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LSB Galaxies Detection Using
Markovian Segmentation on
Astronomical Images
Mireille Louys1, Benjamin Perret 1
Bernd Vollmer 2, François Bonnarel 2
Sebastien Lefèvre1, Christophe Collet1
1
Laboratoire des Sciences de l’Informatique , de l’Image et de la Télédétection
2
Centre de donnees astronomiques de Strasbourg,
Rationale
• Surface brightness is highly variable from one
galaxy to another (Disney, 1976)
• Low surface brightness galaxies are close to
the image background level and satisfy:
SB > 22.5 mag.arcsec-2
• Found inside and outside galaxy clusters
– How complete are our detections?
• More understanding in the baryonic fraction of
the Dark Matter in the universe
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Noise and background
Easy case
More difficult
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Overview
Original Image
Segmentation Map
Segmentation
Objects
list
Objects Profiles
Fitting
Selection
Visual
Inspection and
Validation
DetectLSB
Markovian Quadtree
3D mathematical
morphology
Ellipse parameters
Fit error
Magnitude profile LinLog, LinLin
Measured Surface brightness
Estimated Surface brightness
Scale length
VO Objects
list
VOTable
XML
Markovian segmentation
• The goal is to classify pixel values and discriminate
bright objects, sky background and faint sources to be
considered later as LSB candidates .
• It works as an inverse method :
– Try to find the Bayesian estimate of the most likely
class partition that maps the observation
– We make 2 assumptions :
• on the noise and inner class statistics (here gaussian),
• on the statistical phenomenon: Markovian quadtree.
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Markovian quadtree
•The succesive tree levels correspond
to increasing scales of the image.
•We want to compute the a posteriori
probability of having site s in one class
c, knowing the observation Ys and the
class number of the parent node within
the Markov tree.
•The Quadtree provides an in-scale
regularisation framework, providing
exact and non iterative solution
•It is fully applicable to multiband
observations
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Steps achieved with DetectLSB
• For each detection map:
– Identify pixel connected components
– Remove very extended components : > 300 pixels
– Remove components at the edges of the image
• Ellipse fitting
• Luminosity profile analysis
• Selection criteria:
– Sort out stars according to their profile (steep slope, etc.)
– Remove bad centered and/or overlaping objects (crowded
regions)
– Check for central surface brightness
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Luminosity profile analysis
• For each object in the detection map:
– Average profile along each ellipse radius
f(r)=U0*exp(-r/R0)
At least 3 points aligned above
background level
Source Measurements
Fitted
SB
Surface
Radius
Brightness
Profile Fit
Error
Data set
• B-images of a region of the Virgo cluster
obtained with the Isaac Newton Telescope
WFCS (Wild Field Camera Survey)
• Analysed by Sabina Sabatini et coll. in
SABATINI S.; DAVIES J.; SCARAMELLA R.; SMITH R.;
BAES M.; LINDER S.M.; ROBERTS S.; TESTA V. Mon. Not.
R. Astron. Soc., 341, 981-992 (2003) .
and provided to us as a test set
• Full data set = 80 images of 4096x2048 pixels
• Analysed: 18 images with X-match with the
detection lists provided by the authors
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Results on the INT data set
• Found 79 % of our LSB candidates are
confirmed by Sabatini et al.
23 (Markov+detectLSB) compared to 29 (Sabatini)
• Found new LSB candidates
• The validation on the full data set is still an
on-going collaboration effort with S. Sabatini
and W. van Driel.
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Conclusion
• The Markovian segmentation approach allows to
study LSB candidates even in a noisy environment
• Multiband image analysis is beeing currently applied
to the same data set in B band completed with I band
• First tests are promising
• Such a procedure has been implemented in the AIDA
image processing workflow project at CDS
(Cf Schaaff et al, this conference)
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2 detections
for the
same
object