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We present an algorithm for the detection of Low Surface Brightness (LSB)galaxies in images, called MARSIAA (MARkovian Software for Image Analysis inAstronomy), which is based on multi-scale Markovian modeling. MARSIAA can beapplied simultaneously to different bands. It segments an image into auser-defined number of classes, according to their surface brightness andsurroundings - typically, one or two classes contain the LSB structures. Wehave developed an algorithm, called DetectLSB, which allows the efficientidentification of LSB galaxies from among the candidate sources selected byMARSIAA. To assess the robustness of our method, the method was applied to aset of 18 B and I band images (covering 1.3 square degrees in total) of theVirgo cluster. To further assess the completeness of the results of our method,both MARSIAA, SExtractor, and DetectLSB were applied to search for (i) mockVirgo 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 afull set of NGVS square degree gri images. MARSIAA/DetectLSB recovered ~20%more mock LSB galaxies and ~40% more LSB galaxies identified by eye thanSExtractor/DetectLSB. With a 90% fraction of false positives from an entirelyunsupervised pipeline, a completeness of 90% is reached for sources with r_e >3" at a mean surface brightness level of mu_g=27.7 mag/arcsec^2 and a centralsurface brightness of mu^0 g=26.7 mag/arcsec^2. About 10% of the falsepositives are artifacts, the rest being background galaxies. We have found ourmethod to be complementary to the application of matched filters and anoptimized use of SExtractor, and to have the following advantages: it isscale-free, can be applied simultaneously to several bands, and is well adaptedfor crowded regions on the sky.

Simultaneous multi-band detection of Low Surface Brightness galaxies with Markovian modelling