calculateCellularity: Calculation of tumour cellularity
In CRImage: CRImage a package to classify cells and calculate tumour cellularity

Description Usage Arguments Details Value Author(s) Examples

The function calculates the tumour cellularity of an image by counting tumour and non tumour cells.

calculateCellularity(filename="",image=NA,classifier=NULL,cancerIdentifier=NA,KS=FALSE,maxShape=NA,minShape=NA,failureRegion=NA,colors=c(),threshold="otsu",classesToExclude=c(),numWindows=2,classifyStructures=FALSE,pixelClassifier=NA,ksToExclude=c(),densityToExclude=c(),numDensityWindows=4)

`filename`	A path to an image file.
`image`	If filename is undefined, an Image object
`classifier`	A SVM object, created with createClassifier or directly with the package e1071
`cancerIdentifier`	A string which describes, how the cancer class is named.
`KS`	Apply kernel smoother?
`maxShape`	Maximum size of cell nuclei
`minShape`	Minimum size of cell nuclei
`failureRegion`	minimum size of failure regions
`colors`	Colors to paint the classes
`threshold`	Which threshold should be uses, "otsu" or "phansalkar"
`classesToExclude`	Should a class be excluded from cellularity calculation?
`numWindows`	Number of windows for the threshold.
`classifyStructures`	Use hierarchical classification. If yes a pixel classifier has to be defined.
`pixelClassifier`	A SVM to classify pixel based on their color values. Needed if hierarchical classification should be applied.
`ksToExclude`	These classes are excluded from kernel smoothing.
`densityToExclude`	This class is excluded from cellularity calculation.
`numDensityWindows`	Number of windows for the density plot.

The method calculates tumour cellularity of an image. The cells of the image are classified and the cellularity is: numTumourCells/numPixel. Furthermore the number of cells of the different classes are counted. A heatmap of cellularity is created. The image is divided in 16 subwindows and cellularity is calculated for every subwindow. Green in the heatmaps indicates strong cellularity, white low cellularity.

A list containing

`cellularity values`	a vector, the n first values indicate the n numbers of cells in the n classes, the n + 1th value indicates the tumour cellularity, The n + 2th value is the ratio of tumour cells by all cells
`cancerHeatmap`	Heatmap of cancer density

Henrik Failmezger, failmezger@mpipz.mpg.de

t = system.file("extdata", "trainingData.txt", package="CRImage")
#read training data
trainingData=read.table(t,header=TRUE)
#create classifier
classifier=createClassifier(trainingData)[[1]]
#calculation of cellularity
f = system.file("extdata", "exImg.jpg", package="CRImage")
exImg=readImage(f)
cellularity=calculateCellularity(classifier=classifier,filename=f,KS=TRUE,maxShape=800,minShape=40,failureRegion=2000,classifyStructures=FALSE,cancerIdentifier="c",numDensityWindows=2,colors=c("green","red"))