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Context. A large portion of all the transportation in the world consists of voyages over the sea. Systems such as Automatic Identification Systems (AIS) have been developed to aid in the surveillance of the maritime traffic, in order to help keeping the amount accidents and illegal activities down. In recent years a lot of time and effort has gone into automated surveillance of maritime traffic, with the purpose of finding and reporting behaviour deviating from what is considered normal. An issue with many of the present approaches is inaccuracy and the amount of false positives that follow from it. Objectives. This study continues the work presented by Woxberg and Grahn in 2015. In their work they used quadtrees to improve upon the existing tool STRAND, created by Osekowska et al. STRAND utilizes potential fields to build a model of normal behaviour from received AIS data, which can then be used to detect anomalies in the traffic. The goal of this study is to further improve the system by adding statistical analysis to reduce the number of false positives detected by Grahn and Woxberg's implementation. Method. The method for reducing false positives proposed in this thesis uses the charge in overlapping potential fields to approximate a normal distribution of the charge in the area. If a charge is too similar to that of the overlapping potential fields the detection is dismissed as a false positive. A series of experiments were ran to find out which of the methods proposed by the thesis are most suited for this application.   Results. The tested methods for estimating the normal distribution of a cell in the potential field, i.e. the unbiased formula for estimating the standard deviation and a version using Kalman filtering, both find as many of the confirmed anomalies as the base implementation, i.e. 9/12. Furthermore, both suggested methods reduce the amount of false positives by 11.5% in comparison to the base implementation, bringing the amount of false positives down to 17.7%. However, there are indications that the unbiased method has more promise. Conclusion. The two proposed methods both work as intended and both proposed methods perform equally. There are however indications that the unbiased method may be better despite the test results, but a new extended set of training data is needed to confirm or deny this. The two methods can only work if the examined overlapping potential fields are independent from each other, which means that the methods can not be applied to anomalies of the positional variety. Constructing a filter for these anomalies is left for future study.

Previous research into the accuracy of referrals for glaucoma has shown that a large number of referrals to the Hospital Eye Service are false positive. Research in areas of healthcare other than ophthalmology has shown that psychological distress can be caused by false positive referrals. The present study aimed to evaluate the quality of referrals to the HES for all ocular pathologies, and also to quantify the proportion of these referrals that were false positive. Any commonality between false positive referrals was investigated. The psychological effect of being referred to the HES was also evaluated using the Hospital Anxiety and Depression Scale (HADS) and State-Trait Anxiety Inventory (STAI). Both scales were validated in this population with Rasch analysis before use. A final aim was to develop an improvement to the present referral pathway in order to reduce numbers of false positive referrals. The accuracy of referrals to the HES appears to improve as clinicians become more experienced, and greater numbers of false positive referrals are generated by female clinicians. Optometrists refer patients with a wide range of ocular diseases and in most cases include both fundus observations and visual acuity measurements in their referrals. GPs mainly refer patients with anterior segment disorders, particularly lid lesions, based on direct observation and symptoms. Illegibility and missing clinical information reduce the quality of many optometric referrals. Patients referred to the HES experience raised levels of anxiety as measured by the STAI and raised levels of depression as measured by the HADS-Depression subscale. As a method of assessing psychological distress, the questionnaires HADS-T (all items), STAI-S (State subscale) and STAI-T (Trait subscale) show good discrimination between patients when administered to a population of new ophthalmic outpatients, despite all having a floor effect. Subsequently a referral refinement service was developed which reduced numbers of unnecessary referrals and reduced costs for the NHS.

O presente trabalho consiste na investigação das características da imagem mamográfica digitalizada para definir automaticamente parâmetros de processamento em um esquema de diagnóstico auxiliado por computador (CAD) para mamografia, com o objetivo de se obter o melhor desempenho possível. Além disso, com base na aplicação dos resultados dessa primeira investigação, propõe-se também uma técnica de redução dos índices de falso-positivo em esquemas CAD visando à redução do número de biópsias desnecessárias. Para a definição automática dos parâmetros de processamento nas técnicas de detecção de microcalcificações e nódulos, foram extraídas algumas características das imagens, como desvio padrão, terceiro momento e o limiar de binarização. Utilizando o método de automatização proposto, observou-se um aumento de 20% no desempenho do esquema CAD (Az da curva ROC) em relação ao método não automatizado com parâmetro fixo. Para que fosse possível o processamento da imagem mamográfica inteira pelo esquema CAD e as técnicas desenvolvidas, foi desenvolvida também uma técnica para seleção automática de regiões de interesses, que recorta partes relevantes da mama para a segmentação. O índice de falsos positivos foi tratado por técnica específica desenvolvida com base na comparação das duas incidências típicas do exame mamográfico que, juntamente com a avaliação automática da imagem no pré-processamento para detecção de microcalcificações produziu uma redução significativa de 86% daquela taxa em relação ao procedimento de parâmetro fixo. / This present work consists on the investigation of mammographic image characteristics for automatic determination of image processing parameters for a mammography computer aided diagnosis scheme (CAD) in order to get optimal performance. Additionally, using the results obtained on this first investigation, it was also developed a new technique for the reduction of false-positive rates on CAD projects, which can result on the reduction of the number of unnecessary biopsies. For the automatic definition of the image processing parameters for the techniques of detection of microcalcifications and nodules, some image characteristics had been extracted, as standard deviation, third momentum and the thresholding value. Using the proposed automatization method it was reported an increase of 20% in the CAD performance (evaluated determining the ROC curve) in comparison to the non-automatic method (fixed parameter). Besides, for CAD schemes it is necessary to process the entire mammographic image. Thus, it was also developed a technique for automatic selection of regions of interests in the mammogram, which extracts better regions from breast image for further segmentation. False-positives rates was treated by a specific technique based on the comparison of the two typical incidences of mammographic examination that together with the automatic parameter determination method for microcalcification detection produced a significant reduction of 86% of that rate in relation to the procedure that uses fixed parameter.

This thesis investigates the application of index based, region segmentation, and deep learning methods to the sensor fusion of omnidirectional (O-D) Infrared (IR) sensors, Kinnect sensors, and O-D vision sensors to increase the level of intelligent perception for unmanned robotic platforms. The goals of this work is first to provide a more robust calibration approach and improve the calibration of low resolution and noisy IR O-D cameras. Then our goal was to explore the best approach to sensor fusion for vegetation detection. We looked at index based, region segmentation, and deep learning methods and compared them with a goal of significant reduction in false positives while maintaining reasonable vegetation detection. The results are as follows: Direct Spherical Calibration of the IR camera provided a more consistent and robust calibration board capture and resulted in the best overall calibration results with sub-pixel accuracy The best approach for sensor fusion for vegetation detection was the deep learning approach, the three methods are detailed in the following chapters with the results summarized here. Modified Normalized Difference Vegetation Index approach achieved 86.74% recognition and 32.5% false positive, with peaks to 80% Thermal Region Fusion (TRF) achieved a lower recognition rate at 75.16% but reduced false positives to 11.75% (a 64% reduction) Our Deep Learning Fusion Network (DeepFuseNet) results demonstrated that deep learning approach showed the best results with a significant (92%) reduction in false positives when compared to our modified normalized difference vegetation index approach. The recognition was 95.6% with 2% false positive. Current approaches are primarily focused on O-D color vision for localization, mapping, and tracking and do not adequately address the application of these sensors to vegetation detection. We will demonstrate the contradiction between current approaches and our deep sensor fusion (DeepFuseNet) for vegetation detection. The combination of O-D IR and O-D color vision coupled with deep learning for the extraction of vegetation material type, has great potential for robot perception. This thesis will look at two architectures: 1) the application of Autoencoders Feature Extractors feeding a deep Convolution Neural Network (CNN) fusion network (DeepFuseNet), and 2) Bottleneck CNN feature extractors feeding a deep CNN fusion network (DeepFuseNet) for the fusion of O-D IR and O-D visual sensors. We show that the vegetation recognition rate and the number of false detects inherent in the classical indices based spectral decomposition are greatly improved using our DeepFuseNet architecture. We first investigate the calibration of omnidirectional infrared (IR) camera for intelligent perception applications. The low resolution omnidirectional (O-D) IR image edge boundaries are not as sharp as with color vision cameras, and as a result, the standard calibration methods were harder to use and less accurate with the low definition of the omnidirectional IR camera. In order to more fully address omnidirectional IR camera calibration, we propose a new calibration grid center coordinates control point discovery methodology and a Direct Spherical Calibration (DSC) approach for a more robust and accurate method of calibration. DSC addresses the limitations of the existing methods by using the spherical coordinates of the centroid of the calibration board to directly triangulate the location of the camera center and iteratively solve for the camera parameters. We compare DSC to three Baseline visual calibration methodologies and augment them with additional output of the spherical results for comparison. We also look at the optimum number of calibration boards using an evolutionary algorithm and Pareto optimization to find the best method and combination of accuracy, methodology and number of calibration boards. The benefits of DSC are more efficient calibration board geometry selection, and better accuracy than the three Baseline visual calibration methodologies. In the context of vegetation detection, the fusion of omnidirectional (O-D) Infrared (IR) and color vision sensors may increase the level of vegetation perception for unmanned robotic platforms. A literature search found no significant research in our area of interest. The fusion of O-D IR and O-D color vision sensors for the extraction of feature material type has not been adequately addressed. We will look at augmenting indices based spectral decomposition with IR region based spectral decomposition to address the number of false detects inherent in indices based spectral decomposition alone. Our work shows that the fusion of the Normalized Difference Vegetation Index (NDVI) from the O-D color camera fused with the IR thresholded signature region associated with the vegetation region, minimizes the number of false detects seen with NDVI alone. The contribution of this work is the demonstration of two new techniques, Thresholded Region Fusion (TRF) technique for the fusion of O-D IR and O-D Color. We also look at the Kinect vision sensor fused with the O-D IR camera. Our experimental validation demonstrates a 64% reduction in false detects in our method compared to classical indices based detection. We finally compare our DeepFuseNet results with our previous work with Normalized Difference Vegetation index (NDVI) and IR region based spectral fusion. This current work shows that the fusion of the O-D IR and O-D visual streams utilizing our DeepFuseNet deep learning approach out performs the previous NVDI fused with far infrared region segmentation. Our experimental validation demonstrates an 92% reduction in false detects in our method compared to classical indices based detection. This work contributes a new technique for the fusion of O-D vision and O-D IR sensors using two deep CNN feature extractors feeding into a fully connected CNN Network (DeepFuseNet).

Ovarian cancer (OC) is the fifth most common cancer among women and has the highest mortality of any cancer of the female reproductive system. The majority (61%) of OC cases are diagnosed at a distant stage. Because diagnoses occur most commonly at a late-stage and prognosis for advanced disease is poor, research focusing on the development of effective OC screening methods to facilitate early detection in high-risk, asymptomatic women is fundamental in reducing OC-specific mortality. Presently, there is no screening modality proven efficacious in reducing OC-mortality. However, transvaginal ultrasonography (TVS) has shown value in early detection of OC. TVS presents with the possibility of false positive results which occur when a women receives an abnormal TVS screening test result that is deemed benign following repeat testing (about 7% of the time). The purpose of this dissertation was to evaluate the impact of false positive TVS screening test results on a variety of psychological and behavioral outcomes using mixed and trajectory statistical modeling. The three specific aims of this dissertation were to 1) compare psychological and behavioral outcomes between women receiving normal and false positive results, 2) identify characteristics of women receiving false positive results associated with increased OC-specific distress and 3) characterize distress trajectories following receipt of false positive results. Analyses included a subset of women participating in an experimental study conducted through the University of Kentucky Ovarian Cancer Screening Program. 750 women completed longitudinal assessments: 375 false positive and 375 normal results. Mixed and group-based trajectory modeling were used to evaluate the specific aims. Results suggest women receiving false positive TVS result experience increased OC-specific distress compared to women receiving normal results. Among those receiving false positives, less education, no history of an abnormal screening test result, less optimism and more social constraint were associated with increased OC-specific distress. Family history was associated with increased distress among women with monitoring informational coping styles. Three distinct trajectories characterize the trajectory of distress over a four-month study period. Although decreasing over time, a notable proportion of women experience sustained high levels of OC-specific distress.

Interpretation of Common Used Tumor Markers Affected by Systemic and Inflammatory Diseases Introduction: An examination of tumor markers is often made as a basis for the successful diagnosis and follow-up treatment of patients with malignant tumors. However, are tumor markers truly significant by themselves, or are they just a baseline quantitative expression of value that we use to diagnose a patient as better or worse based on it increasing or decreasing value? Objective: This paper attempts to answer the question of what factors can affect serum protein and mucin markers and thus lead to a misinterpretation of their results. Methods: Tumor markers were determined by isotopic and non-isotopic laboratory analysis methods, using operational protocols of the immunoanalytic laboratory. All methods were checked using internal quality control, and four times a year using an external quality control. Additionally, 16 236 samples were analysed using 3180 probands during the period 2008-2014. Results: We discovered that in premenopausal women, the markers AFP, CA 125 and HE 4 rise during ovulation peak periods while other markers changed minimally or not at all. However, in postmenopausal women, we proved the incidence of a false positivity marker. With women in the 1st and 2nd trimester of pregnancy, the...

O presente trabalho consiste na investigação das características da imagem mamográfica digitalizada para definir automaticamente parâmetros de processamento em um esquema de diagnóstico auxiliado por computador (CAD) para mamografia, com o objetivo de se obter o melhor desempenho possível. Além disso, com base na aplicação dos resultados dessa primeira investigação, propõe-se também uma técnica de redução dos índices de falso-positivo em esquemas CAD visando à redução do número de biópsias desnecessárias. Para a definição automática dos parâmetros de processamento nas técnicas de detecção de microcalcificações e nódulos, foram extraídas algumas características das imagens, como desvio padrão, terceiro momento e o limiar de binarização. Utilizando o método de automatização proposto, observou-se um aumento de 20% no desempenho do esquema CAD (Az da curva ROC) em relação ao método não automatizado com parâmetro fixo. Para que fosse possível o processamento da imagem mamográfica inteira pelo esquema CAD e as técnicas desenvolvidas, foi desenvolvida também uma técnica para seleção automática de regiões de interesses, que recorta partes relevantes da mama para a segmentação. O índice de falsos positivos foi tratado por técnica específica desenvolvida com base na comparação das duas incidências típicas do exame mamográfico que, juntamente com a avaliação automática da imagem no pré-processamento para detecção de microcalcificações produziu uma redução significativa de 86% daquela taxa em relação ao procedimento de parâmetro fixo. / This present work consists on the investigation of mammographic image characteristics for automatic determination of image processing parameters for a mammography computer aided diagnosis scheme (CAD) in order to get optimal performance. Additionally, using the results obtained on this first investigation, it was also developed a new technique for the reduction of false-positive rates on CAD projects, which can result on the reduction of the number of unnecessary biopsies. For the automatic definition of the image processing parameters for the techniques of detection of microcalcifications and nodules, some image characteristics had been extracted, as standard deviation, third momentum and the thresholding value. Using the proposed automatization method it was reported an increase of 20% in the CAD performance (evaluated determining the ROC curve) in comparison to the non-automatic method (fixed parameter). Besides, for CAD schemes it is necessary to process the entire mammographic image. Thus, it was also developed a technique for automatic selection of regions of interests in the mammogram, which extracts better regions from breast image for further segmentation. False-positives rates was treated by a specific technique based on the comparison of the two typical incidences of mammographic examination that together with the automatic parameter determination method for microcalcification detection produced a significant reduction of 86% of that rate in relation to the procedure that uses fixed parameter.

Interpretation of Common Used Tumor Markers Affected by Systemic and Inflammatory Diseases Introduction: An examination of tumor markers is often made as a basis for the successful diagnosis and follow-up treatment of patients with malignant tumors. However, are tumor markers truly significant by themselves, or are they just a baseline quantitative expression of value that we use to diagnose a patient as better or worse based on it increasing or decreasing value? Objective: This paper attempts to answer the question of what factors can affect serum protein and mucin markers and thus lead to a misinterpretation of their results. Methods: Tumor markers were determined by isotopic and non-isotopic laboratory analysis methods, using operational protocols of the immunoanalytic laboratory. All methods were checked using internal quality control, and four times a year using an external quality control. Additionally, 16 236 samples were analysed using 3180 probands during the period 2008-2014. Results: We discovered that in premenopausal women, the markers AFP, CA 125 and HE 4 rise during ovulation peak periods while other markers changed minimally or not at all. However, in postmenopausal women, we proved the incidence of a false positivity marker. With women in the 1st and 2nd trimester of pregnancy, the...

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