Combination of Global Features for the Automatic Quality Assessment of Retinal Images

Molecular-Genetic Biomarkers of Diabetic Macular Edema

Background: Diabetic macular edema (DME) is a leading cause of vision impairment and blindness among diabetic patients, requiring effective diagnostic and monitoring strategies. This systematic review aims to synthesize current knowledge on molecular biomarkers associated with DME, focusing on their potential to improve diagnostic accuracy and disease management. Methods: A comprehensive search was conducted in PubMed, Embase, Medline, and the Cochrane Central Register of Controlled Trials, covering literature from 2004 to 2023. Out of 1074 articles initially identified, 48 relevant articles were included in this systematic review. Results: We found that molecules involved in several cellular processes, such as neuroinflammation, oxidative stress, vascular dysfunction, apoptosis, and cell-to-cell communication, exhibit differential expression profiles in various biological fluids when comparing diabetic individuals with or without macular edema. Conclusions: The study of these molecules could lead to the proper identification of specific biomarkers that may improve the diagnosis, prognosis, and therapeutic management of DME patients.

Automated image quality appraisal through partial least squares discriminant analysis

PURPOSE

Automatic retinal fundus image quality analysis is one of the most essential preliminary stages in automatic computer-aided retinal disease diagnosis system, which allows good-quality fundus images for accurate disease prediction through localization and segmentation of retinal regions. This paper presents new feature extraction methods using full-reference and no-reference image quality metrics for image quality classification.

METHODS

Basic image features, reference and no-reference features are extracted from the fundus image and applied through different classification techniques to determine the image quality for further diagnosis. In this paper, human-made categorization including good and non-good-quality fundus image classification is constructed by considering major features of retinal fundus images are illumination, clarity, image intensity, contrast and region visibility. The proposed system presented fundus image quality classification by automatic extraction of features from fundus images through image processing techniques and automatic classification of image quality through different classification algorithm.

RESULTS

This system was thoroughly investigated on 2674 retinal fundus images from publically available datasets, namely MESSIDOR, Drishti-GS1, DRIVE, HRF, DRIONS-DB, DIARETDB0, DIARETDB1, IDRiD, INSPIRE-AVR, CHASE-DB1, ONHSD, DRIMDB and e-ophtha-EX with better performance results in terms of sensitivity, accuracy, precision and F1 score of 99.36%, 96.79%, 96.29% and 97.79%, respectively.

CONCLUSION

The proposed system results were compared to the existing state-of-the-art approaches and outperform existing methods for image quality assessment representing the efficiency and robustness of our system is most suitable for automatic image analysis during retinal disease diagnosis.

Leon A, Quiroz-Mercado H. A Teleophthalmology Support System Based on the Visibility of Retinal Elements Using the CNNs

This paper proposes a teleophthalmology support system in which we use algorithms of object detection and semantic segmentation, such as faster region-based CNN (FR-CNN) and SegNet, based on several CNN architectures such as: Vgg16, MobileNet, AlexNet, etc. These are used to segment and analyze the principal anatomical elements, such as optic disc (OD), region of interest (ROI) composed by the macular region, real retinal region, and vessels. Unlike the conventional retinal image quality assessment system, the proposed system provides some possible reasons about the low-quality image to support the operator of an ophthalmoscope and patient to acquire and transmit a better-quality image to central eye hospital for its diagnosis. The proposed system consists of four steps: OD detection, OD quality analysis, obstruction detection of the region of interest (ROI), and vessel segmentation. For the OD detection, artefacts and vessel segmentation, the FR-CNN and SegNet are used, while for the OD quality analysis, we use transfer learning. The proposed system provides accuracies of 0.93 for the OD detection, 0.86 for OD image quality, 1.0 for artefact detection, and 0.98 for vessel segmentation. As the global performance metric, the kappa-based agreement score between ophthalmologist and the proposed system is calculated, which is higher than the score between ophthalmologist and general practitioner.

Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis

An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in this paper, a novel blind image quality assessment (BIQA) method for the evaluation of MR images is introduced. It is observed that the result of filtering using non-maximum suppression (NMS) strongly depends on the perceptual quality of an input image. Hence, in the method, the image is first processed by the NMS with various levels of acceptable local intensity difference. Then, the quality is efficiently expressed by the entropy of a sequence of extrema numbers obtained with the thresholded NMS. The proposed BIQA approach is compared with ten state-of-the-art techniques on a dataset containing MR images and subjective scores provided by 31 experienced radiologists. The Pearson, Spearman, Kendall correlation coefficients and root mean square error for the method assessing images in the dataset were 0.6741, 0.3540, 0.2428, and 0.5375, respectively. The extensive experimental evaluation of the BIQA methods reveals that the introduced measure outperforms related techniques by a large margin as it correlates better with human scores.