Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available

Cloud distribution evaluated by the WRF model during the EUSO-SPB1 flight

EUSO-SPB1 was a balloon-borne mission of the JEM-EUSO (Joint Experiment Missions for Extreme Universe Space Observatory) Program aiming at the ultra-high energy cosmic ray (UHECR) observations from space. We operated the EUSO-SPB1 telescope consisting of 1 m2 Fresnel refractive optics and multi-anode photomultiplier tubes. With a total of 2304 channels, each performed the photon counting every 2.5 µs, allowing for spatiotemporal imaging of the air shower events in an ~ 11°× 11° field of view. EUSO-SPB1 was the first balloon-borne fluorescence detector with a potential to detect air shower events initiated by the EeV energy cosmic rays. On 24 April 2017 UTC, EUSO-SPB1 was launched on the NASA’s Super Pressure Balloon that flew at ~16 – 33 km flight height for ~12 days. Before the flight was terminated, ~27 hours of data acquired in the air shower detection mode were transmitted to the ground. In the present work, we aim at evaluating the role of the clouds during the operation of EUSO-SPB1. We employ the WRF (Weather Research and Forecasting) model to numerically simulate the cloud distribution below EUSO-SPB1. We discuss the key results of the WRF model and the impact of the clouds on the air shower measurement and the efficiency of the cosmic ray observation. The present work is a part of the collaborative effort to estimate the exposure for air shower detections