Mammogram Diagnostics via 2-D Complex Wavelet-based Self-similarity Measures

Breast cancer is the second leading cause of death in women in the United States. Mammography is currently the most eective method for detecting breast cancer early; however, radiological inter- pretation of mammogram images is a challenging task. Many medical images demonstrate a certain degree of self-similarity over a range of scales. This scaling can help us to describe and classify mammograms. In this work, we generalize the scale-mixing wavelet spectra to the complex wavelet domain. In this domain, we estimate Hurst parameter and image phase and use them as discriminatory descriptors to clas- sify mammographic images to benign and malignant. The proposed methodology is tested on a set of images from the University of South Florida Digital Database for Screening Mammography (DDSM). Keywords: Scaling; Complex Wavelets; Self-similarity; 2-D Wavelet Scale-Mixing Spectra

Robust wavelet-based assessment of scaling with applications

A number of approaches have dealt with statistical assessment of selfsimilarity, and many of those are based on multiscale concepts. Most rely on certain distributional assumptions which are usually violated by real data traces, often characterized by large temporal or spatial mean level shifts, missing values or extreme observations. A novel, robust approach based on Theil-type weighted regression is proposed for estimating self-similarity in two-dimensional data (images). The method is compared to two traditional estimation techniques that use wavelet decompositions; ordinary least squares (OLS) and Abry-Veitch bias correcting estimator (AV). As an application, the suitability of the self-similarity estimate resulting from the the robust approach is illustrated as a predictive feature in the classification of digitized mammogram images as cancerous or non-cancerous. The diagnostic employed here is based on the properties of image backgrounds, which is typically an unused modality in breast cancer screening. Classification results show nearly 68% accuracy, varying slightly with the choice of wavelet basis, and the range of multiresolution levels used

Bayesian data mining techniques in public health and biomedical applications

The emerging research issues in evidence-based healthcare decision-making and explosion of comparative effectiveness research (CER) are evident proof of the effort to thoroughly incorporate the rich data currently available within the system. The flexibility of Bayesian data mining techniques lends its strength to handle the challenging issues in the biomedical and health care domains. My research focuses primarily on Bayesian data mining techniques for non-traditional data in this domain, which includes, 1. Missing data: Matched-pair studies with fixed marginal totals with application to meta-analysis of dental sealants effectiveness. 2. Data with unusual distribution: Modeling spatial repeated measures with excess zeros and no covariates to estimate U.S. county level natural fluoride concentration. 3. Highly irregular data: Assess overall image regularity in complex wavelet domain to classify mammography image. The goal of my research is to strengthen the link from data to decisions. By using Bayesian data mining techniques including signal and image processing (wavelet analysis), hierarchical Bayesian modeling, clinical trials meta-analyses and spatial statistics, this thesis resolves challenging issues of how to incorporate data to improve the systems of health care and bio fields and ultimately benefit public health.PhDCommittee Chair: Vidakovic, Brani; Committee Member: Huo, Xiaoming; Committee Member: Mei, Yajun; Committee Member: Park, Youngja; Committee Member: Swann, Juli

One-step signal enhancement method of lateral flow immunoassay using water-soluble hybrid nanofibers

2

Mammogram diagnostics via 2-D complex wavelet-based self-similarity measures

Breast cancer is the second leading cause of death in women in the United States. Mammography is currently the most effective method for detecting breast cancer early; however, radiological interpretation of mammogram images remains a challenging task. On the other hand, many medical images demonstrate a certain degree of self-similarity over a range of scales which can guide us in their description and classification. In this work, we generalize the scale-mixing wavelet transform to the complex wavelet domain. In this domain, we estimate Hurst parameter and phase and use them as discriminatory descriptors to classify mammographic images to benign and malignant. The proposed methodology is tested on a set of images from the University of South Florida Digital Database for Screening Mammography (DDSM)

Fabrication of a uniform chromate conversion coating on Zn alloy for improved corrosion resistance in humid environments

Abstract We developed a facile method to produce a uniform chromate conversion (CC) coating on zinc alloy-plated steel substrates (ZS). When an acidic CC solution is applied to ZS (C-ZS), zinc is dissolved and chromium ions are reduced to form a chromate coating. In localized areas where zinc is excessively dissolved, zinc hydroxide particles are formed, which hinders the formation of a uniform chromate film, leaving the areas vulnerable to further corrosion (i.e., the formation of dark spots) when exposed to high humidity conditions. To suppress the excessive dissolution of zinc, the ZS surface was pretreated with thiolated polyethylene oxide to form a hydrophilic self-assembled monolayer. A more uniform protective CC coating was obtained on the pretreated ZS, resulting in superior corrosion resistance under high humidity conditions

The urgency of resuming disrupted dog rabies vaccination campaigns: a modeling and cost-effectiveness analysis

Abstract Dog vaccination is a cost-effective approach to preventing human rabies deaths. In Haiti, the last nation-wide dog vaccination campaign occurred in 2018. We estimated the number of human lives that could be saved by resuming dog vaccination in 2021 compared to 2022 and compared the cost-effectiveness of these two scenarios. We modified a previously published rabies transmission and economic model to estimate trends in dog and human rabies cases in Haiti from 2005 to 2025, with varying assumptions about when dog vaccinations resume. We compared model outputs to surveillance data on human rabies deaths from 2005 to 2020 and animal rabies cases from 2018 to 2020. Model predictions and surveillance data both suggest a 5- to 8-fold increase in animal rabies cases occurred in Haiti’s capital city between Fall 2019 and Fall 2020. Restarting dog vaccination in Haiti in 2021 compared to 2022 could save 285 human lives and prevent 6541 human rabies exposures over a five-year period. It may also decrease program costs due to reduced need for human post-exposure prophylaxis. These results show that interruptions in dog vaccination campaigns before elimination is achieved can lead to significant human rabies epidemics if not promptly resumed

Determining the post-elimination level of vaccination needed to prevent re-establishment of dog rabies.

BackgroundOnce a canine rabies-free status has been achieved, there is little guidance available on vaccination standards to maintain that status. In areas with risk of reintroduction, it may be practical to continue vaccinating portions of susceptible dogs to prevent re-establishment of canine rabies.MethodsWe used a modified version of RabiesEcon, a deterministic mathematical model, to evaluate the potential impacts and cost-effectiveness of preventing the reintroduction of canine rabies through proactive dog vaccination. We analyzed four scenarios to simulate varying risk levels involving the reintroduction of canine rabies into an area where it is no longer present. In a sensitivity analysis, we examined the influences of reintroduction frequency and intensity, the density of susceptible dog population, dog birth rate, dog life expectancy, vaccine efficacy, rate of loss of vaccine immunity, and the basic reproduction number (R0).ResultsTo prevent the re-establishment of canine rabies, it is necessary to vaccinate 38% to 56% of free-roaming dogs that have no immunity to rabies. These coverage levels were most sensitive to adjustments in R0 followed by the vaccine efficacy and the rate of loss of vaccine immunity. Among the various preventive vaccination strategies, it was most cost-effective to continue dog vaccination at the minimum coverage required, with the average cost per human death averted ranging from $257 to$398 USD.ConclusionsWithout strong surveillance systems, rabies-free countries are vulnerable to becoming endemic when incursions happen. To prevent this, it may be necessary to vaccinate at least 38% to 56% of the susceptible dog population depending on the risk of reintroduction and transmission dynamics

Estimated Cases Averted by COVID-19 Digital Exposure Notification, Pennsylvania, USA, November 8, 2020–January 2, 2021

We combined field-based data with mathematical modeling to estimate the effectiveness of smartphone-enabled COVID-19 exposure notification in Pennsylvania, USA. We estimated that digital notifications potentially averted 7–69 cases/1,000 notifications during November 8, 2020–January 2, 2021. Greater use and increased compliance could increase the effectiveness of digital notifications

Estimated public health impact of concurrent mask mandate and vaccinate-or-test requirement in Illinois, October to December 2021

Abstract Background Facing a surge of COVID-19 cases in late August 2021, the U.S. state of Illinois re-enacted its COVID-19 mask mandate for the general public and issued a requirement for workers in certain professions to be vaccinated against COVID-19 or undergo weekly testing. The mask mandate required any individual, regardless of their vaccination status, to wear a well-fitting mask in an indoor setting. Methods We used Illinois Department of Public Health’s COVID-19 confirmed case and vaccination data and investigated scenarios where masking and vaccination would have been reduced to mimic what would have happened had the mask mandate or vaccine requirement not been put in place. The study examined a range of potential reductions in masking and vaccination mimicking potential scenarios had the mask mandate or vaccine requirement not been enacted. We estimated COVID-19 cases and hospitalizations averted by changes in masking and vaccination during the period covering October 20 to December 20, 2021. Results We find that the announcement and implementation of a mask mandate are likely to correlate with a strong protective effect at reducing COVID-19 burden and the announcement of a vaccinate-or-test requirement among frontline professionals is likely to correlate with a more modest protective effect at reducing COVID-19 burden. In our most conservative scenario, we estimated that from the period of October 20 to December 20, 2021, the mask mandate likely prevented approximately 58,000 cases and 1,175 hospitalizations, while the vaccinate-or-test requirement may have prevented at most approximately 24,000 cases and 475 hospitalizations. Conclusion Our results indicate that mask mandates and vaccine-or-test requirements are vital in mitigating the burden of COVID-19 during surges of the virus