57,266 research outputs found
Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors
Segmentation of biomedical images is essential for studying and
characterizing anatomical structures, detection and evaluation of pathological
tissues. Segmentation has been further shown to enhance the reconstruction
performance in many tomographic imaging modalities by accounting for
heterogeneities of the excitation field and tissue properties in the imaged
region. This is particularly relevant in optoacoustic tomography, where
discontinuities in the optical and acoustic tissue properties, if not properly
accounted for, may result in deterioration of the imaging performance.
Efficient segmentation of optoacoustic images is often hampered by the
relatively low intrinsic contrast of large anatomical structures, which is
further impaired by the limited angular coverage of some commonly employed
tomographic imaging configurations. Herein, we analyze the performance of
active contour models for boundary segmentation in cross-sectional optoacoustic
tomography. The segmented mask is employed to construct a two compartment model
for the acoustic and optical parameters of the imaged tissues, which is
subsequently used to improve accuracy of the image reconstruction routines. The
performance of the suggested segmentation and modeling approach are showcased
in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin
Fast human detection for video event recognition
Human body detection, which has become a research hotspot during the last two years, can be used in many video content analysis applications. This paper investigates a fast human detection method for volume based video event detection. Compared with other object detection systems, human body detection brings more challenge due to threshold problems coming from a wide range of dynamic properties. Motivated by approaches successfully introduced in facial recognition applications, it adapts and adopts feature extraction and machine learning mechanism to classify certain areas from video frames. This method starts from the extraction of Haar-like features from large numbers of sample images for well-regulated feature distribution and is followed by AdaBoost learning and detection algorithm for pattern classification. Experiment on the classifier proves the Haar-like feature based machine learning mechanism can provide a fast and steady result for human body detection and can be further applied to reduce negative aspects in human modelling and analysis for volume based event detection
The MUSE-Wide Survey: Survey Description and First Data Release
We present the MUSE-Wide survey, a blind, 3D spectroscopic survey in the
CANDELS/GOODS-S and CANDELS/COSMOS regions. Each MUSE-Wide pointing has a depth
of 1 hour and hence targets more extreme and more luminous objects over 10
times the area of the MUSE-Deep fields (Bacon et al. 2017). The legacy value of
MUSE-Wide lies in providing "spectroscopy of everything" without photometric
pre-selection. We describe the data reduction, post-processing and PSF
characterization of the first 44 CANDELS/GOODS-S MUSE-Wide pointings released
with this publication. Using a 3D matched filtering approach we detected 1,602
emission line sources, including 479 Lyman- (Lya) emitting galaxies
with redshifts . We cross-match the emission line
sources to existing photometric catalogs, finding almost complete agreement in
redshifts and stellar masses for our low redshift (z < 1.5) emitters. At high
redshift, we only find ~55% matches to photometric catalogs. We encounter a
higher outlier rate and a systematic offset of z0.2 when
comparing our MUSE redshifts with photometric redshifts. Cross-matching the
emission line sources with X-ray catalogs from the Chandra Deep Field South, we
find 127 matches, including 10 objects with no prior spectroscopic
identification. Stacking X-ray images centered on our Lya emitters yielded no
signal; the Lya population is not dominated by even low luminosity AGN. A total
of 9,205 photometrically selected objects from the CANDELS survey lie in the
MUSE-Wide footprint, which we provide optimally extracted 1D spectra of. We are
able to determine the spectroscopic redshift of 98% of 772 photometrically
selected galaxies brighter than 24th F775W magnitude. All the data in the first
data release - datacubes, catalogs, extracted spectra, maps - are available on
the website https://musewide.aip.de. [abridged]Comment: 25 pages 15+1 figures. Accepted, A&A. Comments welcom
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