9,944 research outputs found
3D time series analysis of cell shape using Laplacian approaches
Background:
Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes.
Results:
We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells.
Conclusions:
The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations
Fast and robust 3D feature extraction from sparse point clouds
Matching 3D point clouds, a critical operation in map building and localization, is difficult with Velodyne-type sensors due to the sparse and non-uniform point clouds that they produce. Standard methods from dense 3D point clouds are generally not effective. In this paper, we describe a featurebased approach using Principal Components Analysis (PCA) of neighborhoods of points, which results in mathematically principled line and plane features. The key contribution in this work is to show how this type of feature extraction can be done efficiently and robustly even on non-uniformly sampled point clouds. The resulting detector runs in real-time and can be easily tuned to have a low false positive rate, simplifying data association. We evaluate the performance of our algorithm on an autonomous car at the MCity Test Facility using a Velodyne HDL-32E, and we compare our results against the state-of-theart NARF keypoint detector. © 2016 IEEE
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Astigmatism and Pseudoaccommodation in Pseudophakic Eyes
noAdvanced IOLs with circumferential zones of different power provide pseudoaccommodation. We investigated the potential for power variation with meridian, namely astigmatism, to provide pseudo-accommodation. With appropriate power and axis orientations, acceptable pseudo-accommodation can be achieved
Planetary nebulae and stellar kinematics in the flattened elliptical galaxy NGC 1344
We present photometric and kinematic information obtained by measuring 197
planetary nebulae (PNs) discovered in the flattened Fornax elliptical galaxy
NGC 1344 (also known as NGC 1340) with an on-band, off-band, grism + on-band
filter technique. We build the PN luminosity function (PNLF) and use it to
derive a distance modulus m-M=31.4, slightly smaller than, but in good
agreement with, the surface brightness fluctuation distance. The PNLF also
provides an estimate of the specific PN formation rate: 6x10^-12 PNs per year
per solar luminosity. Combining the positional information from the on-band
image with PN positions measured on the grism + on-band image, we can measure
the radial velocities of 195 PNs, some of them distant more than 3 effective
radii from the center of NGC 1344. We complement this data set with stellar
kinematics derived from integrated spectra along the major and minor axes, and
parallel to the major axis of NGC 1344. The line-of-sight velocity dispersion
profile indicates the presence of a dark matter halo around this galaxy.Comment: 45 pages, 18 figures, accepted for publication in Ap
The Three-Dimensional Circumstellar Environment of SN 1987A
We present the detailed construction and analysis of the most complete map to
date of the circumstellar environment around SN 1987A, using ground and
space-based imaging from the past 16 years. PSF-matched difference-imaging
analyses of data from 1988 through 1997 reveal material between 1 and 28 ly
from the SN. Careful analyses allows the reconstruction of the probable
circumstellar environment, revealing a richly-structured bipolar nebula. An
outer, double-lobed ``Peanut,'' which is believed to be the contact
discontinuity between red supergiant and main sequence winds, is a prolate
shell extending 28 ly along the poles and 11 ly near the equator. Napoleon's
Hat, previously believed to be an independent structure, is the waist of this
Peanut, which is pinched to a radius of 6 ly. Interior to this is a cylindrical
hourglass, 1 ly in radius and 4 ly long, which connects to the Peanut by a
thick equatorial disk. The nebulae are inclined 41\degr south and 8\degr east
of the line of sight, slightly elliptical in cross section, and marginally
offset west of the SN. From the hourglass to the large, bipolar lobes, echo
fluxes suggest that the gas density drops from 1--3 cm^{-3} to >0.03 cm^{-3},
while the maximum dust-grain size increases from ~0.2 micron to 2 micron, and
the Si:C dust ratio decreases. The nebulae have a total mass of ~1.7 Msun. The
geometry of the three rings is studied, suggesting the northern and southern
rings are located 1.3 and 1.0 ly from the SN, while the equatorial ring is
elliptical (b/a < 0.98), and spatially offset in the same direction as the
hourglass.Comment: Accepted for publication in the ApJ Supplements. 38 pages in
apjemulate format, with 52 figure
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
Toward an object-based semantic memory for long-term operation of mobile service robots
Throughout a lifetime of operation, a mobile service robot needs to acquire, store and update its knowledge of a working environment. This includes the ability to identify and track objects in different places, as well as using this information for interaction with humans. This paper introduces a long-term updating mechanism, inspired by the modal model of human memory, to enable a mobile robot to maintain its knowledge of a changing environment. The memory model is integrated with a hybrid map that represents the global topology and local geometry of the environment, as well as the respective 3D location of objects. We aim to enable the robot to use this knowledge to help humans by suggesting the most likely locations of specific objects in its map. An experiment using omni-directional vision demonstrates the ability to track the movements of several objects in a dynamic environment over an extended period of time
The Southampton-York Natural Scenes (SYNS) dataset: statistics of surface attitude
Recovering 3D scenes from 2D images is an under-constrained task; optimal estimation depends upon knowledge of the underlying scene statistics. Here we introduce the Southampton-York Natural Scenes dataset (SYNS: https://syns.soton.ac.uk), which provides comprehensive scene statistics useful for understanding biological vision and for improving machine vision systems. In order to capture the diversity of environments that humans encounter, scenes were surveyed at random locations within 25 indoor and outdoor categories. Each survey includes (i) spherical LiDAR range data (ii) high-dynamic range spherical imagery and (iii) a panorama of stereo image pairs. We envisage many uses for the dataset and present one example: an analysis of surface attitude statistics, conditioned on scene category and viewing elevation. Surface normals were estimated using a novel adaptive scale selection algorithm. Across categories, surface attitude below the horizon is dominated by the ground plane (0° tilt). Near the horizon, probability density is elevated at 90°/270° tilt due to vertical surfaces (trees, walls). Above the horizon, probability density is elevated near 0° slant due to overhead structure such as ceilings and leaf canopies. These structural regularities represent potentially useful prior assumptions for human and machine observers, and may predict human biases in perceived surface attitude
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