Supervised machine learning based multi-task artificial intelligence classification of retinopathies

Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists may particularly benefit from this technology. Quantitative optical coherence tomography angiography (OCTA) imaging provides excellent capability to identify subtle vascular distortions, which are useful for classifying retinovascular diseases. However, application of AI for differentiation and classification of multiple eye diseases is not yet established. In this study, we demonstrate supervised machine learning based multi-task OCTA classification. We sought 1) to differentiate normal from diseased ocular conditions, 2) to differentiate different ocular disease conditions from each other, and 3) to stage the severity of each ocular condition. Quantitative OCTA features, including blood vessel tortuosity (BVT), blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour irregularity (FAZ-CI) were fully automatically extracted from the OCTA images. A stepwise backward elimination approach was employed to identify sensitive OCTA features and optimal-feature-combinations for the multi-task classification. For proof-of-concept demonstration, diabetic retinopathy (DR) and sickle cell retinopathy (SCR) were used to validate the supervised machine leaning classifier. The presented AI classification methodology is applicable and can be readily extended to other ocular diseases, holding promise to enable a mass-screening platform for clinical deployment and telemedicine.Comment: Supplemental material attached at the en

Simple fractal method of assessment of histological images for application in medical diagnostics

We propose new method of assessment of histological images for medical diagnostics. 2-D image is preprocessed to form 1-D landscapes or 1-D signature of the image contour and then their complexity is analyzed using Higuchi's fractal dimension method. The method may have broad medical application, from choosing implant materials to differentiation between benign masses and malignant breast tumors

Land use/cover maps by RS and ancillary data integration in a GIS environment

The main purpose of the research presented in this paper is the development and validation, through the application to a case study, of an efficient form of satellite image classification that integrates ancillary information (Census data; the Municipal Master Plan; the Road Network) and remote sensing data in a Geographic Information System. The developed procedure follows a layered classification approach, being composed by three main stages: (1) Pre-classification stratification; (2) Application of Bayesian and Maximum-likelihood classifiers; (3) Post-classification sorting. Common approaches incorporate the ancillary data before, during or after classification. In the proposed method, all the steps take the auxiliary information into account. The proposed method achieves, globally, much better classification results than the classical, one layer, Minimum Distance and Maximum-likelihood classifiers. Also, it greatly improves the accuracy of those classes where the classification process uses the ancillary data.info:eu-repo/semantics/publishedVersio

Fractal-based models for internet traffic and their application to secure data transmission

This thesis studies the application of fractal geometry to the application of covert communications systems. This involves the process of hiding information in background noise; the information being encrypted or otherwise. Models and methods are considered with regard to two communications systems: (i) wireless communications; (ii) internet communications. In practice, of course, communication through the Internet cannot be disassociated from wireless communications as Internet traffic is 'piped' through a network that can include wireless communications (e.g. satellite telecommunications). However, in terms of developing models and methods for covert communications in general, points (i) and (ii) above require different approaches and access to different technologies. With regard to (i) above, we develop two methods based on fractal modulation and multi-fractal modulation. With regard to (ii), we implement a practical method and associated software for covert transmission of file attachments based on an analysis of Internet traffic noise. In both cases, however, two fractal models are considered; the first is the standard Random Scaling Fractal model and the second is a generalisation of this model that incorporates a greater range of spectral properties than the first—a Generalised Random Scaling Fractal Model. [Continues.

Generalised Local Fractional Hermite-Hadamard Type Inequalities on Fractal Sets

Fractal geometry and analysis constitute a growing field, with numerous applications, based on the principles of fractional calculus. Fractals sets are highly effective in improving convex inequalities and their generalisations. In this paper, we establish a generalized notion of convexity. By defining generalised $\phi_{h-s}$ convex functions, we extend the well known concepts of generalised convex functions, $P$-functions, Breckner $s$-convex functions, $h$-convex functions amongst others. With this definition, we prove Hermite-Hadamard type inequalities for generalized $\phi_{h-s}$ convex mappings onto fractal sets. Our results are then applied to probability theory

Process–Structure–Properties in Polymer Additive Manufacturing II

Additive manufacturing (AM) methods have grown and evolved rapidly in recent years. AM for polymers is particularly exciting and has great potential in transformative and translational research in many fields, such as biomedicine, aerospace, and even electronics. The current methods for polymer AM include material extrusion, material jetting, vat polymerization, and powder bed fusion. In this Special Issue, state-of-the-art reviews and current research results, which focus on the process–structure–properties relationships in polymer additive manufacturing, are reported. These include, but are not limited to, assessing the effect of process parameters, post-processing, and characterization techniques

Edge detection and ridge detection with automatic scale selection

When extracting features from image data, the type of information that can be extracted may be strongly dependent on the scales at which the feature detectors are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection of scale levels when detecting one-dimensional features, such as edges and ridges. Anovel concept of a scale-space edge is introduced, defined as a connected set of points in scale-space at which: (i) the gradient magnitude assumes a local maximum in the gradient direction, and (ii) a normalized measure of the strength of the edge response is locally maximal over scales. An important property of this definition is that it allows the scale levels to vary along the edge. Two specific measures of edge strength are analysed in detail. It is shown that by expressing these in terms of γ-normalized derivatives, an immediate consequence of this definition is that fine scales are selected for sharp edges (so as to reduce the shape distortions due to scale-space smoothing), whereas coarse scales are selected for diffuse edges, such that an edge model constitutes a valid abstraction of the intensity profile across the edge. With slight modifications, this idea can be used for formulating a ridge detector with automatic scale selection, having the characteristic property that the selected scales on a scale-space ridge instead reflect the width of the ridge