\name{calculateCellularity} \alias{calculateCellularity} \title{Calculation of tumour cellularity} \description{ The function calculates the tumour cellularity of an image by counting tumour and non tumour cells. } \usage{ 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) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{filename}{A path to an image file.} \item{image}{If filename is undefined, an Image object } \item{classifier}{A SVM object, created with createClassifier or directly with the package e1071} \item{cancerIdentifier}{ A string which describes, how the cancer class is named.} \item{KS}{Apply kernel smoother?} \item{maxShape}{Maximum size of cell nuclei} \item{minShape}{ Minimum size of cell nuclei } \item{failureRegion}{ minimum size of failure regions} \item{colors}{Colors to paint the classes} \item{threshold}{Which threshold should be uses, "otsu" or "phansalkar"} \item{classesToExclude}{Should a class be excluded from cellularity calculation?} \item{numWindows}{Number of windows for the threshold.} \item{classifyStructures}{Use hierarchical classification. If yes a pixel classifier has to be defined. } \item{pixelClassifier}{A SVM to classify pixel based on their color values. Needed if hierarchical classification should be applied.} \item{ksToExclude}{These classes are excluded from kernel smoothing.} \item{densityToExclude}{This class is excluded from cellularity calculation.} \item{numDensityWindows}{Number of windows for the density plot.} } \details{ 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. } \value{ A list containing \item{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} \item{cancerHeatmap }{Heatmap of cancer density} } \author{ Henrik Failmezger, failmezger@cip.ifi.lmu.de } \examples{ #t = system.file("extdata", "trainingData.txt", package="CRImage") #read training data #trainingData=read.table(t,header=TRUE) #create classifier #classifier=createClassifier(trainingData,topo=FALSE)[[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="1",numDensityWindows=2,colors=c("green","red")) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ misc }