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			        - lsm3d -
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    Tools for segmenting 3D images of plant tissues
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     at multiple scales using the level set method¶
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Copyright 2016 ENS de Lyon, see accompanying file LICENSE.txt
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Authors :
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Typhaine Moreau, Annamaria Kiss <annamaria.kiss@ens-lyon.fr.fr>
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(Laboratoire Reproduction et Développement des Plantes, Univ Lyon, ENS de Lyon, UCB Lyon 1, CNRS, INRA, F-69342, Lyon, France)
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The "lsm3d" tools
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-----------------
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- lsm_contour --> detects the outer surface of the tissue
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- lsm_cells --> used to cellular segmentation or nuclei detection
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Dependencies
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-------------
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The levelset tools make use of the CImg image processing C++ library, which in turn needs Xlib library.
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Furthermore, the lsm_cells tool is parallelized using OpenMP.
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In particular, if you do not have Xlib on your computer, you may add it:
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- on Ubuntu:
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    sudo apt-get install libx11-dev
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- on MacOS X:
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    install xQuartz from www.xquartz.org
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Download
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--------
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In a terminal go to the directory where you wish to download and install the tool
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cd Path_to_your_directory
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svn checkout http://forge.cbp.ens-lyon.fr/svn/lsm3d
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Compile
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--------
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cd lsm3d
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./lsm3d_compile.sh
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Troubleshooting if problems with compilation :
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In lsm3d_compile.sh
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- you might have to choose an appropriate compiler, which supports OpenMP.
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- you might need to precise the location of the X11 library in the compilation options.
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The following binaries will be generated in the bin directory :
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--------------------------------------------------------------
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- lsm_contour --> detects the outer surface of the tissue
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- lsm_cells --> used to cellular segmentation or nuclei detection
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**** lsm_contour --> detects the outer surface of the tissue
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-------------------------------------------------------
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Usage : lsm_contour img t_up t_down a b smooth perUp perDown
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Examples for parameter values:
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------------------------------
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img : grayscale image of cells, (.inr or .inr.gz)
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Upper threshold : t_up = 20
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Down threshold : t_down = 5
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Area term : a = 0 (0.5, 1)
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Curvature term : b = 0 (1)
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Gaussian filter : smooth = 1 (0, if image already filtered)
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Stop criteria : the contour evolution is in [perDown,perUp] for 10 consecutive iterations
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     perUp = 0.002, perDown = -0.002
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Test :
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------
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lsm_detect_contour sample-stack.inr.gz 20 10 0 0 1 0.002 -0.002
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**** lsm_cells --> for cellular segmentation or nuclei detection
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-------------------------------------------------------------
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Usage : lsm_cells img img_wat img_contour erosion [a b smooth lsm_type]
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-----------------
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img : grayscale image of cells, (.inr or .inr.gz)
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img_wat : image of seeds, (.inr or .inr.gz)
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img_contour : mask, where cells do not evolve, (.inr or .inr.gz)
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              if 'None', then cells can evolve on the whole image
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erosion : amount of erosion of seeds for initialisation (uint8) --> -2, 0, 2
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              if 0, then no erosion or dilation
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              if negative, then a dilation is performed
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a : area term (float) --> 0 or 0.5 or 1 (the default is 0.5)
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              if negative, the object retracts
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              if positive, the object inflates
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b : curvature term (float) --> 0 or 1 (the default is 0)
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gamma : scale parameter (float>0) --> 0.5 or 1 (the default is 1)
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smooth : gaussian blur to apply to the image (int) --> 0 or 1 (the default is 0)
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lsm_type : image, gradient or hessien based evolution --> 'i', 'g' or 'h' (the default is g)
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Test :
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------
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lsm_cells sample-satck.inr.gz sample-satck-wat.inr.gz 'None' 2 0.3 0 0.2 1 'h'
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or
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lsm_cells sample-satck.inr.gz sample-satck-wat.inr.gz sample-satck_LSMcont20-10a0b0s1/sample-satck_LSMcont20-10a0b0s1.inr.gz 2 0.3 0 0.2 1 'h'
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