Image Analysis Applied to Slices of History

During the last 10 years, TASC has undertaken several digital image enhancement projects based on nondestructive evaluation (NDE) applications. Most of these projects involved analyzing NDE imagery to determine why a critical part failed to operate as expected, or trying to recover from a failure which degraded NDE imagery or made it difficult to obtain. Examples include our studies of the Inertial Upper Stage nozzle nosecap following the unsuccessful launch of a Tracking Data Relay Satellite in the summer of 1983 [1] and our development of a video data image processing system to enhance, in real time, unevenly lit, poor-contrast signals from within the contaminated Number 2 reactor vessel at Three Mile Island [2]

Search for ZZ and ZW Production in ppbar Collisions at sqrt(s) = 1.96 TeV

We present a search for ZZ and ZW vector boson pair production in ppbar collisions at sqrt(s) = 1.96 TeV using the leptonic decay channels ZZ --> ll nu nu, ZZ --> l l l' l' and ZW --> l l l' nu. In a data sample corresponding to an integrated luminosity of 194 pb-1 collected with the Collider Detector at Fermilab, 3 candidate events are found with an expected background of 1.0 +/- 0.2 events. We set a 95% confidence level upper limit of 15.2 pb on the cross section for ZZ plus ZW production, compared to the standard model prediction of 5.0 +/- 0.4 pb.Comment: 7 pages, 2 figures. This version is accepted for publication by Phys. Rev. D Rapid Communication

Leaf segmentation in plant phenotyping: a collation study

Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (>>90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://​www.​plant-phenotyping.​org/​datasets) to support future challenges beyond segmentation within this application domain

In Vitro Evaluation of a Soluble Leishmania Promastigote Surface Antigen as a Potential Vaccine Candidate against Human Leishmaniasis

International audiencePSA (Promastigote Surface Antigen) belongs to a family of membrane-bound and secreted proteins present in severalLeishmania (L.) species. PSA is recognized by human Th1 cells and provides a high degree of protection in vaccinated mice.We evaluated humoral and cellular immune responses induced by a L. amazonensis PSA protein (LaPSA-38S) produced in aL. tarentolae expression system. This was done in individuals cured of cutaneous leishmaniasis due to L. major (CCLm) or L.braziliensis (CCLb) or visceral leishmaniasis due to L. donovani (CVLd) and in healthy individuals. Healthy individuals weresubdivided into immune (HHR-Lm and HHR-Li: Healthy High Responders living in an endemic area for L. major or L. infantuminfection) or non immune/naive individuals (HLR: Healthy Low Responders), depending on whether they produce high orlow levels of IFN-c in response to Leishmania soluble antigen. Low levels of total IgG antibodies to LaPSA-38S were detectedin sera from the studied groups. Interestingly, LaPSA-38S induced specific and significant levels of IFN-c, granzyme B and IL-10 in CCLm, HHR-Lm and HHR-Li groups, with HHR-Li group producing TNF-a in more. No significant cytokine response wasobserved in individuals immune to L. braziliensis or L. donovani infection. Phenotypic analysis showed a significant increasein CD4+ T cells producing IFN-c after LaPSA-38S stimulation, in CCLm. A high positive correlation was observed between thepercentage of IFN-c-producing CD4+ T cells and the released IFN-c. We showed that the LaPSA-38S protein was able toinduce a mixed Th1 and Th2/Treg cytokine response in individuals with immunity to L. major or L. infantum infectionindicating that it may be exploited as a vaccine candidate. We also showed, to our knowledge for the first time, the capacityof Leishmania PSA protein to induce granzyme B production in humans with immunity to L. major and L. infantum infectio

Space-Variant Gabor Decomposition for Filtering 3D Medical Images

This is an experimental paper in which we introduce the possibility to analyze and to synthesize 3D medical images by using multivariate Gabor frames with Gaussian windows. Our purpose is to apply a space-variant filter-like operation in the space-frequency domain to correct medical images corrupted by different types of acquisitions errors. The Gabor frames are constructed with Gaussian windows sampled on non-separable lattices for a better packing of the space-frequency plane. An implementable solution for 3D-Gabor frames with non-separable lattice is given and numerical tests on simulated data are presented.Austrian Science Fund (FWF) P2751

Monoclonal antibodies and Fc-fusion protein biologic medicines: A multinational cross-sectional investigation of accessibility and affordability in Asia Pacific regions between 2010 and 2020

Background: Monoclonal antibody (mAb) and Fc-fusion protein (FcP) are highly effective therapeutic biologics. We aimed to analyse consumption and expenditure trends in 14 Asia-Pacific countries/regions (APAC) and three benchmark countries (the UK, Canada, and the US). Methods: We analysed 440 mAb and FcP biological products using the IQVIA-MIDAS global sales database. For each year between 2010 and 2020 inclusive, we used standard units (SU) sold per 1000 population and manufacture level price (standardised in 2019 US dollars) to evaluate consumption (accessibility) and expenditure (affordability). Changes of consumption and expenditure were estimated using compound annual growth rate (CAGR). Correlations between consumption, country's economic and health performance indicators were measured using Spearman correlation coefficient. Findings: Between 2010 and 2020, CAGRs of consumption in each region ranged from 7% to 34% and the CAGRs of expenditure ranged from 9% to 31%. The median consumption of biologics was extremely low in lower-middle-income economies (0·29 SU/1000 population) compared with upper-middle-income economies (1·20), high-income economies (40·94) and benchmark countries (109·55), although the median CAGRs of biologics consumption in lower-middle-income economies (31%) was greater than upper-middle-income (14%), high-income economies (13%) and benchmark countries (9%). Consumption was correlated with GDP per capita [Spearman's rank correlation coefficient (r) = 0·75, p < 0·001], health expenditure as a percentage of total (r = 0·83, p < 0·001) and medical doctors’ density (r = 0·85, p < 0·001). Interpretation: There have been significant increases in mAb and FcP biologics consumption and expenditure, however accessibility of biological medicines remains unequal and is largely correlated with country's income level. Funding: This research was funded by NHMRC Project Grant GNT1157506 and GNT1196900; Enhanced Start-up Fund for new academic staff and Internal Research Fund, Department of Medicine, LKS Faculty of Medicine, University of Hong Kong

Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings.

The demand for digitisation of complex engineering drawings becomes increasingly important for the industry given the pressure to improve the efficiency and time effectiveness of operational processes. There have been numerous attempts to solve this problem, either by proposing a general form of document interpretation or by establishing an application dependant framework. Moreover, text/graphics segmentation has been presented as a particular form of addressing document digitisation problem, with the main aim of splitting text and graphics into different layers. Given the challenging characteristics of complex engineering drawings, this paper presents a novel sequential heuristics-based methodology which is aimed at localising and detecting the most representative symbols of the drawing. This implementation enables the subsequent application of a text/graphics segmentation method in a more effective form. The experimental framework is composed of two parts: first we show the performance of the symbol detection system and then we present an evaluation of three different state of the art text/graphic segmentation techniques to find text on the remaining image