisition more vulnerable to distortions. However, a trustworthy diagnosis solely relies upon the quality of the fundus image. In recent years, fundus image quality assessment (IQA) has drawn much attention from researchers. This paper presents a detailed survey of the fundus IQA research. The survey covers a comprehensive discussion on the factors affecting the fundus image quality and the real-time distortions. The fundus IQA algorithms have been analyzed on the basis of the methodologies used and divided into three classes, namely: (i) Similarity-based, (ii) Segmentation-based, and (iii) Machine learning based. In addition, limitations of state of the art in this research field are also presented with the possible solutions. The objective of this paper is to provide a detailed information about the fundus IQA research with its significance, present status, limitations, and future scope. To the best of our knowledge, this is the first survey paper on the fundus IQA research.

show abstract

Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure

Cited by 34 publications

References 30 publications

Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces

Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces

Deep learning from “passive feeding” to “selective eating” of real-world data

Fundus image quality assessment: survey, challenges, and future scope

Contact Info

Product

Resources

About