34,201 research outputs found
Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data
The accurate characterization of the diffusion process in tissue using diffusion MRI is greatly challenged by the presence of artefacts. Subject motion causes not only spatial misalignments between diffusion weighted images, but often also slicewise signal intensity errors. Voxelwise robust model estimation is commonly used to exclude intensity errors as outliers. Slicewise outliers, however, become distributed over multiple adjacent slices after image registration and transformation. This challenges outlier detection with voxelwise procedures due to partial volume effects. Detecting the outlier slices before any transformations are applied to diffusion weighted images is therefore required. In this work, we present i) an automated tool coined SOLID for slicewise outlier detection prior to geometrical image transformation, and ii) a framework to naturally interpret data uncertainty information from SOLID and include it as such in model estimators. SOLID uses a straightforward intensity metric, is independent of the choice of the diffusion MRI model, and can handle datasets with a few or irregularly distributed gradient directions. The SOLID-informed estimation framework prevents the need to completely reject diffusion weighted images or individual voxel measurements by downweighting measurements with their degree of uncertainty, thereby supporting convergence and well-conditioning of iterative estimation algorithms. In comprehensive simulation experiments, SOLID detects outliers with a high sensitivity and specificity, and can achieve higher or at least similar sensitivity and specificity compared to other tools that are based on more complex and time-consuming procedures for the scenarios investigated. SOLID was further validated on data from 54 neonatal subjects which were visually inspected for outlier slices with the interactive tool developed as part of this study, showing its potential to quickly highlight problematic volumes and slices in large population studies. The informed model estimation framework was evaluated both in simulations and in vivo human data.Peer reviewe
Implementation of robust image artifact removal in SWarp through clipped mean stacking
We implement an algorithm for detecting and removing artifacts from
astronomical images by means of outlier rejection during stacking. Our method
is capable of addressing both small, highly significant artifacts such as
cosmic rays and, by applying a filtering technique to generate single frame
masks, larger area but lower surface brightness features such as secondary
(ghost) images of bright stars. In contrast to the common method of building a
median stack, the clipped or outlier-filtered mean stacked point-spread
function (PSF) is a linear combination of the single frame PSFs as long as the
latter are moderately homogeneous, a property of great importance for weak
lensing shape measurement or model fitting photometry. In addition, it has
superior noise properties, allowing a significant reduction in exposure time
compared to median stacking. We make publicly available a modified version of
SWarp that implements clipped mean stacking and software to generate single
frame masks from the list of outlier pixels.Comment: PASP accepted; software for download at
http://www.usm.uni-muenchen.de/~dgruen
Unsupervised routine discovery in egocentric photo-streams
The routine of a person is defined by the occurrence of activities throughout
different days, and can directly affect the person's health. In this work, we
address the recognition of routine related days. To do so, we rely on
egocentric images, which are recorded by a wearable camera and allow to monitor
the life of the user from a first-person view perspective. We propose an
unsupervised model that identifies routine related days, following an outlier
detection approach. We test the proposed framework over a total of 72 days in
the form of photo-streams covering around 2 weeks of the life of 5 different
camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted
F-Score for all the users. Thus, we show that our framework is able to
recognise routine related days and opens the door to the understanding of the
behaviour of people
Identification of Outlying Observations with Quantile Regression for Censored Data
Outlying observations, which significantly deviate from other measurements,
may distort the conclusions of data analysis. Therefore, identifying outliers
is one of the important problems that should be solved to obtain reliable
results. While there are many statistical outlier detection algorithms and
software programs for uncensored data, few are available for censored data. In
this article, we propose three outlier detection algorithms based on censored
quantile regression, two of which are modified versions of existing algorithms
for uncensored or censored data, while the third is a newly developed algorithm
to overcome the demerits of previous approaches. The performance of the three
algorithms was investigated in simulation studies. In addition, real data from
SEER database, which contains a variety of data sets related to various
cancers, is illustrated to show the usefulness of our methodology. The
algorithms are implemented into an R package OutlierDC which can be
conveniently employed in the \proglang{R} environment and freely obtained from
CRAN
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