18,824 research outputs found
Blur resolved OCT: full-range interferometric synthetic aperture microscopy through dispersion encoding
We present a computational method for full-range interferometric synthetic
aperture microscopy (ISAM) under dispersion encoding. With this, one can
effectively double the depth range of optical coherence tomography (OCT),
whilst dramatically enhancing the spatial resolution away from the focal plane.
To this end, we propose a model-based iterative reconstruction (MBIR) method,
where ISAM is directly considered in an optimization approach, and we make the
discovery that sparsity promoting regularization effectively recovers the
full-range signal. Within this work, we adopt an optimal nonuniform discrete
fast Fourier transform (NUFFT) implementation of ISAM, which is both fast and
numerically stable throughout iterations. We validate our method with several
complex samples, scanned with a commercial SD-OCT system with no hardware
modification. With this, we both demonstrate full-range ISAM imaging, and
significantly outperform combinations of existing methods.Comment: 17 pages, 7 figures. The images have been compressed for arxiv -
please follow DOI for full resolutio
Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences
Results: We present an application that enables the quantitative analysis of
multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence
microscopy images. The image sequences show stem cells together with blood
vessels, enabling quantification of the dynamic behaviors of stem cells in
relation to their vascular niche, with applications in developmental and cancer
biology. Our application automatically segments, tracks, and lineages the image
sequence data and then allows the user to view and edit the results of
automated algorithms in a stereoscopic 3-D window while simultaneously viewing
the stem cell lineage tree in a 2-D window. Using the GPU to store and render
the image sequence data enables a hybrid computational approach. An
inference-based approach utilizing user-provided edits to automatically correct
related mistakes executes interactively on the system CPU while the GPU handles
3-D visualization tasks. Conclusions: By exploiting commodity computer gaming
hardware, we have developed an application that can be run in the laboratory to
facilitate rapid iteration through biological experiments. There is a pressing
need for visualization and analysis tools for 5-D live cell image data. We
combine accurate unsupervised processes with an intuitive visualization of the
results. Our validation interface allows for each data set to be corrected to
100% accuracy, ensuring that downstream data analysis is accurate and
verifiable. Our tool is the first to combine all of these aspects, leveraging
the synergies obtained by utilizing validation information from stereo
visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
SPIRE Map-Making Test Report
The photometer section of SPIRE is one of the key instruments on board of
Herschel. Its legacy depends very much on how well the scanmap observations
that it carried out during the Herschel mission can be converted to high
quality maps. In order to have a comprehensive assessment on the current status
of SPIRE map-making, as well as to provide guidance for future development of
the SPIRE scan-map data reduction pipeline, we carried out a test campaign on
SPIRE map-making. In this report, we present results of the tests in this
campaign.Comment: This document has an executive summary, 6 chapters, and 102 pages.
More information can be found at:
https://nhscsci.ipac.caltech.edu/sc/index.php/Spire/SPIREMap-MakingTest201
A multi-scale, multi-wavelength source extraction method: getsources
We present a multi-scale, multi-wavelength source extraction algorithm called
getsources. Although it has been designed primarily for use in the far-infrared
surveys of Galactic star-forming regions with Herschel, the method can be
applied to many other astronomical images. Instead of the traditional approach
of extracting sources in the observed images, the new method analyzes fine
spatial decompositions of original images across a wide range of scales and
across all wavebands. It cleans those single-scale images of noise and
background, and constructs wavelength-independent single-scale detection images
that preserve information in both spatial and wavelength dimensions. Sources
are detected in the combined detection images by following the evolution of
their segmentation masks across all spatial scales. Measurements of the source
properties are done in the original background-subtracted images at each
wavelength; the background is estimated by interpolation under the source
footprints and overlapping sources are deblended in an iterative procedure. In
addition to the main catalog of sources, various catalogs and images are
produced that aid scientific exploitation of the extraction results. We
illustrate the performance of getsources on Herschel images by extracting
sources in sub-fields of the Aquila and Rosette star-forming regions. The
source extraction code and validation images with a reference extraction
catalog are freely available.Comment: 31 pages, 27 figures, to be published in Astronomy & Astrophysic
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
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