Performance Evaluation of Color Retinal Image Quality Assessment in Asymmetric Channel VQ Coding

2013 ◽  
Vol 4 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Agung W. Setiawan ◽  
Andriyan B. Suksmono ◽  
Tati R. Mengko ◽  
Oerip S. Santoso
Keyword(s):  
Image Quality ◽  
Blood Vessel ◽  
Quality Assessment ◽  
Image Coding ◽  
Channel Coding ◽  
Retinal Image ◽  
Image Quality Assessment ◽  
Objective Assessment ◽  
The Other ◽  
Retinal Blood Vessel

The RGB color retinal image has an interesting characteristic, i.e. the G channel contains more important information than the other ones. One of the most important features in a retinal image is the retinal blood vessel structure. Many diseases can be diagnosed based on in the retinal blood vessel, such as micro aneurysms that can lead to blindness. In the G channel, the contrast between retinal blood vessel and its background is significantly high. The authors explore this retinal image characteristic to construct a more suitable image coding system. The coding processes are conduct in three schemes: weighted R channel, weighted G channel, and weighted B channel coding. Their hypothesis is that allocating more bits in the G channel will improve the coding performance. The authors seek for image quality assessment (IQA) metrics that can be used to measure the distortion in retinal image coding. Three different metrics, namely Peak Signal to Noise Ratio (PSNR), Structure Similarity (SSIM), and Visual Information Fidelity (VIF) are compared as objective assessment in image coding and to show quantitatively that G channel has more important role compared to the other ones. The authors use Vector Quantization (VQ) as image coding method due to its simplicity and low-complexity than the other methods. Experiments with actual retinal image shows that the minimum value of SSIM and VIF required in this coding scheme is 0.9940 and 0.8637.

A CNN-BASED RETINAL IMAGE QUALITY ASSESSMENT SYSTEM FOR TELEOPHTHALMOLOGY

2019 ◽  
Vol 19 (05) ◽  
pp. 1950030 ◽  
Author(s):  
XUEWEI WANG ◽  
SHULIN ZHANG ◽  
XIAO LIANG ◽  
CHUN ZHENG ◽  
JINJIN ZHENG ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Retinal Image ◽  
Network Architecture ◽  
Image Quality Assessment ◽  
Binary Classification ◽  
Developed Countries ◽  
Assessment System ◽  
Retinal Image Quality Assessment ◽  
Multi Classification

Oculopathy is a widespread disease among people of all ages around the world. Teleophthalmology can facilitate the ophthalmological diagnosis for less developed countries that lack medical resources. In teleophthalmology, the assessment of retinal image quality is of great importance. In this paper, we propose a no-reference retinal image assessment system based on DenseNet, a convolutional neural network architecture. This system classified fundus images into good and bad quality or five categories: adequate, just noticeable blur, inappropriate illumination, incomplete optic disc, and opacity. The proposed system was evaluated on different datasets and compared to the applications based on other two networks: VGG-16 and GoogLenet. For binary classification, the good-and-bad binary classifier achieves an AUC of 1.000, and the degradation-specified classifiers that distinguish one specified degradation versus the rest achieve AUC values of 0.972, 0.990, 0.982, 0.982 for four categories, respectively. The multi-classification based on DenseNet achieves an overall accuracy of 0.927, which is significantly higher than 0.549 and 0.757 obtained using VGG-16 and GoogLeNet, respectively. The experimental results indicate that the proposed approach produces outstanding performance in retinal image quality assessment and is worth applying in ophthalmological telemedicine applications. In addition, the proposed approach is robust to the image noise. This study fills the gap of multi-classification in retinal image quality assessment.

scholarly journals Digital fundus image quality assessment

2022 ◽  
pp. 25-38
Author(s):  
V. V. Starovoitov ◽  
Y. I. Golub ◽  
M. M. Lukashevich
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Retinal Image ◽  
Image Quality Assessment ◽  
Vascular System ◽  
Visible Range ◽  
Reference Image ◽  
Assessment Procedure ◽  
Image Analysis System ◽  
Fundus Image

Diabetic retinopathy (DR) is a disease caused by complications of diabetes. It starts asymptomatically and can end in blindness. To detect it, doctors use special fundus cameras that allow them to register images of the retina in the visible range of the spectrum. On these images one can see features, which determine the presence of DR and its grade. Researchers around the world are developing systems for the automated analysis of fundus images. At present, the level of accuracy of classification of diseases caused by DR by systems based on machine learning is comparable to the level of qualified medical doctors.The article shows variants for representation of the retina in digital images by different cameras. We define the task to develop a universal approach for the image quality assessment of a retinal image obtained by an arbitrary fundus camera. It is solved in the first block of any automated retinal image analysis system. The quality assessment procedure is carried out in several stages. At the first stage, it is necessary to perform binarization of the original image and build a retinal mask. Such a mask is individual for each image, even among the images recorded by one camera. For this, a new universal retinal image binarization algorithm is proposed. By analyzing result of the binarization, it is possible to identify and remove imagesoutliers, which show not the retina, but other objects. Further, the problem of no-reference image quality assessment is solved and images are classified into two classes: satisfactory and unsatisfactory for analysis. Contrast, sharpness and possibility of segmentation of the vascular system on the retinal image are evaluated step by step. It is shown that the problem of no-reference image quality assessment of an arbitrary fundus image can be solved.Experiments were performed on a variety of images from the available retinal image databases.

Fast macula detection and application to retinal image quality assessment

2020 ◽  
Vol 55 ◽  
pp. 101567 ◽  
Author(s):  
Robin Alais ◽  
Petr Dokládal ◽  
Ali Erginay ◽  
Bruno Figliuzzi ◽  
Etienne Decencière
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Retinal Image ◽  
Image Quality Assessment ◽  
Retinal Image Quality Assessment

Retinal image quality assessment using deep learning

2018 ◽  
Vol 103 ◽  
pp. 64-70 ◽  
Author(s):  
Gabriel Tozatto Zago ◽  
Rodrigo Varejão Andreão ◽  
Bernadette Dorizzi ◽  
Evandro Ottoni Teatini Salles
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Retinal Image ◽  
Image Quality Assessment ◽  
Retinal Image Quality Assessment
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