MEASURING LUNG ABNORMALITIES IN IMAGES-BASED CT

Diagnosis by imaging is one of the most important findings in biomedical imaging because it allows not only the diagnosing of a specific pathology but to perform online and offline surgical operations using imaging as it is noticed in interventional radiology. This paper illustrates the use Hough transform in identifying pathological structures included in CT (Computer Tomography) and HRCT (High Resolution Computer Tomography) images related to patients suffering from lung disease. These abnormal areas appear as bulges of the trophic vessels and they are similar to circular structures with level of lighter gray near to white. Circular Hough transform (CHT) identifies regions with a circular shape. However, a metrics is defined in order to understand if the pointed out area has a pathological morphology. CHT is used here for helping to detect possible events of indolent tumors or undetermined significance pathologies for lung apparatus. For this aim, we use entropy approach with CHT because it measures the scatter of the directional elements in an image. In fact a high entropy value is related to areas with a strong contrast in grayscale, and abnormalities in the image are present as a set of points with more lighter than the dark background. The results have shown, by means of an accuracy true table, rendering a comparison between clinicians’diagnosis and CHT detection, it is possible to indicate, with a better accuracy, potential areas of undetermined significance pathologies. Finally, a receiver operational curve (ROC) is used as an accuracy index for evaluating the positive impact of entropy on diagnosis

MEASURING LUNG ABNORMALITIES IN IMAGES-BASED CT

Diagnosis by imaging is one of the most important findings in biomedical imaging because it allows not only the diagnosing of a specific pathology but to perform online and offline surgical operations using imaging as it is noticed in interventional radiology. This paper illustrates the use Hough transform in identifying pathological structures included in CT (Computer Tomography) and HRCT (High Resolution Computer Tomography) images related to patients suffering from lung disease. These abnormal areas appear as bulges of the trophic vessels and they are similar to circular structures with level of lighter gray near to white. Circular Hough transform (CHT) identifies regions with a circular shape. However, a metrics is defined in order to understand if the pointed out area has a pathological morphology. CHT is used here for helping to detect possible events of indolent tumors or undetermined significance pathologies for lung apparatus. For this aim, we use entropy approach with CHT because it measures the scatter of the directional elements in an image. In fact a high entropy value is related to areas with a strong contrast in grayscale, and abnormalities in the image are present as a set of points with more lighter than the dark background. The results have shown, by means of an accuracy true table, rendering a comparison between clinicians’diagnosis and CHT detection, it is possible to indicate, with a better accuracy, potential areas of undetermined significance pathologies. Finally, a receiver operational curve (ROC) is used as an accuracy index for evaluating the positive impact of entropy on diagnosis

The VVV Infrared Variability Catalog (VIVA-I)

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2020 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved. Final published version available at: https://doi.org/10.1093/mnras/staa1352.Thanks to the VISTA Variables in the Via Lactea (VVV) ESO Public Survey it is now possible to explore a large number of objects in those regions. This paper addresses the variability analysis of all VVV point sources having more than 10 observations in VVVDR4 using a novel approach. In total, the near-IR light curves of 288,378,769 sources were analysed using methods developed in the New Insight Into Time Series Analysis project. As a result, we present a complete sample having 44, 998, 752 variable star candidates (VVV-CVSC), which include accurate individual coordinates, near-IR magnitudes (ZYJHKs), extinctions A(Ks), variability indices, periods, amplitudes, among other parameters to assess the science. Unfortunately, a side effect of having a highly complete sample, is also having a high level of contamination by non-variable (contamination ratio of non-variables to variables is slightly over 10:1). To deal with this, we also provide some flags and parameters that can be used by the community to de-crease the number of variable candidates without heavily decreasing the completeness of the sample. In particular, we cross-identified 339,601 of our sources with Simbad and AAVSO databases, which provide us with information for these objects at other wavelegths. This sub-sample constitutes a unique resource to study the corresponding near-IR variability of known sources as well as to assess the IR variability related with X-ray and Gamma-Ray sources. On the other hand, the other 99.5% sources in our sample constitutes a number of potentially new objects with variability information for the heavily crowded and reddened regions of the Galactic Plane and Bulge. The present results also provide an important queryable resource to perform variability analysis and to characterize ongoing and future surveys like TESS and LSST.Peer reviewe