Laboratoire RDP » Biophysics Team » lsm3d

----------------------------------------------------------------------

			        - lsm3d -

    Tools for segmenting 3D images of plant tissues

     at multiple scales using the level set method¶

----------------------------------------------------------------------

Authors :

Typhaine Moreau, Annamaria Kiss (Laboratoire RDP, ENS Lyon)

----------------------------------------------------------------------

The "lsm3d" tools

-----------------

- lsm_contour --> detects the outer surface of the tissue

- lsm_cells --> used to cellular segmentation or nuclei detection

Dependencies

-------------

The levelset tools make use of the CImg image processing C++ library, which in turn needs Xlib library.

Furthermore, the lsm_cells tool is parallelized using OpenMP.

In particular, if you do not have Xlib on your computer, you may add it:

- on Ubuntu:

    sudo apt-get install libx11-dev

- on MacOS X:

    install xQuartz from www.xquartz.org

Download

--------

In a terminal go to the directory where you wish to download and install the tool

cd Path_to_your_directory

svn --username $USER checkout http://forge.cbp.ens-lyon.fr/svn/levelsetmethod/lsm3D

Compile

--------

cd lsm3D

./lsm3D_compile.sh

Troubleshooting if problems with compilation :

In lsm3D_compile.sh

- you might have to choose an appropriate compiler, which supports OpenMP.

- you might need to precise the location of the X11 library in the compilation options.

The following binaries will be generated in the bin directory :

--------------------------------------------------------------

- lsm_contour --> detects the outer surface of the tissue

- lsm_cells --> used to cellular segmentation or nuclei detection

**** lsm_contour --> detects the outer surface of the tissue

-------------------------------------------------------

Usage : lsm_contour img t_up t_down a b smooth perUp perDown

Examples for parameter values:

------------------------------

img : grayscale image of cells, (.inr or .inr.gz)

Upper threshold : t_up = 20

Down threshold : t_down = 5

Area term : a = 0 (0.5, 1)

Curvature term : b = 0 (1)

Gaussian filter : smooth = 1 (0, if image already filtered)

Stop criteria : the contour evolution is in [perDown,perUp] for 10 consecutive iterations

     perUp = 0.002, perDown = -0.002

Test :

------

lsm_detect_contour sample-stack.inr.gz 20 10 0 0 1 0.002 -0.002

**** lsm_cells --> for cellular segmentation or nuclei detection

-------------------------------------------------------------

Usage : lsm_cells img img_wat img_contour erosion [a b smooth lsm_type]

-----------------

img : grayscale image of cells, (.inr or .inr.gz)

img_wat : image of seeds, (.inr or .inr.gz)

img_contour : mask, where cells do not evolve, (.inr or .inr.gz)

              if 'None', then cells can evolve on the whole image

erosion : amount of erosion of seeds for initialisation (uint8) --> -2, 0, 2

              if 0, then no erosion or dilation

              if negative, then a dilation is performed

a : area term (float) --> 0 or 0.5 or 1 (the default is 0.5)

              if negative, the object retracts

              if positive, the object inflates

b : curvature term (float) --> 0 or 1 (the default is 0)

gamma : scale parameter (float>0) --> 0.5 or 1 (the default is 1)

smooth : gaussian blur to apply to the image (int) --> 0 or 1 (the default is 0)

lsm_type : image, gradient or hessien based evolution --> 'i', 'g' or 'h' (the default is g)

Test :

------

lsm_cells sample-satck.inr.gz sample-satck-wat.inr.gz 'None' 2 0.3 0 0.2 1 'h'

or

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'