8,668 research outputs found
Characterizing neuromorphologic alterations with additive shape functionals
The complexity of a neuronal cell shape is known to be related to its
function. Specifically, among other indicators, a decreased complexity in the
dendritic trees of cortical pyramidal neurons has been associated with mental
retardation. In this paper we develop a procedure to address the
characterization of morphological changes induced in cultured neurons by
over-expressing a gene involved in mental retardation. Measures associated with
the multiscale connectivity, an additive image functional, are found to give a
reasonable separation criterion between two categories of cells. One category
consists of a control group and two transfected groups of neurons, and the
other, a class of cat ganglionary cells. The reported framework also identified
a trend towards lower complexity in one of the transfected groups. Such results
establish the suggested measures as an effective descriptors of cell shape
Combining multiple resolutions into hierarchical representations for kernel-based image classification
Geographic object-based image analysis (GEOBIA) framework has gained
increasing interest recently. Following this popular paradigm, we propose a
novel multiscale classification approach operating on a hierarchical image
representation built from two images at different resolutions. They capture the
same scene with different sensors and are naturally fused together through the
hierarchical representation, where coarser levels are built from a Low Spatial
Resolution (LSR) or Medium Spatial Resolution (MSR) image while finer levels
are generated from a High Spatial Resolution (HSR) or Very High Spatial
Resolution (VHSR) image. Such a representation allows one to benefit from the
context information thanks to the coarser levels, and subregions spatial
arrangement information thanks to the finer levels. Two dedicated structured
kernels are then used to perform machine learning directly on the constructed
hierarchical representation. This strategy overcomes the limits of conventional
GEOBIA classification procedures that can handle only one or very few
pre-selected scales. Experiments run on an urban classification task show that
the proposed approach can highly improve the classification accuracy w.r.t.
conventional approaches working on a single scale.Comment: International Conference on Geographic Object-Based Image Analysis
(GEOBIA 2016), University of Twente in Enschede, The Netherland
Multiscale Phenomenology of the Cosmic Web
We analyze the structure and connectivity of the distinct morphologies that
define the Cosmic Web. With the help of our Multiscale Morphology Filter (MMF),
we dissect the matter distribution of a cosmological CDM N-body
computer simulation into cluster, filaments and walls. The MMF is ideally
suited to adress both the anisotropic morphological character of filaments and
sheets, as well as the multiscale nature of the hierarchically evolved cosmic
matter distribution. The results of our study may be summarized as follows:
i).- While all morphologies occupy a roughly well defined range in density,
this alone is not sufficient to differentiate between them given their overlap.
Environment defined only in terms of density fails to incorporate the intrinsic
dynamics of each morphology. This plays an important role in both linear and
non linear interactions between haloes. ii).- Most of the mass in the Universe
is concentrated in filaments, narrowly followed by clusters. In terms of
volume, clusters only represent a minute fraction, and filaments not more than
9%. Walls are relatively inconspicous in terms of mass and volume. iii).- On
average, massive clusters are connected to more filaments than low mass
clusters. Clusters with M h have on average
two connecting filaments, while clusters with M
h have on average five connecting filaments. iv).- Density profiles
indicate that the typical width of filaments is 2\Mpch. Walls have less well
defined boundaries with widths between 5-8 Mpc h. In their interior,
filaments have a power-law density profile with slope ,
corresponding to an isothermal density profile.Comment: 28 pages, 22 figures, accepted for publication in MNRAS. For a
high-res version see http://www.astro.rug.nl/~weygaert/webmorph_mmf.pd
Learned versus Hand-Designed Feature Representations for 3d Agglomeration
For image recognition and labeling tasks, recent results suggest that machine
learning methods that rely on manually specified feature representations may be
outperformed by methods that automatically derive feature representations based
on the data. Yet for problems that involve analysis of 3d objects, such as mesh
segmentation, shape retrieval, or neuron fragment agglomeration, there remains
a strong reliance on hand-designed feature descriptors. In this paper, we
evaluate a large set of hand-designed 3d feature descriptors alongside features
learned from the raw data using both end-to-end and unsupervised learning
techniques, in the context of agglomeration of 3d neuron fragments. By
combining unsupervised learning techniques with a novel dynamic pooling scheme,
we show how pure learning-based methods are for the first time competitive with
hand-designed 3d shape descriptors. We investigate data augmentation strategies
for dramatically increasing the size of the training set, and show how
combining both learned and hand-designed features leads to the highest
accuracy
Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy
Neural circuits can be reconstructed from brain images acquired by serial
section electron microscopy. Image analysis has been performed by manual labor
for half a century, and efforts at automation date back almost as far.
Convolutional nets were first applied to neuronal boundary detection a dozen
years ago, and have now achieved impressive accuracy on clean images. Robust
handling of image defects is a major outstanding challenge. Convolutional nets
are also being employed for other tasks in neural circuit reconstruction:
finding synapses and identifying synaptic partners, extending or pruning
neuronal reconstructions, and aligning serial section images to create a 3D
image stack. Computational systems are being engineered to handle petavoxel
images of cubic millimeter brain volumes
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