calculateCellularity {CRImage} | R Documentation

## 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)

### Arguments

`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.

### 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

`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

### Author(s)

Henrik Failmezger, failmezger@mpipz.mpg.de

### Examples

    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"))

[Package CRImage version 1.30.0 Index]