27,884 research outputs found
A fully automatic CAD-CTC system based on curvature analysis for standard and low-dose CT data
Computed tomography colonography (CTC) is a rapidly evolving noninvasive medical investigation that is viewed by radiologists as a potential screening technique for the detection of colorectal polyps. Due to the technical advances in CT system design, the volume of data required to be processed by radiologists has increased significantly, and as a consequence the manual analysis of this information has become an increasingly time consuming process whose results can be affected by inter- and intrauser variability. The aim of this paper is to detail the implementation of a fully integrated CAD-CTC system that is able to robustly identify the clinically significant polyps in the CT data. The CAD-CTC system described in this paper is a multistage implementation whose main system components are: 1) automatic colon segmentation; 2) candidate surface extraction; 3) feature extraction; and 4) classification. Our CAD-CTC system performs at 100% sensitivity for polyps larger than 10 mm, 92% sensitivity for polyps in the range 5 to 10 mm, and 57.14% sensitivity for polyps smaller than 5 mm with an average of 3.38 false positives per dataset. The developed system has been evaluated on synthetic and real patient CT data acquired with standard and low-dose radiation levels
Spectral Mapping Reconstruction of Extended Sources
Three dimensional spectroscopy of extended sources is typically performed
with dedicated integral field spectrographs. We describe a method of
reconstructing full spectral cubes, with two spatial and one spectral
dimension, from rastered spectral mapping observations employing a single slit
in a traditional slit spectrograph. When the background and image
characteristics are stable, as is often achieved in space, the use of
traditional long slits for integral field spectroscopy can substantially reduce
instrument complexity over dedicated integral field designs, without loss of
mapping efficiency -- particularly compelling when a long slit mode for single
unresolved source followup is separately required. We detail a custom
flux-conserving cube reconstruction algorithm, discuss issues of extended
source flux calibration, and describe CUBISM, a tool which implements these
methods for spectral maps obtained with ther Spitzer Space Telescope's Infrared
Spectrograph.Comment: 11 pages, 8 figures, accepted by PAS
Optimality in self-organized molecular sorting
We introduce a simple physical picture to explain the process of molecular
sorting, whereby specific proteins are concentrated and distilled into
submicrometric lipid vesicles in eukaryotic cells. To this purpose, we
formulate a model based on the coupling of spontaneous molecular aggregation
with vesicle nucleation. Its implications are studied by means of a
phenomenological theory describing the diffusion of molecules towards multiple
sorting centers that grow due to molecule absorption and are extracted when
they reach a sufficiently large size. The predictions of the theory are
compared with numerical simulations of a lattice-gas realization of the model
and with experimental observations. The efficiency of the distillation process
is found to be optimal for intermediate aggregation rates, where the density of
sorted molecules is minimal and the process obeys simple scaling laws.
Quantitative measures of endocytic sorting performed in primary endothelial
cells are compatible with the hypothesis that these optimal conditions are
realized in living cells
Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data
Image data are increasingly encountered and are of growing importance in many
areas of science. Much of these data are quantitative image data, which are
characterized by intensities that represent some measurement of interest in the
scanned images. The data typically consist of multiple images on the same
domain and the goal of the research is to combine the quantitative information
across images to make inference about populations or interventions. In this
paper we present a unified analysis framework for the analysis of quantitative
image data using a Bayesian functional mixed model approach. This framework is
flexible enough to handle complex, irregular images with many local features,
and can model the simultaneous effects of multiple factors on the image
intensities and account for the correlation between images induced by the
design. We introduce a general isomorphic modeling approach to fitting the
functional mixed model, of which the wavelet-based functional mixed model is
one special case. With suitable modeling choices, this approach leads to
efficient calculations and can result in flexible modeling and adaptive
smoothing of the salient features in the data. The proposed method has the
following advantages: it can be run automatically, it produces inferential
plots indicating which regions of the image are associated with each factor, it
simultaneously considers the practical and statistical significance of
findings, and it controls the false discovery rate.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS407 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Automatic Image Based Time Varying 3D Feature Extraction and Tracking
3D time-varying data sets are complex. The intrinsics of those data cannot be readily comprehended by users solely based on visual investigation. Computational tools such as feature extraction and tracking are often necessary. Until now, most existing algorithms in this domain work effectively in the object space, relying on prior knowledge of the data. How to find a more flexible and efficient method which can perform automatically to implement extraction and tracking remains an attractive topic.
This thesis presents a new image-based method that extracts and tracks the 3D time- varying volume data sets. The innovation of the proposed approach is two-fold. First, all analyses are performed in the image space on volume rendered images without accessing the actual volume data itself. The image-based processing will help to both save storage space in the memory and reduce computation burden. Secondly, the new approach does not require any prior knowledge of the user-defined “feature” or a built model. All the parameters used by the algorithms are automatically determined by the system itself, thus flexibility and efficiency can be achieved at the same time.
The proposed image-based feature extraction and tracking system consists of four components: feature segmentation (or extraction), feature description (or shape analysis), classification, and feature tracking. Feature segmentation is to identify and label individual features from the image so that we can describe and track them separately. We combine both region-based and edge-based segmentation approaches to implement the extraction process. Feature description is to analyze each feature and derive a vector to describe the feature such that the subsequent tracking step does not have to rely on the entire feature extracted, but instead a much smaller and informative feature descriptor. Classification is to identify the corresponding features from two consecutive image frames along both the time and the spatial domain. Feature tracking is to study and model the evolution of features based on the correspondence computation result from classification stage. Experimental results show that the image-based feature extraction and tracking system provides high fidelity with great efficiency
HST and Spitzer Observations of the HD 207129 Debris Ring
A debris ring around the star HD 207129 (G0V; d = 16.0 pc) has been imaged in
scattered visible light with the ACS coronagraph on the Hubble Space Telescope
and in thermal emission using MIPS on the Spitzer Space Telescope at 70 microns
(resolved) and 160 microns (unresolved). Spitzer IRS (7-35 microns) and MIPS
(55-90 microns) spectrographs measured disk emission at >28 microns. In the HST
image the disk appears as a ~30 AU wide ring with a mean radius of ~163 AU and
is inclined by 60 degrees from pole-on. At 70 microns it appears partially
resolved and is elongated in the same direction and with nearly the same size
as seen with HST in scattered light. At 0.6 microns the ring shows no
significant brightness asymmetry, implying little or no forward scattering by
its constituent dust. With a mean surface brightness of V=23.7 mag per square
arcsec, it is the faintest disk imaged to date in scattered light.Comment: 28 pages, 8 figure
Arkansas Bulletin of Water Research - Issue 2018
The Arkansas Bulletin of Water Research is a publication of the Arkansas Water Resources Center (AWRC). This bulletin is produced in an effort to share water research relevant to Arkansas water stakeholders in an easily searchable and aesthetically engaging way. This is the second publication of the bulletin and will be published annually. The submission of a paper to this bulletin is appropriate for topics at all related to water resources, by anyone conducting water research or investigations. This includes but is not limited to university researchers, consulting firms, watershed groups, and other agencies. Prospective authors should read the “Introduction to the Arkanasas Bulletin of Water Research” contained within this publication and should refer to the AWRC website for additional infromation.
https://arkansas-water-center.uark.edu
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