Human Visual System-Based Fundus Image Quality Assessment of Portable Fundus Camera Photographs

Abstract

Telemedicine and the medical "big data" era in ophthalmology highlight the use of non-mydriatic ocular fundus photography, which has given rise to indispensable applications of portable fundus cameras. However, in the case of portable fundus photography, non-mydriatic image quality is more vulnerable to distortions, such as uneven illumination, color distortion, blur, and low contrast. Such distortions are called generic quality distortions. This paper proposes an algorithm capable of selecting images of fair generic quality that would be especially useful to assist inexperienced individuals in collecting meaningful and interpretable data with consistency. The algorithm is based on three characteristics of the human visual system--multi-channel sensation, just noticeable blur, and the contrast sensitivity function to detect illumination and color distortion, blur, and low contrast distortion, respectively. A total of 536 retinal images, 280 from proprietary databases and 256 from public databases, were graded independently by one senior and two junior ophthalmologists, such that three partial measures of quality and generic overall quality were classified into two categories. Binary classification was implemented by the support vector machine and the decision tree, and receiver operating characteristic (ROC) curves were obtained and plotted to analyze the performance of the proposed algorithm. The experimental results revealed that the generic overall quality classification achieved a sensitivity of 87.45% at a specificity of 91.66%, with an area under the ROC curve of 0.9452, indicating the value of applying the algorithm, which is based on the human vision system, to assess the image quality of non-mydriatic photography, especially for low-cost ophthalmological telemedicine applications.

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

“…We can make the following observations: (1) The performance of the non-deep learning baseline [16] is obviously lower than those of deep learning based methods. This is reasonable because deep learning can extract highly discriminative representations from the retinal images directly, using multiple CNN layers, which are superior to the hand-crafted features in [16] and lead to better performance. (2) For the different color-spaces, the networks in RGB and LAB color-spaces perform better than that in HSV color-space.…”

“…We also report the average result (AVG) when combining the predictions of the three color-spaces directly, without the fusion block. For the non-deep learning baseline, we implement the RIQA method from [16], which is based on three visual characteristics (i.e., multi-channel sensation, just noticeable blur, and the contrast sensitivity function) and an SVM classifier with a radial based function. For evaluation metrics, we employ average accuracy, precision, recall, and F-measure ( 2 * precision * recall precision+recall ).…”

“…Wang et.al. 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].…”

Retinal Image Quality Assessment: Portable Eye Examination Kit Retina (Peek Retina)Tm Versus 3d-Printed Ophthalmoscope (3dpo)

“…portable tool in generating satisfactory fundus images for retinal assessment especially during screening [8,11,14,25]. However, in the current study, it has been shown that the retinal image photographed by the Peek Retina was more preferable in tracing retinal vascular network for further analysis.…”

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

“…In the next work under this category the authors supported the importance of retinal IQA research with the fact that the portable and handy fundus imaging devices are more sensitive towards distortions. Based on the theory of Human Visual System (HVS) framework, in 2016 S. Wang, K. Jin, H. Lu et al [100] presented a machine learning approach for quality prediction of portable fundus images. Initially, the quality scores are collected by the subjective evaluation from three ophthalmologists for a dataset of 536 images.…”