Description Usage Arguments Value Author(s) References See Also Examples
Performs the denoising step of a dynamic sequence of images. It is also the first step of the clustering.
The denoising procedure is iteratively applied on each voxel.
The denoised version of Fx is obtained with a three stages procedure: 1) Selection of timehomogeneous voxels in the submask around the voxel of interest; 2) Growth of spatial neighborhoods made of timehomogeneous voxels obtained at stage 1 with sizes growing geometrically—each neiborhood is associated to a denoised version by averaging over its members; 3) Selection of the largest spatial neighborhood such that its associated denoised version is timehomogeneous with all the previous ones.
Time homogeneity is tested with function
MultiTestH0
.
Further details about the denoising method and the statistical test of homogeneity can be found in the references.
1 2 
data.array 
a (2D or 3D)+T array containing the dynamic sequence of images (the dataset). The last dimension is the time. 
data.var 
a numeric indicating the variance of the
dataset (default 1). If set to NULL, the variance is
computed using a baseline image. See 
depth 
a numeric indicating the depth of a voxel. 
alpha 
a numeric value indicating the global level of the multitest. 
mask.size 
a vector indicating the size of the spatial hypercube defined around voxels used to search for neighbors. If NA (default): sqrt(dim(data.array)[1:length(dim(data.array))1]). If NULL (complete image): dim(data.array)[1:length(dim(data.array))1] 
nproc 
a numeric value indicating the number of processors used for parallel computation. 
enhStart 
an integer, if larger than 1, a baseline
is computed as a median image obtain from time indexes
between 1 and enhStart1. Default value

a list containing:
info.den
, a list
of list whose length is the number of voxels, each sublist
contains the result of buildEstimate for one voxel.
data.proj
, the projections of the dynamics. a list
containing a denoised version of the dataset as an array,
as well as a list for which each element contains a list
with the voxel index, the indexes of its neighboors, the
resulting denoised signal, and the variance of the denoised
signal
var
, a numeric providing the known
variance
Tiffany Lieury, Christophe Pouzat, Yves Rozenholc
Rozenholc, Y. and Reiss, M. (2012) Preserving time structures while denoising a dynamical image, Mathematical Methods for Signal and Image Analysis and Representation (Chapter 12), Florack, L. and Duits, R. and Jongbloed, G. and van~Lieshout, M.C. and Davies, L. Ed., SpringerVerlag, Berlin
Lieury, T. and Pouzat, C. and Rozenholc, Y. (submitted) Spatial denoising and clustering of dynamical image sequence: application to DCE imaging in medicine and calcium imaging in neurons
GetDenoisingResults
,
MultiTestH0
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  ## Not run:
library(DynClust)
## use fluorescence calcium imaging of neurons performed with Fura 2 excited at 340 nm
data('adu340_4small',package='DynClust')
## Gain of the CCD camera:
G < 0.146
## readout variance of the CCD camera:
sro2 < (16.4)^2
## Stabilization of the variance to get a normalized dataset (variance=1)
FT < 2*sqrt(adu340_4small/G + sro2)
FT.range = range(FT)
## launches the denoising step on the dataset with a statistical level of 5%
FT.den.tmp < RunDenoising(FT,1,mask.size=NA,nproc=2)
## get the results of the denoising step
FT.den.res < GetDenoisingResults(FT,FT.den.tmp)
## plot results at time 50 in same grey scale
par(mfrow=c(1,3))
image(FT[,,50],zlim=FT.range,col=gray(seq(0,1,l=128)))
title('Original')
image(FT.den.res[,,50],zlim=FT.range,col=gray(seq(0,1,l=128)))
title('Denoised')
image(FT.den.res[,,50]FT[,,50],col=gray(seq(0,1,l=128)))
title('Residuals')
## End(Not run)

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