Tram-Line filtering for retinal vessel segmentation

The segmentation of the vascular network from retinal fundal images is a fundamental step in the analysis of the retina, and may be used for a number of purposes, including diagnosis of diabetic retinopathy. However, due to the variability of retinal images segmentation is difficult, particularly with images of diseased retina which include significant distractors. This paper introduces a non-linear filter for vascular segmentation, which is particularly robust against such distractors. We demonstrate results on the publicly-available STARE dataset, superior to Stare’s performance, with 57.2% of the vascular network (by length) successfully located, with 97.2% positive predictive value measured by vessel length, compared with 57% and 92.2% for Stare. The filter is also simple and computationally efficient

Trainable COSFIRE filters for vessel delineation with application to retinal images

Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.peer-reviewe

The application of arterio‐venous ratio (AVR) cut‐off values in clinic to stratify cardiovascular risk in patients

© 2022 The Authors. Ophthalmic and Physiological Optics published by John Wiley & Sons Ltd on behalf of College of Optometrists. This is an open access article under the terms of the Creative Commons Attribution-Non Commercial License, https://creativecommons.org/licenses/by-nc/4.0/Introduction: Cardiovascular risk calculators are a useful tool for identifying at‐risk individuals. There are standardised methods for assessing the retinal microcirculation which alters as a consequence of cardiovascular disease (CVD). This study aimed to explore if a standardised retinal vessel assessment conducted in primary optometric care reflects current cardiovascular risk, as measured using two validated CVD risk calculators (QRISK 2; Mayo Clinic). Methods: A total of 120 subjects were included in the analyses. Following a routine eye examination, participants had disc‐centred retinal photographs and systemic blood pressure taken. Retinal vessel parameters (central retinal artery and vein equivalent and arterio‐venous ratio (AVR)) were calculated using semi‐automated software. Participants were then grouped into AVR quintiles as defined by the Atherosclerosis Risk in Communities Study (ARIC). Cardiovascular risk was calculated with the validated QRISK and Mayo Clinic health calculators. Results: Systolic blood pressure was significantly greater in those with an AVR value falling in the lowest quintile compared to the highest quintile (150.65 mmHg vs. 132.21 mmHg [p = 0.001]). Similarly, CVD risk was significantly higher in those with the lowest AVR compared to the highest (QRISK: 14.28% vs. 9.87% [p = 0.05]; MAYO risk: 36.35% vs. 19.21% [p = 0.01]). Chi squared analyses showed a significant difference in the number of hypertensives in the lowest AVR quintile compared to those in the highest [p = 0.02]. Conclusion: Whilst the ARIC population is not directly comparable to the population used to develop the QRISK calculator, it has been shown that its application could help to identify at risk individuals using retinal vessel analyses.Peer reviewedFinal Published versio

Computational assessment of the retinal vascular tortuosity integrating domain-related information

[Abstract] The retinal vascular tortuosity presents a valuable potential as a clinical biomarker of many relevant vascular and systemic diseases. Commonly, the existent approaches face the tortuosity quantification by means of fully mathematical representations of the vessel segments. However, the specialists, based on their diagnostic experience, commonly analyze additional domain-related information that is not represented in these mathematical metrics of reference. In this work, we propose a novel computational tortuosity metric that outperforms the mathematical metrics of reference also incorporating anatomical properties of the fundus image such as the distinction between arteries and veins, the distance to the optic disc, the distance to the fovea, and the vessel caliber. The evaluation of its prognostic performance shows that the integration of the anatomical factors provides an accurate tortuosity assessment that is more adjusted to the specialists’ perception.Instituto de Salud Carlos II; DTS18/00136Ministerio de Ciencia, Innovación y Universidades; DPI2015-69948-RMinisterio de Ciencia, Innovación y Universidades; RTI2018-095894-B-I00Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04

PREMATURE INFANT BLOOD VESSEL SEGMENTATION OF RETINAL IMAGES BASED ON HYBRID METHOD FOR THE DETERMINATION OF TORTUOSITY

For the retinal blood vessels segmentation, we used a method, which is based on the morphological operations. The output of this process is extracted retinal binary image, where is situated main blood vessels. In this paper is used dataset of images (2800 images) from device RetCam3. Before applying the image processing, it was selected 30 images with diagnosed pre-plus diseases, and it is divided into two groups with low contrast and good contrast images. In the next part of the analysis, it was analyzing and showing blood vessels with tortuosity. Tortuosity is a symptom of ROP (retinopathy of prematurity). The clinical physicians evaluate tortuosity by visual comparison of the retinal images images. For this reason, it was suggested model which can automatically indicate the tortuosity of the retinal blood vessels by setting a threshold of the blood vessels curvature

The application of retinal fundus camera imaging in dementia:A systematic review

INTRODUCTION: The ease of imaging the retinal vasculature, and the evolving evidence suggesting this microvascular bed might reflect the cerebral microvasculature, presents an opportunity to investigate cerebrovascular disease and the contribution of microvascular disease to dementia with fundus camera imaging. METHODS: A systematic review and meta-analysis was carried out to assess the measurement of retinal properties in dementia using fundus imaging. RESULTS: Ten studies assessing retinal properties in dementia were included. Quantitative measurement revealed significant yet inconsistent pathologic changes in vessel caliber, tortuosity, and fractal dimension. Retinopathy was more prevalent in dementia. No association of age-related macular degeneration with dementia was reported. DISCUSSION: Inconsistent findings across studies provide tentative support for the application of fundus camera imaging as a means of identifying changes associated with dementia. The potential of fundus image analysis in differentiating between dementia subtypes should be investigated using larger well-characterized samples. Future work should focus on refining and standardizing methods and measurements