GetClusteringResults | R Documentation |
GetClusteringResults
returns the results of the
clustering procedure RunClustering
GetClusteringResults(data.array, res.listdenois, res.cluster)
data.array |
a (2D or 3D)+T array containing the original dynamic sequence of images (the dataset). The last dimension is the time. |
res.listdenois |
the list resulting from the
|
res.cluster |
the list resulting from a call to
|
a list containing two components clust.array
and
clust.map
. clust.array
is an array with same
dimension as the original sequence data.array
containing the clustered version. clust.map
is an
array with only spatial dimensions of data.array
whose elements provide the cluster number at each location.
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., Springer-Verlag, 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
RunDenoising
, RunClustering
## 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) ## launches the clustering step on the dataset with a statistical level of 5% FT.clust.tmp <- RunClustering(FT,FT.den.tmp,nproc=2) n.cluster <- length(FT.clust.tmp$clusters) print(paste(n.cluster,'clusters using variance set to',sqrt(FT.den.tmp$var),'^2')) ## get the classified version of the data array and the map of the clusters FT.clust.res <- GetClusteringResults(FT,FT.den.tmp,FT.clust.tmp) ## plotting results of the clusterization par(mfrow=c(2,2)) image(FT.clust.res$clust.map,col=rainbow(n.cluster)) title('Cluster map') matplot(FT.clust.res$clust.center,col=rainbow(n.cluster),type="l",lwd=0.1,lty=1) title('Cluster centers') ## and more: original and clustered slices at time 50 image(FT[,,50],zlim=FT.range,col=grey(seq(0,1,length=n.cluster))) title('Original sequence at time 50') image(FT.clust.res$clust.array[,,50],zlim=FT.range,col=grey(seq(0,1,length=n.cluster))) title('Clustered sequence at time 50') #################################################################################### ## reapply clustering with twice the nominal variance: forces stronger clustering ## #################################################################################### ## launches the denoising step on the dataset with a statistical level of 5% FT.den.tmp <- RunDenoising(FT,2,mask.size=NA,nproc=2) ## launches the clustering step on the dataset with a statistical level of 5% FT.clust.tmp <- RunClustering(FT,FT.den.tmp,nproc=2) n.cluster <- length(FT.clust.tmp$clusters) print(paste(n.cluster,'clusters using twice the nominal variance')) ## get the classified version of the data array and the map of the clusters FT.clust.res <- GetClusteringResults(FT,FT.den.tmp,FT.clust.tmp) ## plotting results of the clusterization par(mfrow=c(2,2)) image(FT.clust.res$clust.map,col=rainbow(n.cluster)) title('Cluster map') matplot(FT.clust.res$clust.center,col=rainbow(n.cluster),type="l",lwd=0.1,lty=1) title('Cluster centers') ## and more: original and clustered slices at time 50 image(FT[,,50],zlim=FT.range,col=grey(seq(0,1,length=n.cluster))) title('Original sequence at time 50') image(FT.clust.res$clust.array[,,50],zlim=FT.range,col=grey(seq(0,1,length=n.cluster))) title('Clustered sequence at time 50') ## End(Not run)
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