6,419 research outputs found
Region-based representations of image and video: segmentation tools for multimedia services
This paper discusses region-based representations of image and video that are useful for multimedia services such as those supported by the MPEG-4 and MPEG-7 standards. Classical tools related to the generation of the region-based representations are discussed. After a description of the main processing steps and the corresponding choices in terms of feature spaces, decision spaces, and decision algorithms, the state of the art in segmentation is reviewed. Mainly tools useful in the context of the MPEG-4 and MPEG-7 standards are discussed. The review is structured around the strategies used by the algorithms (transition based or homogeneity based) and the decision spaces (spatial, spatio-temporal, and temporal). The second part of this paper proposes a partition tree representation of images and introduces a processing strategy that involves a similarity estimation step followed by a partition creation step. This strategy tries to find a compromise between what can be done in a systematic and universal way and what has to be application dependent. It is shown in particular how a single partition tree created with an extremely simple similarity feature can support a large number of segmentation applications: spatial segmentation, motion estimation, region-based coding, semantic object extraction, and region-based retrieval.Peer ReviewedPostprint (published version
AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks
Segmentation of axon and myelin from microscopy images of the nervous system
provides useful quantitative information about the tissue microstructure, such
as axon density and myelin thickness. This could be used for instance to
document cell morphometry across species, or to validate novel non-invasive
quantitative magnetic resonance imaging techniques. Most currently-available
segmentation algorithms are based on standard image processing and usually
require multiple processing steps and/or parameter tuning by the user to adapt
to different modalities. Moreover, only few methods are publicly available. We
introduce AxonDeepSeg, an open-source software that performs axon and myelin
segmentation of microscopic images using deep learning. AxonDeepSeg features:
(i) a convolutional neural network architecture; (ii) an easy training
procedure to generate new models based on manually-labelled data and (iii) two
ready-to-use models trained from scanning electron microscopy (SEM) and
transmission electron microscopy (TEM). Results show high pixel-wise accuracy
across various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and
84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed
and morphological metrics are extracted and compared against the literature.
AxonDeepSeg is freely available at https://github.com/neuropoly/axondeepsegComment: 14 pages, 7 figure
Fast connected component labeling algorithm: a non voxel-based approach
This paper presents a new approach to achieve connected component labeling on both binary images and volumes by using the Extreme Vertices Model (EVM), a representation model for orthogonal
polyhedra, applied to digital images and volume datasets recently. In contrast with previous techniques, this method does not use a voxel-based approach but deals with the inner sections of the object.Postprint (published version
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis
Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis.
Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field
Innovations in the Analysis of Chandra-ACIS Observations
As members of the instrument team for the Advanced CCD Imaging Spectrometer
(ACIS) on NASA's Chandra X-ray Observatory and as Chandra General Observers, we
have developed a wide variety of data analysis methods that we believe are
useful to the Chandra community, and have constructed a significant body of
publicly-available software (the ACIS Extract package) addressing important
ACIS data and science analysis tasks. This paper seeks to describe these data
analysis methods for two purposes: to document the data analysis work performed
in our own science projects, and to help other ACIS observers judge whether
these methods may be useful in their own projects (regardless of what tools and
procedures they choose to implement those methods).
The ACIS data analysis recommendations we offer here address much of the
workflow in a typical ACIS project, including data preparation, point source
detection via both wavelet decomposition and image reconstruction, masking
point sources, identification of diffuse structures, event extraction for both
point and diffuse sources, merging extractions from multiple observations,
nonparametric broad-band photometry, analysis of low-count spectra, and
automation of these tasks. Many of the innovations presented here arise from
several, often interwoven, complications that are found in many Chandra
projects: large numbers of point sources (hundreds to several thousand), faint
point sources, misaligned multiple observations of an astronomical field, point
source crowding, and scientifically relevant diffuse emission.Comment: Accepted by the ApJ, 2010 Mar 10 (\#343576) 39 pages, 16 figure
PINGS: the PPAK IFS Nearby Galaxies Survey
We present the PPAK Integral Field Spectroscopy (IFS) Nearby Galaxies Survey:
PINGS, a 2-dimensional spectroscopic mosaicking of 17 nearby disk galaxies in
the optical wavelength range. This project represents the first attempt to
obtain continuous coverage spectra of the whole surface of a galaxy in the
nearby universe. The final data set comprises more than 50000 individual
spectra, covering in total an observed area of nearly 80 arcmin^2. In this
paper we describe the main astrophysical issues to be addressed by the PINGS
project, we present the galaxy sample and explain the observing strategy, the
data reduction process and all uncertainties involved. Additionally, we give
some scientific highlights extracted from the first analysis of the PINGS
sample.Comment: Accepted for publication in MNRAS, 26 pages, 14 figures (some in low
resolution), 3 table
The effects of spatial resolution on Integral Field Spectrograph surveys at different redshifts. The CALIFA perspective
Over the past decade, 3D optical spectroscopy has become the preferred tool
for understanding the properties of galaxies and is now increasingly used to
carry out galaxy surveys. Low redshift surveys include SAURON, DiskMass,
ATLAS3D, PINGS and VENGA. At redshifts above 0.7, surveys such as MASSIV, SINS,
GLACE, and IMAGES have targeted the most luminous galaxies to study mainly
their kinematic properties. The on-going CALIFA survey () is the
first of a series of upcoming Integral Field Spectroscopy (IFS) surveys with
large samples representative of the entire population of galaxies. Others
include SAMI and MaNGA at lower redshift and the upcoming KMOS surveys at
higher redshift. Given the importance of spatial scales in IFS surveys, the
study of the effects of spatial resolution on the recovered parameters becomes
important. We explore the capability of the CALIFA survey and a hypothetical
higher redshift survey to reproduce the properties of a sample of objects
observed with better spatial resolution at lower redshift. Using a sample of
PINGS galaxies, we simulate observations at different redshifts. We then study
the behaviour of different parameters as the spatial resolution degrades with
increasing redshift.Comment: 20 pages, 16 figures. Accepted for publication in A&
Calipso: Physics-based Image and Video Editing through CAD Model Proxies
We present Calipso, an interactive method for editing images and videos in a
physically-coherent manner. Our main idea is to realize physics-based
manipulations by running a full physics simulation on proxy geometries given by
non-rigidly aligned CAD models. Running these simulations allows us to apply
new, unseen forces to move or deform selected objects, change physical
parameters such as mass or elasticity, or even add entire new objects that
interact with the rest of the underlying scene. In Calipso, the user makes
edits directly in 3D; these edits are processed by the simulation and then
transfered to the target 2D content using shape-to-image correspondences in a
photo-realistic rendering process. To align the CAD models, we introduce an
efficient CAD-to-image alignment procedure that jointly minimizes for rigid and
non-rigid alignment while preserving the high-level structure of the input
shape. Moreover, the user can choose to exploit image flow to estimate scene
motion, producing coherent physical behavior with ambient dynamics. We
demonstrate Calipso's physics-based editing on a wide range of examples
producing myriad physical behavior while preserving geometric and visual
consistency.Comment: 11 page
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