Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure

Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images

Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wide-swath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2, and UAVSAR wide-swath scenes containing open water, sea ice, and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments

A Neural Network Method for Efficient Vegetation Mapping

This paper describes the application of a neural network method designed to improve the efficiency of map production from remote sensing data. Specifically, the ARTMAP neural network produces vegetation maps of the Sierra National Forest, in Northern California, using Landsat Thematic Mapper (TM) data. In addition to spectral values, the data set includes terrain and location information for each pixel. The maps produced by ARTMAP are of comparable accuracy to maps produced by a currently used method, which requires expert knowledge of the area as well as extensive manual editing. In fact, once field observations of vegetation classes had been collected for selected sites, ARTMAP took only a few hours to accomplish a mapping task that had previously taken many months. The ARTMAP network features fast on-line learning, so the system can be updated incrementally when new field observations arrive, without the need for retraining on the entire data set. In addition to maps that identify lifeform and Calveg species, ARTMAP produces confidence maps, which indicate where errors are most likely to occur and which can, therefore, be used to guide map editing

Neural Sensor Fusion for Spatial Visualization on a Mobile Robot

An ARTMAP neural network is used to integrate visual information and ultrasonic sensory information on a B 14 mobile robot. Training samples for the neural network are acquired without human intervention. Sensory snapshots are retrospectively associated with the distance to the wall, provided by on~ board odomctry as the robot travels in a straight line. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. The neural network effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.Office of Naval Research and Naval Research Laboratory (00014-96-1-0772, 00014-95-1-0409, 00014-95-0657

Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes

In recent decades, climate change has caused a more volatile climate leading to more extreme events such as severe rainstorms, heatwaves and floods which are likely to become more frequent. Aiming to reveal climate change impact on the hydroclimatic extremes in a quantitative sense, this thesis presents a comprehensive analysis from three main strands. The first strand focuses on developing a quantitative modelling framework to quantify the spatiotemporal variation of hydroclimatic extremes for the areas of concern. A spatial random sampling toolbox (SRS-GDA) is designed for randomizing the regions of interest (ROIs) with different geographic locations, sizes, shapes and orientations where the hydroclimatic extremes are parameterised by a nonstationary distribution model whose parameters are assumed to be time-varying. The parameters whose variation with respect to different spatial features of ROIs and climate change are finally quantified by various statistical models such as the generalised linear model. The framework is applied to quantify the spatiotemporal variation of rainfall extremes in Great Britain (GB) and Australia and is further used in a comparison study to quantify the bias between observed and climate projected extremes. Then the framework is extended to a multivariate framework to estimate the time-varying joint probability of more than one hydroclimatic variable in the perspective of non-stationarity. A case study for evaluating compound floods in Ho Chi Minh City, Vietnam is applied for demonstrating the application of the framework. The second strand aims to recognise, classify and track the development of hydroclimatic extremes (e.g., severe rainstorms) by developing a stable computer algorithm (i.e., the SPER toolbox). The SPER toolbox can detect the boundary of the event area, extract the spatial and physical features of the event, which can be used not only for pattern recognition but also to support AI-based training for labelling/cataloguing the pattern from the large-sized, grid-based, multi-scaled environmental datasets. Three illustrative cases are provided; and as the front-end of AI study, an example for training a convolution neural network is given for classifying the rainfall extremes in the last century of GB. The third strand turns to support decision making by building both theory-driven and data-driven decision-making models to simulate the decisions in the context of flood forecasting and early warning, using the data collected via laboratory-style experiments based on various information of probabilistic flood forecasts and consequences. The research work demonstrated in this thesis has been able to bridge the knowledge gaps in the related field and it also provides a precritical insight in managing future risks arising from hydroclimatic extremes, which makes perfect sense given the urgent situation of climate change and the related challenges our societies are facing

Probabilistic modeling and statistical inference for random fields and space-time processes

Author from publisher's list. Cover title.Final report for ONR Grant N00014-91-J-100

Computationally Efficient Specifications of Spatial Point Process Models and Spatio-Temporal Gaussian Models: Combining Remote Sensing Drivers with Geospatial Disease Case Data to Enhance Geographic Epidemiology

In this dissertation, the flexibility of Bayesian hierarchical models specified using a latent Gaussian Markov Random Field (GMRF) are evaluated for use in analyzing large complex spatial and spatio-temporal data with the goal of contributing to an interdisciplinary effort of developing an eco-epidemiological model that quantifies the relationship between remotely sensed water quality and the incidence of ALS (Amyotrophic Lateral Sclerosis or Lou Gehrig’s Disease) over large areas such as Northern New England (NNE). In particular, a Log-Gaussian Cox Process (LGCP) specified by the logarithm of a GMRF on a regular lattice is shown to allow for simultaneous estimation of the spatial distribution of ALS risk and its relationship to remotely sensed water quality metrics. This approach improves on previous analyses of the dataset considered by explicitly accounting for the spatial uncertainty in determining locations of ALS “hotspots” needed in the estimation of the hotspots’ relationship to the water quality of lakes in NNE. Finally, since warming lake temperatures have been associated with more frequent cyanobacteria blooms (blue-green algae), which is a possible risk factor of ALS, a spatially varying coefficient model specified with an Extended Autoregression (EAR) latent process is used in an analysis of remotely sensed surface water temperatures of Lake Champlain. New interpretations of the EAR model are suggested and issues relating to its parameter’s identifiability are investigated