1,130 research outputs found
Determining the extragalactic extinction law with SALT
We present CCD imaging observations of early-type galaxies with dark lanes
obtained with the Southern African Large Telescope (SALT) during its
performance-verification phase. We derive the extinction law by the
extragalactic dust in the dark lanes in the spectral range 1.11mu m^{-1} <
lambda^{-1} < 2.94 mu m^{-1} by fitting model galaxies to the unextinguished
parts of the image, and subtracting from these the actual images. We find that
the extinction curves run parallel to the Galactic extinction curve, which
implies that the properties of dust in the extragalactic enviroment are similar
to those of the Milky Way. The ratio of the total V band extinction to the
selective extinction between the V and B bands is derived for each galaxy with
an average of 2.82+-0.38, compared to a canonical value of 3.1 for the Milky
Way. The similar values imply that galaxies with well-defined dark lanes have
characteristic dust grain sizes similar to those of Galactic dust.Comment: 20 pages, 15 figures and 4 tables. Accepted for publication in MNRA
Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
A deep level set method for image segmentation
This paper proposes a novel image segmentation approachthat integrates fully
convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the
integrated method can incorporatesmoothing and prior information to achieve an
accurate segmentation.Furthermore, different than using the level set model as
a post-processingtool, we integrate it into the training phase to fine-tune the
FCN. Thisallows the use of unlabeled data during training in a
semi-supervisedsetting. Using two types of medical imaging data (liver CT and
left ven-tricle MRI data), we show that the integrated method achieves
goodperformance even when little training data is available, outperformingthe
FCN or the level set model alone
Properties of dust in early-type galaxies
We report optical extinction properties of dust for a sample of 26 early-type
galaxies based on the analysis of their multicolour CCD observations. The
wavelength dependence of dust extinction for these galaxies is determined and
the extinction curves are found to run parallel to the Galactic extinction
curve, which implies that the properties of dust in the extragalactic
environment are quite similar to those of the Milky Way. For the sample
galaxies, value of the parameter , the ratio of total extinction in
band to selective extinction in & bands, lies in the range 2.03 - 3.46
with an average of 3.02, compared to its canonical value of 3.1 for the Milky
Way. A dependence of on dust morphology of the host galaxy is also
noticed in the sense that galaxies with a well defined dust lane show tendency
to have smaller values compared to the galaxies with disturbed dust
morphology. The dust content of these galaxies estimated using total optical
extinction is found to lie in the range to 10^6 \rm M_{\sun}, an order
of magnitude smaller than those derived from IRAS flux densities, indicating
that a significant fraction of dust intermixed with stars remains undetected by
the optical method. We examine the relationship between dust mass derived from
IRAS flux and the X-ray luminosity of the host galaxies.The issue of the origin
of dust in early-type galaxies is also discussed.Comment: 12 pages, 6 figures. Accepted for publication in Astronomy &
Astrophysic
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