Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.

Nutritional status in commercial currant fields

Selostus: Herukkaviljelmien ravinnetila

Weeds in spring cereal fields in Finland - a third survey

Selostus: Kolmas kevätviljapeltojen rikkakasvikartoitus

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Stress intensity factor of wood from crack-tip displacement fields obtained from digital image processing

Abstract

Exposure to radio-frequency electromagnetic fields from broadcast transmitters and risk of childhood cancer: a census-based cohort study.

We investigated the association between exposure to radio-frequency electromagnetic fields (RF-EMFs) from broadcast transmitters and childhood cancer. First, we conducted a time-to-event analysis including children under age 16 years living in Switzerland on December 5, 2000. Follow-up lasted until December 31, 2008. Second, all children living in Switzerland for some time between 1985 and 2008 were included in an incidence density cohort. RF-EMF exposure from broadcast transmitters was modeled. Based on 997 cancer cases, adjusted hazard ratios in the time-to-event analysis for the highest exposure category (>0.2 V/m) as compared with the reference category (<0.05 V/m) were 1.03 (95% confidence interval (CI): 0.74, 1.43) for all cancers, 0.55 (95% CI: 0.26, 1.19) for childhood leukemia, and 1.68 (95% CI: 0.98, 2.91) for childhood central nervous system (CNS) tumors. Results of the incidence density analysis, based on 4,246 cancer cases, were similar for all types of cancer and leukemia but did not indicate a CNS tumor risk (incidence rate ratio = 1.03, 95% CI: 0.73, 1.46). This large census-based cohort study did not suggest an association between predicted RF-EMF exposure from broadcasting and childhood leukemia. Results for CNS tumors were less consistent, but the most comprehensive analysis did not suggest an association.

Symmetric periodic orbits near heteroclinic loops at infinity for a class of polynomial vector fields

For polynomial vector fields in R3, in general, it is very difficult to detect the existence of an open set of periodic orbits in their phase portraits. Here, we characterize a class of polynomial vector fields of arbitrary even degree having an open set of periodic orbits. The main two tools for proving this result are, first, the existence in the phase portrait of a symmetry with respect to a plane and, second, the existence of two symmetric heteroclinic loops.

Active deformation fields: dense deformation field estimation for atlas-based segmentation using the active contour framework.

This paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner.

Avalanche-like vortex penetration driven by pulsed microwave fields in an epitaxial LaSrCuO thin film

Different vortex penetration regimes have been registered in the output voltage signal of a magnetometer when single microwave pulses are applied to an epitaxial overdoped La2− x Sr x CuO4 thin film in a perpendicular dc magnetic field. The onset of a significant variation in the sample magnetization which exists below threshold values of temperature, dc magnetic field, and pulse duration is interpreted as an avalanche-type flux penetration. The microwave contribution to the background electric field suggests that the nucleation of this fast vortex motion is of electric origin, which also guarantees the occurrence of vortex instabilities under adiabatic conditions via the enhancement of the flux flow resistivity. Flux creep phenomena and heat transfer effects act as stabilizing factors against the microwave-pulse-induced fast flux diffusion.

Hot spots for diversity of Magnaporthe oryzae physiological races in irrigated rice fields in Brazil

The objective of this work was to evaluate the Magnaporthe oryzae pathotype diversity in new commercial irrigated rice fields in the Araguaia River Valley, state of Tocantins, Brazil. The causal agent of rice blast has heavily affected rice production in the region. Despite the efforts of breeding programs, blast resistance breakdown has been recorded shortly after the release of new resistant cultivars developed for the region. Among the causes of resistance breakage is the capacity of the fungus to rapidly develop new pathotypes. A sample of 479 M. oryzae monosporic isolates was obtained and tested using the international rice blast differential set. Isolate collections were made in small areas designed as trap nurseries and in scattered sites in their vicinity. Analysis of 250 M. oryzae isolates from three trap nurseries indicated the presence of 45 international M. oryzae races belonging to seven pathotype groups (IA-IG). In the isolates tested, 61 M. oryzae pathotypes belonging to all but the IH group were detected. The new areas of irrigated rice in the Araguaia River Valley have the highest diversity of M. oryzae pathotypes reported so far in Brazil.

Minimization of Nyquist ghosting for echo-planar imaging at ultra-high fields based on a "negative readout gradient" strategy.

PURPOSE: To improve the traditional Nyquist ghost correction approach in echo planar imaging (EPI) at high fields, via schemes based on the reversal of the EPI readout gradient polarity for every other volume throughout a functional magnetic resonance imaging (fMRI) acquisition train. MATERIALS AND METHODS: An EPI sequence in which the readout gradient was inverted every other volume was implemented on two ultrahigh-field systems. Phantom images and fMRI data were acquired to evaluate ghost intensities and the presence of false-positive blood oxygenation level-dependent (BOLD) signal with and without ghost correction. Three different algorithms for ghost correction of alternating readout EPI were compared. RESULTS: Irrespective of the chosen processing approach, ghosting was significantly reduced (up to 70% lower intensity) in both rat brain images acquired on a 9.4T animal scanner and human brain images acquired at 7T, resulting in a reduction of sources of false-positive activation in fMRI data. CONCLUSION: It is concluded that at high B(0) fields, substantial gains in Nyquist ghost correction of echo planar time series are possible by alternating the readout gradient every other volume.

On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

In vivo Structural Imaging of the Cerebellum, the Contribution of Ultra-High Fields.

This review covers some of the contributions to date from cerebellar imaging studies performed at ultra-high magnetic fields. A short overview of the general advantages and drawbacks of the use of such high field systems for imaging is given. One of the biggest advantages of imaging at high magnetic fields is the improved spatial resolution, achievable thanks to the increased available signal-to-noise ratio. This high spatial resolution better matches the dimensions of the cerebellar substructures, allowing a better definition of such structures in the images. The implications of the use of high field systems is discussed for several imaging sequences and image contrast mechanisms. This review covers studies which were performed in vivo in both rodents and humans, with a special focus on studies that were directed towards the observation of the different cerebellar layers.