Extensive simulations on different publicly available databases highlight an improved performance over the existing methods with an average accuracy of and robustness in detecting the various types of DR lesions irrespective of their intrinsic properties.

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The management of retinal diseases relies heavily on digital imaging data, including optical coherence tomography (OCT) and fluorescein angiography (FA). Targeted feature extraction and the objective quantification of features provide important opportunities in biomarker discovery, disease burden assessment, and predicting treatment response. Additional important advantages include increased objectivity in interpretation, longitudinal tracking, and ability to incorporate computational models to create automated diagnostic and clinical decision support systems. Advances in computational technology, including deep learning and radiomics, open new doors for developing an imaging phenotype that may provide in-depth personalized disease characterization and enhance opportunities in precision medicine. In this review, we summarize current quantitative and radiomic imaging biomarkers described in the literature for age-related macular degeneration and diabetic eye disease using imaging modalities such as OCT, FA, and OCT angiography (OCTA). Various approaches used to identify and extract these biomarkers that utilize artificial intelligence and deep learning are also summarized in this review. These quantifiable biomarkers and automated approaches have unleashed new frontiers of personalized medicine where treatments are tailored, based on patient-specific longitudinally trackable biomarkers, and response monitoring can be achieved with a high degree of accuracy.

This paper addresses the automatic blood vessel detection problem in retinal images using matched filtering in an integrated system design platform that involves curvelet transform and fuzzy c-means. Although noise is kept constant in medical CCD cameras, due to a number of factors, the contrast between the background and the blood vessels in retinal images and consequently the visual quality of the images looks very poor. Some form of pre-processing operation is therefore essential for the accurate extraction of these blood vessels. Since curvelet transform can represent lines, edges and curvatures very well as compared to other multi-resolution techniques, this paper uses curvelet transform to enhance the retinal vasculature. Matched filtering is then used to intensify the blood vessels which is further employed by fuzzy c-means algorithm to extract the vessel silhouette from the background. Performance is evaluated on publicly available DRIVE database and is compared with the existing blood vessel extraction methodology that uses curvelet transform. Simulation results demonstrate that the proposed method is very much efficient in detecting long and thick as well as short and thin vessels, wherein the existing methods fail to extract tiny and thin vessels.

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