11,264 research outputs found
Segmentation of skin lesions in 2D and 3D ultrasound images using a spatially coherent generalized Rayleigh mixture model
This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images
Basic research planning in mathematical pattern recognition and image analysis
Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis
CORRECTING FOR SPATIAL EFFECTS IN LIMITED DEPENDENT VARIABLE REGRESSION: ASSESSING THE VALUE OF "AD-HOC" TECHNIQUES
A common test for spatial dependence in regression analysis with continuous dependent variables is the Moran's I. For limited dependent variable models, the standard definition of a residual breaks down because yi is qualitative. Efforts to correct for potential spatial effects in limited dependent variable models have relied on ad-hoc methods such as including a spatial lag variable or using a regular sample that omits neighboring observations. Kelejian and Prucha have recently developed a version of Moran's I for limited dependent variable models. We present the statistic in a more accessible way and use it to test the value of previously-used ad-hoc techniques with a specific data set. Keywords: Morans I, Spatial Autocorrelation, Limited Dependent Variable Models, Land-Use Change, Geographical Information Systems (GIS),Moran's I, Spatial Autocorrelation, Limited Dependent Variable Models, Land-Use Change, Geographical Information Systems (GIS), Research Methods/ Statistical Methods,
Evidence for self-interaction of charge distribution in charge-coupled devices
Charge-coupled devices (CCDs) are widely used in astronomy to carry out a
variety of measurements, such as for flux or shape of astrophysical objects.
The data reduction procedures almost always assume that ther esponse of a given
pixel to illumination is independent of the content of the neighboring pixels.
We show evidence that this simple picture is not exact for several CCD sensors.
Namely, we provide evidence that localized distributions of charges (resulting
from star illumination or laboratory luminous spots) tend to broaden linearly
with increasing brightness by up to a few percent over the whole dynamic range.
We propose a physical explanation for this "brighter-fatter" effect, which
implies that flatfields do not exactly follow Poisson statistics: the variance
of flatfields grows less rapidly than their average, and neighboring pixels
show covariances, which increase similarly to the square of the flatfield
average. These covariances decay rapidly with pixel separation. We observe the
expected departure from Poisson statistics of flatfields on CCD devices and
show that the observed effects are compatible with Coulomb forces induced by
stored charges that deflect forthcoming charges. We extract the strength of the
deflections from the correlations of flatfield images and derive the evolution
of star shapes with increasing flux. We show for three types of sensors that
within statistical uncertainties,our proposed method properly bridges
statistical properties of flatfields and the brighter-fatter effect
Efficient transfer entropy analysis of non-stationary neural time series
Information theory allows us to investigate information processing in neural
systems in terms of information transfer, storage and modification. Especially
the measure of information transfer, transfer entropy, has seen a dramatic
surge of interest in neuroscience. Estimating transfer entropy from two
processes requires the observation of multiple realizations of these processes
to estimate associated probability density functions. To obtain these
observations, available estimators assume stationarity of processes to allow
pooling of observations over time. This assumption however, is a major obstacle
to the application of these estimators in neuroscience as observed processes
are often non-stationary. As a solution, Gomez-Herrero and colleagues
theoretically showed that the stationarity assumption may be avoided by
estimating transfer entropy from an ensemble of realizations. Such an ensemble
is often readily available in neuroscience experiments in the form of
experimental trials. Thus, in this work we combine the ensemble method with a
recently proposed transfer entropy estimator to make transfer entropy
estimation applicable to non-stationary time series. We present an efficient
implementation of the approach that deals with the increased computational
demand of the ensemble method's practical application. In particular, we use a
massively parallel implementation for a graphics processing unit to handle the
computationally most heavy aspects of the ensemble method. We test the
performance and robustness of our implementation on data from simulated
stochastic processes and demonstrate the method's applicability to
magnetoencephalographic data. While we mainly evaluate the proposed method for
neuroscientific data, we expect it to be applicable in a variety of fields that
are concerned with the analysis of information transfer in complex biological,
social, and artificial systems.Comment: 27 pages, 7 figures, submitted to PLOS ON
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