Curvature Based Biomarkers for Diabetic Retinopathy via Exponential Curve Fits in SE(2)

We propose a robust and fully automatic method for the analysis of vessel tortuosity. Our method does not rely on pre-segmentation of vessels, but instead acts directly on retinal image data. The method is based on theory of best-fit exponential curves in the roto-translation group SE(2). We lift 2D images to 3D functions called orientation scores by including an orientation dimension in the domain. In the extended domain of positions and orientations (identified with SE(2)) we study exponential curves, whose spatial projections have constant curvature. By locally fitting such curves to data in orientation scores, via our new iterative stabilizing refinement method, we are able to assign to each location a curvature and confidence value. These values are then used to define global tortuosity measures. The method is validated on synthetic and retinal images. We show that the tortuosity measures can serve as effective biomarkers for diabetes and different stages of diabetic retinopathy

Retinal Artery/Vein Classification via Graph Cut Optimization

In many diseases with a cardiovascular component, the geometry of microvascular blood vessels changes. These changes are specific to arteries and veins, and can be studied in the microvasculature of the retina using retinal photography. To facilitate large-scale studies of artery/vein-specific changes in the retinal vasculature, automated classification of the vessels is required. Here we present a novel method for artery/vein classification based on local and contextual feature analysis of retinal vessels. For each vessel, local information in the form of a transverse intensity profile is extracted. Crossings and bifurcations of vessels provide contextual information. The local and contextual features are integrated into a non-submodular energy function, which is optimized exactly using graph cuts. The method was validated on a ground truth data set of 150 retinal fundus images, achieving an accuracy of 88.0% for all vessels and 94.0% for the six arteries and six veins with highest caliber in the image

Infrastructure for Retinal Image Analysis

This paper introduces a retinal image analysis infrastructure for the automatic assessment of biomarkers related to early signs of diabetes, hypertension and other systemic diseases. The developed application provides several tools, namely normalization, vessel enhancement and segmentation, optic disc and fovea detection, junction detection, bifurcation/crossing discrimination, artery/vein classification and red lesion detection. The pipeline of these methods allows the assessment of important biomarkers characterizing dynamic properties of retinal vessels, such as tortuosity, width, fractal dimension and bifurcation geometry features

Understanding Business Process Quality

Abstract Organizations have taken benefit from quality management prac-tices in manufacturing and logistics with respect to competitiveness as well as profitability. At the same time, an ever-growing share of the organizational value chain centers around transactional administrative processes addressed by business process management concepts, e.g. in finance and accounting. Integrating these fields is thus very promising from a management perspec-tive. Obtaining a clear understanding of business process quality constitutes the most important prerequisite in this respect. However, related approaches have not yet provided an effective solution to this issue. In this chapter, we consider effectiveness requirements towards business process quality concepts from a management perspective, compare existing approaches from various fields, deduct a definition framework from organizational targets, and take initial steps towards practical adoption. These steps provide fundamental in-sights into business process quality, and contribute to obtain a clear grasp of what constitutes a good business process.

What is the Oxygen Isotope Composition of Venus? The Scientific Case for Sample Return from Earth’s “Sister” Planet

Venus is Earth’s closest planetary neighbour and both bodies are of similar size and mass. As a consequence, Venus is often described as Earth’s sister planet. But the two worlds have followed very different evolutionary paths, with Earth having benign surface conditions, whereas Venus has a surface temperature of 464 °C and a surface pressure of 92 bar. These inhospitable surface conditions may partially explain why there has been such a dearth of space missions to Venus in recent years.The oxygen isotope composition of Venus is currently unknown. However, this single measurement (Δ17O) would have first order implications for our understanding of how large terrestrial planets are built. Recent isotopic studies indicate that the Solar System is bimodal in composition, divided into a carbonaceous chondrite (CC) group and a non-carbonaceous (NC) group. The CC group probably originated in the outer Solar System and the NC group in the inner Solar System. Venus comprises 41% by mass of the inner Solar System compared to 50% for Earth and only 5% for Mars. Models for building large terrestrial planets, such as Earth and Venus, would be significantly improved by a determination of the Δ17O composition of a returned sample from Venus. This measurement would help constrain the extent of early inner Solar System isotopic homogenisation and help to identify whether the feeding zones of the terrestrial planets were narrow or wide.Determining the Δ17O composition of Venus would also have significant implications for our understanding of how the Moon formed. Recent lunar formation models invoke a high energy impact between the proto-Earth and an inner Solar System-derived impactor body, Theia. The close isotopic similarity between the Earth and Moon is explained by these models as being a consequence of high-temperature, post-impact mixing. However, if Earth and Venus proved to be isotopic clones with respect to Δ17O, this would favour the classic, lower energy, giant impact scenario.We review the surface geology of Venus with the aim of identifying potential terrains that could be targeted by a robotic sample return mission. While the potentially ancient tessera terrains would be of great scientific interest, the need to minimise the influence of venusian weathering favours the sampling of young basaltic plains. In terms of a nominal sample mass, 10 g would be sufficient to undertake a full range of geochemical, isotopic and dating studies. However, it is important that additional material is collected as a legacy sample. As a consequence, a returned sample mass of at least 100 g should be recovered.Two scenarios for robotic sample return missions from Venus are presented, based on previous mission proposals. The most cost effective approach involves a “Grab and Go” strategy, either using a lander and separate orbiter, or possibly just a stand-alone lander. Sample return could also be achieved as part of a more ambitious, extended mission to study the venusian atmosphere. In both scenarios it is critical to obtain a surface atmospheric sample to define the extent of atmosphere-lithosphere oxygen isotopic disequilibrium. Surface sampling would be carried out by multiple techniques (drill, scoop, “vacuum-cleaner” device) to ensure success. Surface operations would take no longer than one hour.Analysis of returned samples would provide a firm basis for assessing similarities and differences between the evolution of Venus, Earth, Mars and smaller bodies such as Vesta. The Solar System provides an important case study in how two almost identical bodies, Earth and Venus, could have had such a divergent evolution. Finally, Venus, with its runaway greenhouse atmosphere, may provide data relevant to the understanding of similar less extreme processes on Earth. Venus is Earth’s planetary twin and deserves to be better studied and understood. In a wider context, analysis of returned samples from Venus would provide data relevant to the study of exoplanetary systems

Standardizing ordinal subadult age indicators : testing for observer agreement and consistency across modalities

Skeletal and dental data for subadult analyses obtained from dry bones or various types of medical images, such as computed tomography (CT) scans or conventional radiographs/x-rays, should be consistent and repeatable to ensure method applicability across modalities and support combining study samples. The present study evaluates observer agreement of epiphyseal fusion and dental development stages obtained on CT scans of a U.S. sample and the consistency of epiphyseal fusion stages between CT scans and projected scan radiographs/scout images (U.S. CT sample), and between dry bones and conventional x-rays (Colombian osteological sample). Results show that both intra- and interobserver agreements of scores on CT scans were high (intra: mean Cohen’s kappa = 0.757–0.939, inter: mean Cohen’s kappa = 0.773–0.836). Agreements were lower for dental data (intra: mean Cohen’s kappa = 0.757, inter: mean Cohen’s kappa = 0.773–0.0.820) compared to epiphyseal fusion data (intra: mean Cohen’s kappa = 0.939, inter: mean Cohen’s kappa = 0.807–0.836). Consistency of epiphyseal fusion stages was higher between dry bones and conventional x-rays than between CT scans and scout images (mean Cohen’s kappa = 0.708–0.824 and 0.726–0.738, respectively). Differences rarely surpassed a one-stage value between observers or modalities. The complexity of some ossification patterns and superimposition had a greater negative impact on agreement and consistency rates than observer experience. Results suggest ordinal subadult skeletal data can be collected and combined across modalities.The National Institute of Justice and the National Science Foundation.http://www.elsevier.com/locate/forsciint2022-01-10hj2021Anatom