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 $R_V$, the ratio of total extinction in $V$ band to selective extinction in $B$ & $V$ 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 $R_V$ 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 $R_V$ 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 $10^4$ 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