We present to the astronomical community an algorithm for the detection of Low Surface Brightness ( LSB ) galaxies in images , called MARSIAA ( MARkovian Software for Image Analysis in Astronomy ) , which is based on multi-scale Markovian modeling .
MARSIAA can be applied simultaneously to different bands .
It segments an image into a user-defined number of classes , according to their surface brightness and surroundings – typically , one or two classes contain the LSB structures .
We have developed an algorithm , called DetectLSB , which allows the efficient identification of LSB galaxies from among the candidate sources selected by MARSIAA .
The application of the method to two and three bands simultaneously was tested on simulated images .
Based on our tests we are confident that we can detect LSB galaxies down to a central surface brightness level of only 1.5 times the standard deviation from the mean pixel value in the image background .
To assess the robustness of our method , the method was applied to a set of 18 B and I band images ( covering 1.3 square degrees in total ) of the Virgo cluster to which Sabatini et al .
( 2003 , 2005 ) previously applied a matched-filters dwarf LSB galaxy search algorithm .
We have detected all 20 objects from the Sabatini et al .
catalog which we could classify by eye as bona fide LSB galaxies .
Our method has also detected 4 additional Virgo cluster LSB galaxy candidates undetected by Sabatini et al .
To further assess the completeness of the results of our method , both MARSIAA , SExtractor , and DetectLSB were applied to search for ( i ) mock Virgo LSB galaxies inserted into a set of deep Next Generation Virgo Survey ( NGVS ) gri-band subimages and ( ii ) Virgo LSB galaxies identified by eye in a full set of NGVS square degree gri images .
MARSIAA/DetectLSB recovered \sim 20 % more mock LSB galaxies and \sim 40 % more LSB galaxies identified by eye than SExtractor/DetectLSB .
With a 90 % fraction of false positives from an entirely unsupervised pipeline , a completeness of 90 % is reached for sources with r _ { e } > 3 ^ { \prime \prime } at a mean surface brightness level of \mu _ { g } = 27.7  mag arcsec ^ { -2 } and a central surface brightness of \mu ^ { 0 } _ { g } = 26.7  mag arcsec ^ { -2 } .
About 10 % of the false positives are artifacts , the rest being background galaxies .
We have found our proposed Markovian LSB galaxy detection method to be complementary to the application of matched filters and an optimized use of SExtractor , and to have the following advantages : it is scale-free , can be applied simultaneously to several bands , and is well adapted for crowded regions on the sky .