Browsing by Subject "Detection"

Now showing 1 - 16 of 16
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    Automated segmentation and pathology detection in ophthalmic images
    (2014-07) Roy Chowdhury, Sohini
    Computer-aided medical diagnostic system design is an emerging inter-disciplinary technology that assists medical practitioners for providing quick and accurate diagnosis and prognosis of pathology. Since manual assessments of digital medical images can be both ambiguous and time-consuming, computer-aided image analysis and evaluation systems can be beneficial for baseline diagnosis, screening and disease prioritization tasks. This thesis presents automated algorithms for detecting ophthalmic pathologies pertaining to the human retina that may lead to acquired blindness in the absence of timely diagnosis and treatment. Multi-modal automated segmentation and detection algorithms for diabetic manifestations such as Diabetic Retinopathy and Diabetic Macular Edema are presented. Also, segmentation algorithms are presented that can be useful for automated detection of Glaucoma, Macular Degeneration and Vein Occlusions. These algorithms are robust to normal and pathological images and incur low computationally complexity.First, we present a novel blood vessel segmentation algorithm using fundus images that extracts the major blood vessels by applying high-pass filtering and morphological transforms followed by addition of fine vessel pixels that are classified by a Gaussian Mixture Model (GMM) classifier. The proposed algorithm achieves more than 95% vessel segmentation accuracy on three publicly available data sets. Next, we present an iterative blood vessel segmentation algorithm that initially estimates the major blood vessels, followed by iterative addition of fine blood vessel segments till a novel stopping criterion terminates the iterative vessel addition process. This iterative algorithm is specifically robust to thresholds since it achieves 95.35% vessel segmentation accuracy with 0.9638 area under ROC curve (AUC) on abnormal retinal images from the publicly available STARE data set.We propose a novel rule-based automated optic disc (OD) segmentation algorithm that detects the OD boundary and the location of vessel origin (VO) pixel. This algorithm initially detects OD candidate regions at the intersection of the bright regions and the blood vessels in a fundus image subjected to certain structural constraints, followed by the estimation of a best fit ellipse around the convex hull that combines all the detected OD candidate regions. The centroid of the blood vessels within the segmented OD boundary is detected as the VO pixel location. The proposed algorithm results in an average of 80% overlap score on images from five public data sets.We present a novel computer-aided screening system (DREAM) that analyzes fundus images with varying illumination and fields of view, and generates a severity grade for non-proliferative diabetic retinopathy (NPDR) using machine learning. Initially, the blood vessel regions and the OD region are detected and masked as the fundus image background. Abnormal foreground regions corresponding to bright and red retinopathy lesions are then detected. A novel two-step hierarchical classification approach is proposed where the non-lesions or false positives are rejected in the first step. In the second step, the bright lesions are classified as hard exudates and cotton wool spots, and the red lesions are classified as hemorrhages and micro-aneurysms. Finally, the number of lesions detected per image is combined to generate a severity grade. The DReAM system achieves 100% sensitivity, 53.16% specificity and 0.904 AUC on a publicly available MESSIDOR data set with 1200 images. Additionally, we propose algorithms that detect post-operative laser scars and fibrosed tissues and neovascularization in fundus images. The proposed algorithm achieves 94.74% sensitivity and 92.11% specificity for screening normal images in the STARE data set from the images with proliferative diabetic retinopathy (PDR). Finally, we present a novel automated system that segments six sub-retinal thickness maps from optical coherence tomography (OCT) image stacks of healthy patients and patients with diabetic macular edema (DME). First, each image in the OCT stack is denoised using a Wiener Deconvolution algorithm that estimates the speckle noise variance using a Fourier-domain based structural error. Next, the denoised images are subjected to an iterative multi-resolution high-pass filtering algorithm that detects seven sub-retinal surfaces in six iterative steps. The thicknesses of each sub-retinal layer for all scans from a particular OCT stack are then combined to generate sub-retinal thickness maps. Using the proposed system the average inner sub-retinal layer thickness in abnormal images is estimated as 275 um (r = 0.92) with an average error of 9.3 um, while the average thickness of the outer segments in abnormal images is estimated as 57.4 um (r = 0.74) with an average error of 3.5 um. Further analysis of the thickness maps from abnormal OCT image stacks demonstrates irregular plateau regions in the inner nuclear layer (INL) and outer nuclear layer (ONL), whose area can be estimated with r = 0.99 by the proposed segmentation system.
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    Concentration and extraction of Bacillus anthracis spores and ricin.
    (2009-06) Leishman, Oriana Nicole
    Food is an essential part of life for every human and animal. In order to feed the world, food production has become a global industry. This globalization brings efficiency of production, transportation, and year round availability of many ingredients. However, mass production of food also means that any mistake made during production is magnified in scale and distribution. Recent incidents of food contamination have involved not only traditional food pathogens, such as Salmonella and Escherichia coli O157, but also have included chemical contaminants such as melamine. These incidents serve to highlight the inherent vulnerability of food to contamination. Although the majority of foodborne illnesses are caused by a small group of pathogens, this does not preclude other bacteria or agents from being transmitted through food sources. Many potential bioterrorist agents have also the potential to be transmissible through food and water sources. Some of these agents, such as Bacillus anthracis spores and ricin toxin, are also resistant to the effects of existing food processing technologies such as pasteurization. Given the inherent vulnerability of the food production system, it seems a likely target for potential bioterrorism attack. Many rapid and sensitive tests have been developed to detect biological agents in a variety of settings. However, the complex nature of food matrices often limits the application of these tests to food sources. In addition, the distribution of a select agent in a food source may not be homogeneous, and testing of small samples may not represent the whole batch. The goal of this project was to design and test pre-analytical extraction techniques for two potential bioterrorism agents, B. anthracis spores and ricin toxin, from liquid foods. The outcome of this project was the development of a rapid concentration and extraction protocol for milk and fruit juice potentially contaminated with B. anthracis spores. The resulting sample was compatible with detection via real-time PCR for both milk and fruit juice samples and juice samples were compatible with detection with a commercially available lateral flow immunoassay. This concentration and extraction procedure enhanced the limit of detection by 2 log CFU/ml spores, such that real-time PCR can consistently detect B. anthracis at a level of 10 spores/mL in the initial sample. This project also examined the application of immunomagnetic separation for extraction of ricin toxin from liquids. Results from this portion of the project suggested that immunomagnetic beads can specifically bind ricin in traditional immunomagnetic separation. However, recirculating immunomagnetic separation using the Pathatrix® system was not demonstrated to specifically bind ricin.
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    Detection of Whey Protein in a Hot Dog Using Immunomagnetic Separation Coupled with Surface Enhanced Raman Spectroscopy
    (2017-04) Swanson, Benjamin
    With the passing of recent legislation, most notably the Food Allergen Labeling and Consumer Protection Act in 2006 and the Food Safety Modernization Act of 2011, the focus on allergens in the food supply is a top priority for the food industry. With the consideration of unintentional allergens now being considered an adulteration, companies are trying to find detection methods that can accurately identify an unintentional allergen, but that are also rapid enough to use so as not to interrupt the production line. Immunomagnetic Separation (IMS) coupled with Surface Enhanced Raman Spectroscopy (SERS) was investigated in this research as one possible detection method. We decided to test and compare two types of IMS methods, antibody and aptamer, to see if one or the other would produce better results. The methods were based off of previous work by Dr. Lili He and were adapted to detect whey in a hot dog. During initial testing in a pure solution, both of the IMS methods appeared to show similar results, both being able to detect whey at levels of at least 125μg/mL of solution. But once we switched over testing whey in a hot dog, the antibody based IMS method proved to be the better IMS method. With a detection limit of 600μg of whey protein isolate/g of hot dog, the antibody based IMS method proved to be the more effective method. The aptamer IMS method ran into trouble with non-specific binding to the magnetic beads and was unable to detect any whey protein isolate in the hot dogs during the experiment. It is therefore concluded by the results of this experiment that the antibody based 6 IMS-SERS method is a better method to detect whey protein in a hot dog versus the aptamer method.
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    Fusarium and Phytophthora Species associated with root rot of soybean (Glycine max)
    (2011-01) Bienapfl, John Christopher
    Root diseases of soybean cause substantial yield reduction in the United States. Fusarium and Phytophthora represent groups of fungal pathogens commonly associated with root rot of soybean. Little is known regarding their distribution, etiology, and how they may interact in causing root rot on soybean. Additionally, diagnostic tools that allow for rapid and accurate detection of these pathogens are essential for disease management, but need to be developed and validated. Furthermore, fungicidal compounds that potentially affect root infection by these fungal pathogens are being studied to minimize yield losses due to root diseases of soybean and improve crop productivity.
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    Intersection Control Through Video Image Processing: Executive Summary
    (Minnesota Department of Transportation, 1992-07) Michalopoulos, Panos
    Among the most promising and innovative concepts today for alleviating urban traffic congestion is the use of video imaging for vehicle detection, automatic surveillance, and advanced control strategies. Because of its conceptual appeal, research in this area was initiated in the mid 70's in the United States and abroad. A system for vehicle detection through video imaging was recently developed at the University of Minnesota and is being implemented on the 1-394 and l-35W freeways in Minneapolis, Minnesota for incident detection. The Minnesota system, called AUTOSCOPE (TM), emulates loop detectors, a large number of which can easily be placed within the field of the camera's view through interactive graphics. In recent tests its performance matched or exceeded that of loops in vehicle counting, speed measurements, and extraction of certain measures of effectiveness. Evaluation tests of the AUTOSCOPE (TM) were very encouraging, thus the system was installed at a traffic intersection to demonstrate the effectiveness of this new technology as a replacement for loop detectors.
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    A learning approach to detecting lung nodules in CT Images.
    (2009-12) Aschenbeck, Michael G.
    Lung cancer is one of the most common types of cancer and has the highest mortality rate. Unfortunately, it is a long and difficult process for the physician to detect the presence of this disease. He/she must search through three-dimensional medical images and look for possibly cancerous, small structures that are roughly spherical. These structures are called pulmonary nodules. Due to the difficult and time consuming detection task faced by the physician, computer-aided detection (CAD) has been the focus of many research efforts. Most of these works involve segmenting the image into structures, extracting features from the structures, and classifying the resulting feature vectors. Unfortunately, the first of these tasks, segmentation, is a difficult problem and many times the origin for missed detections. This work attempts to eliminate the structure segmentation step. Instead, features are extracted from fixed size subwindows and sent to a classifier. Bypassing the segmentation step allows for every location to be classified. Feature extraction is accomplished by learning a complete basis for the subwindow on the training set and using the inner product of the subwindow with each basis element. This approach is preferred over choosing features based on human interpretation, as the latter approach will most likely result in valuable information being overlooked. The bases used are derived from the singular value decomposition (SVD), a modification of the SVD, tensor decompositions, vectors reminiscent of the Haar wavelets, and the Fourier basis. The features are sent to a number of different classifiers for comparison. The classifiers include parametric methods such as likelihood classifiers and probabilistic clustering, as well as non-parametric classifiers such as kernel support vector machines (SVM), classification trees, and AdaBoost. While different feature and classifiers bring about a wide range of results, the non-parametric classifiers unsurprisingly produce much better detection and false positive rates. The best combination on the test set yields 100\% detection of the nodule subwindows, while only classifying 1\% of the non-nodule windows as nodules. This is in comparison to previous CAD approaches discussed in this thesis which achieve no better than 85\% detection rates.
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    Loki Flight 45
    (2014-07-28) Taylor, Brian
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    Loki Flight 45 v2
    (2014-07-29) Taylor, Brian
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    Low complexity MIMO detection algorithms and implementations
    (2014-12) Huai, Lian
    MIMO techniques use multiple antennas at both the transmitter and receiver sides to achieve diversity gain, multiplexing gain, or both. One of the key challenges in exploiting the potential of MIMO systems is to design high-throughput, low-complexity detection algorithms while achieving near-optimal performance. In this thesis, we design and optimize algorithms for MIMO detection and investigate the associated performance and FPGA implementation aspects.First, we study and optimize a detection algorithm developed by Shabany and Gulak for a K-Best based high throughput and low energy hard output MIMO detection and expand it to the complex domain. The new method uses simple lookup tables, and it is fully scalable for a wide range of K-values and constellation sizes. This technique reduces the computational complexity, without sacrificing performance and the complexity scales only sub-linearly with the constellation size. Second, we apply the bidirectional technique to trellis search and propose a high performance soft output bidirectional path preserving trellis search (PPTS) detector for MIMO systems. The comparative error analysis between single direction and bidirectional PPTS detectors is given. We demonstrate that the bidirectional PPTS detector can minimize the detection error. Next, we design a novel bidirectional processing algorithm for soft-output MIMO systems. It combines features from several types of fixed complexity tree search procedures. The proposed approach achieves a higher performance than previously proposed algorithms and has a comparable computational cost. Moreover, its parallel nature and fixed throughput characteristics make it attractive for very large scale integration (VLSI) implementation.Following that, we present a novel low-complexity hard output MIMO detection algorithm for LTE and WiFi applications. We provide a well-defined tradeoff between computational complexity and performance. The proposed algorithm uses a much smaller number of Euclidean distance (ED) calculations while attaining only a 0.5dB loss compared to maximum likelihood detection (MLD). A 3x3 MIMO system with a 16QAM detector architecture is designed, and the latency and hardware costs are estimated.Finally, we present a stochastic computing implementation of trigonometric and hyperbolic functions which can be used for QR decomposition and other wireless communications and signal processing applications.
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    Produce Safety in the United States: Epidemiological Trends and Risk Management Utilizing a Novel Screening Method for Shiga-Toxin Producing E. coli and Salmonella in Irrigation Water
    (2019-12) Wu, Yan
    Despite significantly improved technologies in food science and public health and tremendous efforts being put by governments to ensure food safety, foodborne outbreaks are still abundant worldwide. Produce products have been frequently implicated in foodborne illness outbreaks in recent years due to changes in consumer demands, consumption habits and production practices. A better understanding on epidemiology changes of produce outbreaks is needed to evaluate current risks associated with produce supply chain and to understand safety regulations regarding produce safety. In addition, it is evident that water used in produce production plays an important role in potentially introducing microbial contaminations. Therefore, its risk management is crucial for safety assurance of the produce supply chain. The goal of this thesis research is to analyze the epidemiological trends of produce outbreaks and to improve the risk management of microbial quality of irrigation water. It summarizes the changing epidemiology of produce outbreaks in the United States from 1998-2007, establishes the baseline to further evaluate the potential impact from the recently implemented Food Safety Modernization Act (FSMA). The study also describes the development, optimization, and evaluation of a novel selective medium for sensitive enrichment and screening of Shiga-toxin producing E. coli and Salmonella in irrigation water. The developed enrichment-indicator system meets the increasing demand of method for multi-pathogen enrichment and detection in a single assay format allowing cost effective detection of STEC and Salmonella within 24 hours.
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    Reduced-complexity epileptic seizure prediction with EEG.
    (2012-01) Park, Yun Sang
    In the dissertation we seek to develop and validate reliable frameworks for human epileptic seizure prediction with electrocorticogram (ECoG) and intracranial electroencephalogram (iEEG). The long-term goal of the research is to develop and prototype an implantable device that can reliably provide alarms prior to a seizure in real-time. The specific objective is to develop a patient-specific algorithm that can predict seizures in ECoG/iEEG with high sensitivity and low false positive rate as well as low complexity. This dissertation starts by demonstrating that seizures can be predicted with linear features of spectral power, and it ultimately focuses on developing a reduced-complexity algorithm that can decode ECoG/iEEG for human epileptic seizure prediction with high sensitivity and acceptable low false positive rate. By contrast to prior prediction work, most of which focused on nonlinear measurements, we demonstrate that human epileptic seizures can be predicted with linear features of ECoG/iEEG in machine learning classification approach. To begin with, a new patient-specific seizure prediction algorithm with ECoG/iEEG is proposed. It is novel in sense that it employs a set of linear features of spectral power from ECoG/iEEG for prediction and that predictive models are established and tested using cost-sensitive support vector machines (SVMs) using double cross-validation method. The proposed algorithm is tested over 433.2 hours of interictal recordings including 80 seizure events from 18 human epileptics in the Freiburg EEG database. It achieves high sensitivity of 97.5% (78/80), a low false alarm rate of 0.27 per hour (total 117 FPs), and total false prediction times of 13.0% (56.4-hour). Bipolar and/or time-differential preprocessing improves sensitivity and false positive rate. For the seizure prediction algorithm to be practically feasible on an implantable device, we further propose a reduced-complexity prediction algorithm. We lower the complexity of the algorithm by investigating and using small numbers of essential features and by replacing nonlinear SVMs and the Kalman filter with linear SVMs and moving-average filters. The key features are determined using the RFE SVM (recursive feature elimination using SVMs). The proposed reduced-complexity algorithm significantly lowers the predictor's complexity and thus the power consumption, while producing high sensitivity as well as reasonable false positives. It is tested on 9 subjects selected from the Freiburg database that result in high prediction rate when the initial prediction algorithm is applied, and successfully demonstrates high sensitivity of 100.0% (38/38) as well as low false positive rate of 0.15 per hour (total 32 FPs) and false positive portion of 9.65% (21.0-hour) in the 217.5-hour interictal recordings with the selected six time-differential features. It has been observed that time-differential preprocessing improves the prediction rate significantly. Additionally, we develop an enhanced approach for seizure onset and offset detection in rats' ECoG. This is an improved version of the automatic seizure detection and termination system in in-vivo rats' ECoG. We improve the system by using a specific frequency range of 14-22Hz, which has been observed to be more relevant to seizure onsets than other bands; by using spectral power instead of spectral amplitudes as a feature set; and by substituting the 2-point moving average filter with the Kalman filter. Furthermore, while the proposed algorithm provides better detection statistics, it also lowers the system's complexity by removing the fast Fourier transform computation and keeping a single structure though the proposed algorithm uses the two different spectral features for detecting onsets and offsets.
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    Seasonal influence on detection probabilities for multiple aquatic invasive species using environmental DNA
    (2023-12-14) Rounds, Christopher; Arnold, Todd W; Chun, Chan Lan; Dumke, Josh; Totsch, Anna; Keppers, Adelle; Edbald, Katarina; García, Samantha M; Larson, Eric R; Nelson, Jenna KR; Hansen, Gretchen JA; round060@umn.edu; Rounds, Christopher; University of Minnesota Fisheries Systems Ecology Lab
    Aquatic invasive species (AIS) are a threat to freshwater ecosystems. Documenting AIS prevalence is critical to effective management and early detection. However, conventional monitoring for AIS is time and resource intensive and is rarely applied at the resolution and scale required for effective management. Monitoring using environmental DNA (eDNA) of AIS has the potential to enable surveillance at a fraction of the cost of conventional methods, but key questions remain related to how eDNA detection probability varies among environments, seasons, and multiple species with different life histories. To quantify spatiotemporal variation in the detection probability of AIS using eDNA sampling, we surveyed 20 lakes with known populations of four aquatic invasive species: Common Carp (Cyprinus carpio), Rusty Crayfish (Faxonius rusticus), Spiny Waterflea (Bythotrephes longimanus), and Zebra Mussels (Dreissena polymorpha). We collected water samples at 10 locations per lake, five times throughout the open water season. Quantitative PCR was used with species-specific assays to determine the presence of species DNA in water samples. Using Bayesian occupancy models, we quantified the effects of lake and site characteristics and sampling season on eDNA detection probability. These results provide critical information for decision makers interested in using eDNA as a multispecies monitoring tool and highlight the importance of sampling when species are in DNA releasing life history stages.
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    Studies on Puccinia coronata var. coronata and other recently observed rust fungi in Minnesota
    (2023-07) Greatens, Nicholas
    In the spring of 2017, a prolific crown rust fungus was observed on the highly invasive glossy buckthorn (Frangula alnus) around Central Park, Roseville, MN. Field observation and greenhouse studies established the grass host as another invasive species, reed canarygrass (Phalaris arundinacea), and sequencing identified the rust fungus as Puccinia coronata var. coronata sensu stricto (Pcc), a taxon of likely Eurasian origin not previously known in Minnesota. Curiously, this new pathogen appeared to have a desirable effect locally, strongly affecting only two invasive plant species. In a research project that began in 2019 and was funded in 2020 by the Minnesota Invasive Terrestrial Plants and Pests Center, we pursued three goals, which correspond to the first three chapters of this dissertation: 1) to determine the distribution of Pcc in Minnesota and North America; 2) to assess its host specificity on potential buckthorn and grass hosts; and 3) to evaluate its potential as an augmentative biological control agent of one or both of its invasive hosts. We report Pcc across the range of glossy buckthorn in the Midwest and Northeastern U.S. but find that it is absent on susceptible reed canarygrass outside the range of glossy buckthorn within Minnesota. Cereal crop and turfgrass species were highly resistant to Pcc, but other grass and buckthorn species were susceptible, including some native North American species. In greenhouse trials, Pcc significantly reduced the height and biomass of both reed canarygrass and glossy buckthorn, supporting its use as a possible biological control agent of one or both of its hosts, although non-target effects and deployment strategies would require further consideration. Chapter four describes a similarly designed study around another crown rust fungus, Puccinia digitaticoronata, which we confirm for the first time in North America. We investigate its relation to other crown rust fungi and its pathogenicity on grass and buckthorn species. In greenhouse studies, the popular turfgrass species Kentucky bluegrass (Poa pratensis) is broadly susceptible, along with numerous other native and weedy Poa spp. Common buckthorn (Rhamnus cathartica), another widespread invasive species, is an aecial host of the rust fungus and likely facilitates its sexual cycle locally. Chapter five combines the results of two small projects published as plant disease notes: first reports of Puccinia glechomatis, a rust of creeping Charlie (Glechoma hederacea) in Minnesota and of a Puccinia sp. causing rust of lemongrass in Minnesota.
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    Techniques for Detection of Incidents and Traffic Disturbances
    (1994-04) Stephanedes, Yorgos J.; Vasilakis, George
    The increasing contribution of incidents to freeway congestion has generated strong interest in the development of incident detection algorithms in the last two decades. According to Federal Highway Administration estimates (Lindley, 1986), incidents currently account for up to 60% of the vehicle-hours lost to freeway congestion; projection for the year 2005 indicates a 70% contribution of incidents to total delay. Fast and accurate detection of incidents can, therefore, substantially reduce the impact of incident congestion on freeway traffic. In particular, when an incident alarm is promptly signaled, traffic management plans can be adjusted in real time to produce the best control and guidance actions in freeway corridors. In addition, the incident management process (detection, response, and clearance) is initiated as emergency vehicles can be promptly dispatched to clear the incident. Existing techniques for the detection of freeway incidents do not provide the necessary reliability for freeway operations. Conventional automated techniques, based on computerized algorithms, are less effective than is desirable for operational use because they generate a high level of false alarms. Operator-assisted methods minimize the false alarm risk, but suffer from missed or delayed detections, are labor intensive, and restrict the potential benefits from advanced, integrated traffic management schemes. The initial phase of this research focused in assessing the performance limitations of conventional automatic incident detection systems. That research was directed towards two objectives, the performance evaluation of major existing algorithms and the development of an improved algorithm. This part of the research pointed out that the existing techniques for the automatic detection of freeway incidents are not reliable as they are seriously handicapped by excessive, operationally unacceptable false alarm rates. The new algorithm proposed by the authors was developed for identifying capacityreducing incidents in freeway traffic. That algorithm aims to minimize the number of false alarms that the existing algorithms generate when temporal random oscillations in the traffic measurements, frequently observed in congested flows, occur. The proposed structure involved preprocessing the traffic data with average, median, or exponential smoothers over data windows of approximately five minute length to eliminate or reduce the size of traffic fluctuations. Although the new algorithm showed an improved and satisfactory performance relative to the conventional algorithms, the initial stage of this research pointed out the need of more research in finding ways and methods for distinguishing between the incident and the non-incident alarms and highlighted the issues that had to be addressed by the second stage of this project.
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    Understanding Lower Extremity Symptoms for Improved Detection of Peripheral Artery Disease: The PREDICT PAD Study
    (2021-08) Brown, Rebecca
    Background Of all the major manifestations of atherosclerosis, peripheral artery disease (PAD) is one of the most underdiagnosed and undertreated vascular diseases, due, in part, to the large number of individuals who experience atypical symptoms, yet our current screening tools are designed to detect those with classic symptoms. PAD causes functional decline and disability, low quality of life, and increased risk of all-cause mortality and cardiovascular death, therefore there is an immediate need to improve detection methods. Aims This dissertation is aimed to 1) Comprehensively describe the range of atypical symptom characteristics in individuals with PAD, how atypical symptoms are defined, and examine potential factors associated with atypical symptoms reported in the literature. 2) Identify characteristics that discriminate between PAD and non-PAD in a group of previously undiagnosed individuals with any type of lower extremity symptoms. 3) Measure the effects of exercise on symptom reporting and calf muscle ischemia and 4) elucidate the experience of individuals with undiagnosed PAD and explore differences in symptomatology in those with and without PAD. Methods Aim 1. A critical review of the published literature on symptom description in PAD was conducted with a particular focus on articles delineating classic or typical symptoms from atypical symptoms. Studies were analyzed based on methodological approach including a) questionnaire-based, b) clinician assessment, and c) qualitative interview. The definitions and associated conditions are discussed. Aim 2. One-time study visit was conducted to evaluate lower extremity symptoms at rest, during, and post exercise in adults 60 years and older with persistent lower extremity symptoms, not previously diagnosed with PAD (n = 25). Symptom assessment included validated questionnaires, a symptom adjective checklist, measurement of muscle tissue oxygenation, and an exploratory semi-structured interview. The visit concluded with a diagnostic vascular assessment using the ankle brachial index (ABI) test. Results were analyzed by naturally occurring groups based off the ABI (abnormal vs normal ABI). Aim 3. Symptoms were measured pre and post six-minute walk test to determine whether exercise elicits symptoms that more closely resemble classic PAD symptoms. Muscle tissue oxygen saturation was measured using near infrared spectroscopy to determine differences in recovery time post exercise between the PAD and non-PAD group. Aim 4. A qualitative content analysis of the audio-recorded semi-structured interviews was conducted to gain a deeper understanding of the differences between those with and without PAD and to understand the experience of individuals with undiagnosed PAD. Results Aim 1. Twenty-four articles were included in the review and was comprised of 8,169 unique individuals with PAD, with an average age of 68.0 years (42.3% female). The definition of atypical symptoms varied across the literature, as did the prevalence ranging from 7.9% - 50.3%. Atypical symptoms were more often associated with increased rates of comorbid diseases, coexisting conditions affecting ambulation, and a greater number of PAD risk factors. The majority of participants reported atypical symptoms and as well as interference with activities of daily living, physical function, and social and personal lives. Aim 2. Four questions were statistically significantly different between participants in the PAD group vs. the non-PAD group. Two of the four questions are contrary to the expected findings. The questions that best differentiated PAD from non-PAD were “Do your symptoms disappear while walking?”, “Do you have difficulty keeping up with your friends or family?”, “Do you have symptoms while sitting?”, “Where are your symptoms?”. Participants in the PAD group reported that their symptoms did not disappear while walking, they had difficulty keeping up with friends and family, had pain while sitting, and were less likely to experience calf or thigh pain. Aim 3. Exercise did not yield symptoms that more closely resembled classic PAD symptoms. Post exercise, the sensitivity and specificity of the validated screening questionnaires were either unchanged or worse. Participants in the PAD group took longer to recover based off return to normal muscle tissue oxygenation levels. Aim 4. The content analysis revealed eight concepts, two of which were unique to the PAD group. These were: Breath of Physical Findings and Confounding Factors, Coping Strategies, Impact on Activities of Daily Living, Determinants of Behavior, Communication Barriers, Symptoms Threaten, Credibility Feels at Risk (PAD only), and Unexplained Symptoms are Distressing (PAD only). Conclusion This dissertation examined discriminating characteristics of PAD in a sample of participants who were previously undiagnosed. Four screening questions may improve detection of PAD, however there are many communication barriers, atypical symptoms, and overlapping symptoms which continuously plague this approach to screening. Participants with undiagnosed PAD experienced discomfort, social limitations, and were distressed over their symptoms. The results emphasize the need to use definitions that serve to meet the needs of the patients who experience them. A broader definition of claudication beyond classic claudication is needed to improve access to vascular testing and enhance detection of PAD.
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    Wide Area Detection System (WADS): Image Recognition Algorithms
    (1990-02) Michalopoulos, Panos; Johnston, S. E.; Fundakowski, R. A.; Fitch, R. C.
    Vehicle detection through machine vision is one of the most promising advanced technologies available today for dealing with the problem of urban traffic congestion. In this project an existing Wide Area Detection System (WADS) was improved for performing detection under all weather, traffic, and artifact conditions (e.g. shadows, reflections, lightning, etc. As a result of this and other related research efforts by the same team, a real-time (instead of the initially envisioned off-line) multispot breadboard WADS system was developed, installed, tested, and demonstrated in several real-life situations. The system can simultaneously detect traffic at multiple points within the field of the camera's view and emulates loop detectors. The test results to this point suggest high accuracy levels, comparable to loop detectors, while speed measurement appears to be more accurate than loops. Live demonstrations and off-line presentations generated the enthusiasm and support of practicing engineers and public officials. They also suggest that the WADS system developed in this project is the most advanced one available today. Despite this, further work remains to be done prior to production. This includes extensive field testing and validation as well as implementation of applications possibly through demonstration projects. This report describes the WADS algorithm development and testing and makes recommendations for field implementation of the technology.