distance2border {bioimagetools}

A function to compute the distance from spots to borders of classes

Description

A function to compute the distance from spots to borders of classes

Usage

distance2border(
  points,
  img.classes,
  x.microns,
  y.microns,
  z.microns,
  class1,
  class2 = NULL,
  mask = array(TRUE, dim(img.classes)),
  voxel = FALSE,
  hist = FALSE,
  main = "Minimal distance to border",
  xlab = "Distance in Microns",
  xlim = c(-0.3, 0.3),
  n = 20,
  stats = TRUE,
  file = NULL,
  silent = FALSE,
  parallel = FALSE
)

Arguments

points

Data frame containing the coordinates of points in microns as X-, Y-, and Z-variables.

img.classes

3D array (or image) of classes for each voxel.

x.microns

Size of image in x-direction in microns.

y.microns

Size of image in y-direction in microns.

z.microns

Size of image in z-direction in microns.

class1

Which class is the reference class. If is.null(class2), the function computes the distance of points to the border of class (in img.classes).

class2

Which class is the second reference class. If not is.null(class2), the function computes the distance of points from the border between classes class1 and class2. Default: class2=NULL.

mask

Array of mask. Needs to have same dimension as img.classes. Only voxels with mask[i,j,k]==TRUE are used. Default: array(TRUE,dim(img.classes))

voxel

Logical. If TRUE, points coordinates are given as voxels rather than in microns.

hist

Automatically plot histogram using hist() function. Default: FALSE.

main

If (hist) title of histogram. Default: "Minimal distance to border".

xlab

If (hist) description of x axis. Default: "Distance in Microns".

xlim

If (hist) vector of range of x axis (in microns). Default: c(-.3,.3)

n

If (hist) number of bins used in hist(). Default: 20.

stats

If (hist) write statistics into plot. Default: TRUE.

file

If (hist) the file name of the produced png. If NULL, the histogram is plotted to the standard device. Default: NULL.

silent

if TRUE, function remains silent during running time

parallel

Logical. Can we use parallel computing?

Details

This function computes the distances from points to the border of a class or the border between two classes. For the latter, only points in these two classes are used.

Value

The function returns a vector with distances. Negative values correspond to points lying in class1.

Note

Warning: So far no consistency check for arguments is done. E.g., distance2border(randompoints,img.classes=array(1,c(100,100,2)),3,3,1,class1=2) will fail with some cryptic error message (because class1 > max(img.classes)).

Examples

## Not run: 
#simulate random data
randompoints<-data.frame("X"=runif(100,0,3),"Y"=runif(100,0,3),"Z"=runif(100,0,.5))
# coordinates in microns!
plot(randompoints$X,randompoints$Y,xlim=c(0,3),ylim=c(0,3),pch=19)

# points in a circle
circlepoints<-read.table(system.file("extdata","kreispunkte.table",
                               package="bioimagetools"),header=TRUE)
plot(circlepoints$X,circlepoints$Y,xlim=c(0,3),ylim=c(0,3),pch=19)

# a circle like image
img<-readTIF(system.file("extdata","kringel.tif",package="bioimagetools"))
img<-array(img,dim(img)) # save as array for easier handling
img(img, z=1)

#and a mask
mask<-readTIF(system.file("extdata","amask.tif",package="bioimagetools"))
img(mask, z=1, col="greyinverted")

xy.microns <- 3 # size in x and y direction (microns)
z.microns <- 0.5 # size in z direction (microns)

# distance from points to class 
d1<-distance2border(randompoints, img, xy.microns, xy.microns, z.microns, class1=1,hist=TRUE)
d2<-distance2border(circlepoints, img, xy.microns, xy.microns, z.microns, class1=1,hist=FALSE)
plot(density(d2),type="l")
lines(c(0,0),c(0,10),lty=3)
lines(density(d1),col="blue")

# use mask, should give some small changes
d3<-distance2border(circlepoints, img, xy.microns, xy.microns, z.microns, 
                                                class1=1,mask=mask,hist=FALSE)
plot(density(d2),type="l")
lines(c(0,0),c(0,10),lty=3)
lines(density(d3),col="blue")

# distance from border between classes
anotherimg<-img+mask
image(seq(0,3,length=300),seq(0,3,length=300),anotherimg[,,1])
points(circlepoints,pch=19)
d4<-distance2border(circlepoints, anotherimg, xy.microns, xy.microns, z.microns, 
                                                               class1=1,class2=2)
plot(density(d4),lwd=2)

# this should give the same answer
d5<-distance2border(circlepoints, anotherimg, xy.microns, xy.microns, z.microns, 
                                                                class1=2,class2=1)
lines(density(-d5),lty=3,col="blue",lwd=1.5)

## End(Not run)