Quantification of Global Tortuosity in Retinal Blood Vessels

Tortuosity is a parameter that indicates the tendency of a blood vessel segment to contain multiple twists and turns. Chronic hemodynamic changes in the body due to diabetes and hypertension will manifest as increased retinal vascular tortuosity, rendering tortuosity as a suitable indicator for diabetic and hypertensive retinopathy. Retinal tortuosity may be evaluated locally on a single segment or globally in the complete vascular network. Global tortuosity quantification consists of automated segmentation and partition of retinal vessel network, local tortuosity measurement, and global tortuosity index derivation from weighted combination of local tortuosity values. This paper proposes several weighting schemes and evaluates their performance when combined with different local tortuosity indexes. We perform rank correlation analysis to find the global tortuosity quantification that is most consistent with the ophthalmologists. Our results show that local tortuosity indexes that are robust to variations in scale and number of sampling points provide the best performance. Furthermore, weighting scheme based on chord length yields better results than the one based on arc length. The combination of Tortuosity Density (TD) local index and Tortuosity Density Global (TDG) weighting scheme provides the highest consistency with ophthalmologists, with the average rank correlation coefficient of 0.98 (p-value < 0.03)

How wry is a wrybill?

The laterally asymmetrical bill of New Zealand’s endemic Wrybill Anarhynchus frontalis is unique among birds and has inspired much debate regarding its evolution and functional significance. Despite this, only one previous study has attempted to quantify the range of individual variation in bill shape, but used a single metric of curvature (bill tip angle). Using standardized digital photographs of 40 live Wrybills, we explored a range of metrics of bill length and curvature to describe the variation in bill shape in greater detail. Like the previous study, we found no sexual dimorphism in bill shape, despite males being slightly longer-billed than females, and recorded similar variation in bill tip angle (16–23°). However, we found that this single metric under-represented overall variation in bill shape, due to significant differences in where curvature began and was most pronounced along the length of the bill. Principal component analysis indicated that at least three independent metrics were required to describe the shape variation among individuals. Subtle differences in bill shape could plausibly affect an individual’s relative success among the range of Wrybill foraging strategies observed in breeding and non-breeding habitats. Elucidating the potential behavioral and fitness consequences of this variation will require detailed foraging and demographic studies with individuals of known bill morphology

High-fidelity finite element models of composite wind turbine blades with shell and solid elements

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Curvature-based Tortuosity Evaluation for Infant Retinal Images

The clinical recognition of abnormal retinal tortuosity is significant in the diagnosis of several ocular and systemic diseases. An automatic evaluation and quantification of tortuosity would help in the early detection of such pathologies. We applied two tortuosity evaluation approach based on continuous curvature to a dataset of 45 infant fundus images. Performance evaluation is done on classification accuracy of three classifiers-Naïve Bayesian classifier and k-nearest neighbor classifier, and K-means clustering algorithm, by comparing the estimated results against ground truth from expert ophthalmologists. Results show that different numerical methods provide different tortuosity values for same retinal vessels however have the potential to detect and evaluate abnormal retinal curves. The best classification accuracy of 87.3% is achieved by the method 2 using K-nearest neighbor classifier. Keywords: Retinal vessels, curvature, tortuosit

STOL aircraft transient ground effects. Part 1: Fundamental analytical study

The first phases of a fundamental analytical study of STOL ground effects were presented. Ground effects were studied in two dimensions to establish the importance of nonlinear effects, to examine transient aspects of ascent and descent near the ground, and to study the modelling of the jet impingement on the ground. Powered lift system effects were treated using the jet-flap analogy. The status of a three-dimensional jet-wing ground effect method was presented. It was shown, for two-dimensional unblown airfoils, that the transient effects are small and are primarily due to airfoil/freestream/ground orientation rather than to unsteady effects. The three-dimensional study showed phenomena similar to the two-dimensional results. For unblown wings, the wing/freestream/ground orientation effects were shown to be of the same order of magnitude as for unblown airfoils. This may be used to study the nonplanar, nonlinear, jet-wing ground effect

NASTRAN level 16 user's manual updates for aeroelastic analysis of bladed discs

The NASTRAN aeroelastic and flutter capability was extended to solve a class of problems associated with axial flow turbomachines. The capabilities of the program are briefly discussed. The aerodynamic data pertaining to the bladed disc sector, the associated aerodynamic modeling, the steady aerothermoelastic 'design/analysis' formulations, and the modal, flutter, and subcritical roots analyses are described. Sample problems and their solutions are included

Direct occlusion handling for high level image processing algorithms

Many high-level computer vision algorithms suffer in the presence of occlusions caused by multiple objects overlapping in a view. Occlusions remove the direct correspondence between visible areas of objects and the objects themselves by introducing ambiguity in the interpretation of the shape of the occluded object. Ignoring this ambiguity allows the perceived geometry of overlapping objects to be deformed or even fractured. Supplementing the raw image data with a vectorized structural representation which predicts object completions could stabilize high-level algorithms which currently disregard occlusions. Studies in the neuroscience community indicate that the feature points located at the intersection of junctions may be used by the human visual system to produce these completions. Geiger, Pao, and Rubin have successfully used these features in a purely rasterized setting to complete objects in a fashion similar to what is demonstrated by human perception. This work proposes using these features in a vectorized approach to solving the mid-level computer vision problem of object stitching. A system has been implemented which is able extract L and T-junctions directly from the edges of an image using scale-space and robust statistical techniques. The system is sensitive enough to be able to isolate the corners on polygons with 24 sides or more, provided sufficient image resolution is available. Areas of promising development have been identified and several directions for further research are proposed

Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.

This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the first part, we propose two different solutions to the coronal loop detection problem. The first solution is a block-based coronal loop mining method that detects coronal loops from solar images by dividing the solar image into fixed sized blocks, labeling the blocks as Loop or Non-Loop , extracting features from the labeled blocks, and finally training classifiers to generate learning models that can classify new image blocks. The block-based approach achieves 64% accuracy in IO-fold cross validation experiments. To improve the accuracy and scalability, we propose a contour-based coronal loop detection method that extracts contours from cluttered regions, then labels the contours as Loop and Non-Loop , and extracts geometric features from the labeled contours. The contour-based approach achieves 85% accuracy in IO-fold cross validation experiments, which is a 20% increase compared to the block-based approach. In the second part, we propose a method to extract semi-elliptical open curves from cluttered regions. Our method consists of the following steps: obtaining individual smooth contours along with their saliency measures; then starting from the most salient contour, searching for possible grouping options for each contour; and continuing the grouping until an optimum solution is reached. Our work involved the design and development of a complete system for coronal loop mining in solar images, which required the formulation of new Gestalt perceptual rules and a systematic methodology to select and combine them in a fully automated judicious manner using machine learning techniques that eliminate the need to manually set various weight and threshold values to define an effective cost function. After finding salient contour groups, we close the gaps within the contours in each group and perform B-spline fitting to obtain smooth curves. Our methods were successfully applied on cluttered solar images from TRACE and STEREO/SECCHI to discern coronal loops. Aerial road images were also used to demonstrate the applicability of our grouping techniques to other contour-types in other real applications