Three--dimensional medical imaging: Algorithms and computer systems

This paper presents an introduction to the field of three-dimensional medical imaging It presents medical imaging terms and concepts, summarizes the basic operations performed in three-dimensional medical imaging, and describes sample algorithms for accomplishing these operations. The paper contains a synopsis of the architectures and algorithms used in eight machines to render three-dimensional medical images, with particular emphasis paid to their distinctive contributions. It compares the performance of the machines along several dimensions, including image resolution, elapsed time to form an image, imaging algorithms used in the machine, and the degree of parallelism used in the architecture. The paper concludes with general trends for future developments in this field and references on three-dimensional medical imaging

Analytische Methoden bei Gravitationslinsenphänomenen: Schatten Schwarzer Löcher

This doctoral thesis is about an analytic way to describe the shadow of black holes. As introduction, a survey of the attempts to observe the shadow of the black holes in our Galaxy near Sgr A* and in the neighbouring galaxy M87 is presented. Black holes are described by metrics of the general Plebanski-Demianski class of space-times. All these metrics are axially symmetric and stationary type D solutions to the Einstein-Maxwell equations with a cosmological constant. The space-times are characterized by seven parameters: mass, spin, electric and magnetic charge, gravitomagnetic NUT charge, a so-called acceleration parameter and the cosmological constant. Based on a detailed discussion of the metrics, analytical formulas are derived for the photon regions (regions that contain spherical lightlike geodesics) and for the boundary curve of the shadow as it is seen by an observer at given Boyer-Lindquist coordinates in the domain of outer communication. They make it possible to analyze the dependency of the shadow of a Kerr black hole on the motion of the observer. For all cases, the photon regions and shadows are visualized for various values of the parameters. The analytical formulas are used to find explicit expressions for the horizontal and vertical angular diameters of the shadow. Finally, these values are estimated for the black holes at the centers of our Galaxy and of M87

Multisource Data Integration in Remote Sensing

Papers presented at the workshop on Multisource Data Integration in Remote Sensing are compiled. The full text of these papers is included. New instruments and new sensors are discussed that can provide us with a large variety of new views of the real world. This huge amount of data has to be combined and integrated in a (computer-) model of this world. Multiple sources may give complimentary views of the world - consistent observations from different (and independent) data sources support each other and increase their credibility, while contradictions may be caused by noise, errors during processing, or misinterpretations, and can be identified as such. As a consequence, integration results are very reliable and represent a valid source of information for any geographical information system

Recognizing Objects And Reasoning About Their Interactions

The task of scene understanding involves recognizing the different objects present in the scene, segmenting the scene into meaningful regions, as well as obtaining a holistic understanding of the activities taking place in the scene. Each of these problems has received considerable interest within the computer vision community. We present contributions to two aspects of visual scene understanding. First we explore multiple methods of feature selection for the problem of object detection. We demonstrate the use of Principal Component Analysis to detect avifauna in field observation videos. We improve on existing approaches by making robust decisions based on regional features and by a feature selection strategy that chooses different features in different parts of the image. We then demonstrate the use of Partial Least Squares to detect vehicles in aerial and satellite imagery. We propose two new feature sets; Color Probability Maps are used to capture the color statistics of vehicles and their surroundings, and Pairs of Pixels are used to capture captures the structural characteristics of objects. A powerful feature selection analysis based on Partial Least Squares is employed to deal with the resulting high dimensional feature space (almost 70,000 dimensions). We also propose an Incremental Multiple Kernel Learning (IMKL) scheme to detect vehicles in a traffic surveillance scenario. Obtaining task and scene specific datasets of visual categories is far more tedious than obtaining a generic dataset of the same classes. Our IMKL approach initializes on a generic training database and then tunes itself to the classification task at hand. Second, we develop a video understanding system for scene elements, such as bus stops, crosswalks, and intersections, that are characterized more by qualitative activities and geometry than by intrinsic appearance. The domain models for scene elements are not learned from a corpus of video, but instead, naturally elicited by humans, and represented as probabilistic logic rules within a Markov Logic Network framework. Human elicited models, however, represent object interactions as they occur in the 3D world rather than describing their appearance projection in some specific 2D image plane. We bridge this gap by recovering qualitative scene geometry to analyze object interactions in the 3D world and then reasoning about scene geometry, occlusions and common sense domain knowledge using a set of meta-rules

Changes in Tall Shrub Abundance on the North Slope of Alaska, 2000-2010

The observed greening of Arctic vegetation and the expansion of shrubs in the last few decades has likely had profound implications for the tundra ecosystem, including feedbacks to climate. Uncertainty surrounding the magnitude, direction, and implications of this vegetation shift calls for monitoring of vegetation structural parameters, such as fractional cover of shrubs. Due to the extent of the North Slope of Alaska and its extreme environments, remote sensing may be the most suitable tool to produce wall-to-wall fractional shrub cover maps for the entire region, however, most regional maps have relied on vegetation indices or needed many years worth of data to cover the whole region. Here, a new mapping approach is presented that uses satellite imagery from the Multi-angle Imaging SpectroRadiometer (MISR) sensor and some landscape variables to predict tall shrub (\u3e 0.5 m) cover with the ultimate goal of evaluating temporal changes in tall shrub fractional cover during the period of 2010-2000. Specifically, we: 1) undertook two field surveys in the North Slope of Alaska to obtain estimates of tall shrub cover, canopy height, crown radius, and total number of shrubs at 26 sites (250 m × 250 m each); 2) evaluated the ability of the semi-automated image interpretation algorithm CANAPI - CANopy Analysis from Panchromatic Imagery, to derive structural data for tall (\u3e 0.5 m) shrubs in the Arctic; 3) constructed a robust reference database with estimates of shrub structural parameters; 4) trained and validated the boosted regression tree model to predict tall shrub fractional cover from moderate resolution imagery; 5) created the 2000 and the 2010 tall shrub fractional cover map for the North Slope of Alaska; and 6) evaluated the changes in shrub abundance during the period 2010-2000 in the North Slope of Alaska. Results from the field surveys suggested that tall shrub fractional cover was less than 5% at 250 m scales. The evaluation of the CANAPI algorithm showed that CANAPI could successfully retrieve fractional cover (R2 = 0.83, P \u3c 0.001), mean crown radius (R2 = 0.81, P \u3c 0.001), and total number of shrubs (R2 = 0.54, P \u3c 0.001) from very-high resolution imagery. As a result, a robust reference database was constructed with estimates of tall shrub fractional cover, canopy radius, and total number of shrubs for 1,039 sites across the domain of the North Slope. After the training and validation of the Boosted Regression Tree (BRT), the best model used 14 predictor variables and explained 52% of the variation in the response variable, fractional cover. The red reflectance, slope, nadir Bidirectional Reflectance Distribution Function (BRDF) adjusted weight of determination, and isotropic scattering kernel were the variables more often used to generate the regression trees, and therefore they contributed the most to the model. The trained BRT model was used to construct the tall shrub fractional cover map for the year 2000 and 2010 using moderate resolution imagery. The maps revealed that cover ranged from 0.00 to 0.21 and about 75% of the sites had a fractional cover less than 0.013. High cover values were predicted along floodplains, creeks, and sloped terrain. The 2000 MISR-derived fractional cover map presented here outperformed the 2000 Landsat-derived tall shrub fractional cover map when compared to the robust validation data set (R2= 0.38, Root Mean Square Error (RMSE) = 0.08). Temporal comparisons of tall shrub abundance in the MISR-derived maps suggested that shrubs expanded during the period 2000-2010. The extent of the area that unequivocally experienced a robust change in tall shrub cover was less than 1 % (1,487 km2) of the total area of the North Slope of Alaska (213,090 km2). It is possible that tall shrubs may have expanded throughout a larger area but there is insufficient precision in the MISR-based estimates to make an unequivocal determination. Nevertheless, it seems that there was a positive trend toward an increase in shrub cover considering that 95% of the locations that had a robust change saw an increase. The tall shrub cover expansion rate varied between 0.006 yr-1 and 0.017 yr-1, being higher along the forest-tundra ecotone, north of the Brooks Range. More research is necessary to determine if the increase in cover corresponded to the advance of the tree line, or to the expansion of the tall shrubs, or both

The integrated hydrologic and societal impacts of a warming climate in Interior Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2014In this dissertation, interdisciplinary research methods were used to examine how changes in hydrology associated with climate affect Alaskans. Partnerships were established with residents of Fairbanks and Tanana to develop scientific investigations relevant to rural Alaskans. In chapter 2, local knowledge was incorporated into scientific models to identify a socialecological threshold used to model potential driftwood harvest from the Yukon River. Anecdotal evidence and subsistence calendar records were combined with scientific data to model the harvest rates of driftwood. Modeling results estimate that between 1980 and 2010 hydrologic factors alone were responsible for a 29% decrease in the annual wood harvest, which approximately balanced a 23% reduction in wood demand due to a decline in number of households. The community's installation of wood-fired boilers in 2007 created a threshold increase (76%) in wood demand that is not met by driftwood harvest. Modeling of climatic scenarios illustrates that increased hydrologic variability decreases driftwood harvest and increases the financial or temporal costs for subsistence users. In chapter 3, increased groundwater flow related to permafrost degradation was hypothesized to be affect river ice thickness in sloughs of the Tanana River. A physically-based, numerical model was developed to examine the importance of permafrost degradation in explaining unfrozen river conditions in the winter. Results indicated that ice melt is amplified by increasing groundwater upwelling rates, groundwater temperatures, and snowfall. Modeling results also suggest that permafrost degradation could be a valid explanation of the phenomenon, but does not address the potential drivers (e.g. warming climate, forest fire, etc.) of the permafrost warming. In chapter 4, remote sensing techniques were hypothesized to be useful for mapping dangerous ice conditions on the Tanana River in interior Alaska. Unsupervised classification of high-resolution satellite imagery was used to identify and map open water and degraded ice conditions on the Tanana River. Ninety-five percent of the total river channel surface was classified as "safe" for river travel, while 4% of the channel was mapped as having degraded ice and 0.6% of the channel was classified as open water (overall accuracy of 73%). This research demonstrates that the classification of high-resolution satellite images can be useful for mapping hazardous ice for recreational, transportation, or industrial applications in northern climates. These results are applicable to communities throughout the North. For people that rely upon subsistence activities, increased variability in climate cycles can have substantial financial, cultural, recreational, or even mortal consequences. This research demonstrates how collaborations between scientists and local stakeholders can create tools that help to assess the impacts of increased environmental variability (such as flooding) or to detect or predict unsafe conditions (such as thin or unpredictable ice cover). Based upon this research, I conclude that regional-scale adaptations and technological advances (such as modeling and remote sensing tools) may help to alleviate the effects of environmental variability associated by climate