Package overview

Description

EBImage is an image processing and analysis package for R. Its primary goal is to enable automated analysis of large sets of images such as those obtained in high throughput automated microscopy.

EBImage relies on the Image object to store and process images but also works on multi-dimensional arrays.

Examples

example(readImage)
example(display)
example(rotate)
example(propagate)

Defunct functions in package EBImage

Description

These functions are defunct and no longer available.

Details

The following functions are defunct and no longer available; use the replacement indicated below.

• list(list("animate"), ": ", list(list("display")))

• list(list("blur"), ": ", list(list("gblur")))

• list(list("drawtext"), ": see package vignette for documentation on how to add text labels to images")

• list(list("drawfont"), ": see package vignette for documentation on how to add text labels to images")

• list(list("getFeatures"), ": ", list(list("computeFeatures")))

• list(list("getNumberOfFrames"), ": ", list(list("numberOfFrames")))

• list(list("hullFeatures"), ": ", list(list("computeFeatures.shape")))

• list(list("zernikeMoments"), ": ", list(list("computeFeatures")))

• list(list("edgeProfile"), ": ", list(list("computeFeatures")))

• list(list("edgeFeatures"), ": ", list(list("computeFeatures.shape")))

• list(list("haralickFeatures"), ": ", list(list("computeFeatures")))

• list(list("haralickMatrix"), ": ", list(list("computeFeatures")))

• list(list("moments"), ": ", list(list("computeFeatures.moment")))

• list(list("cmoments"), ": ", list(list("computeFeatures.moment")))

• list(list("rmoments"), ": ", list(list("computeFeatures.moment")))

• list(list("smoments"), ": ", list(list("computeFeatures.moment")))

• list(list("dilateGreyScale"), ": ", list(list("dilate")))

• list(list("erodeGreyScale"), ": ", list(list("erode")))

• list(list("openingGreyScale"), ": ", list(list("opening")))

• list(list("closingGreyScale"), ": ", list(list("closing")))

• list(list("whiteTopHatGreyScale"), ": ", list(list("whiteTopHat")))

• list(list("blackTopHatGreyScale"), ": ", list(list("blackTopHat")))

• list(list("selfcomplementaryTopHatGreyScale"), ": ", list(list("selfComplementaryTopHat")))

Image class

Description

EBImage uses the Image class to store and process images. Images are stored as multi-dimensional arrays containing the pixel intensities. Image extends the base class array and uses the colormode slot to store how the color information of the multi-dimensional data is handled.

The colormode slot can be either Grayscale or Color . In either mode, the first two dimensions of the underlying array are understood to be the spatial dimensions of the image. In the Grayscale mode the remaining dimensions contain other image frames. In the Color mode, the third dimension contains color channels of the image, while higher dimensions contain image frames. The number of channels is not limited and can be any number >= 1; these can be, for instance, the red, green, blue and, possibly, alpha channel. Note that grayscale images containing an alpha channel are stored with colormode=Color .

All methods from the EBImage package work either with Image objects or multi-dimensional arrays. In the latter case, the color mode is assumed to be Grayscale .

Usage

Image(data, dim, colormode)
as.Image(x)
is.Image(x)
list(list("as.array"), list("Image"))(x, list())
list(list("as.raster"), list("Image"))(x, max = 1, i = 1L, list())
colorMode(y)
colorMode(y) <- value
imageData(y)
imageData(y) <- value
getFrame(y, i, type = c('total', 'render'))
getFrames(y, i, type = c('total', 'render'))
numberOfFrames(y, type = c('total', 'render'))

Arguments

Details

Depending on type , numberOfFrames returns the total number of frames contained in the object y or the number of rendered frames. The total number of frames is independent of the color mode and equals to the product of all the dimensions except the two first ones. The number of rendered frames is equal to the total number of frames in the Grayscale color mode, or to the product of all the dimensions except the three first ones in the Color color mode.

getFrame returns the i-th frame contained in the image y . If type is total , the function is unaware of the color mode and returns an xy-plane. For type=render , the function returns the i-th image as shown by the display function.

Value

Image and as.Image return a new Image object.

is.Image returns TRUE if x is an Image object and FALSE otherwise.

as.raster coerces an Image object to its raster representation. For stacked images the i -th frame is returned (by default the first one).

colorMode returns the color mode of y and colorMode<- changes the color mode of y .

imageData returns the array contained in an Image object.

Seealso

readImage , writeImage , display

Author

Oleg Sklyar, osklyar@ebi.ac.uk , 2005-2007

Examples

s1 = exp(12i*pi*seq(-1, 1, length=300)^2)
y = Image(outer(Im(s1), Re(s1)))
display(normalize(y))

x = Image(rnorm(300*300*3),dim=c(300,300,3), colormode='Color')
display(x)

w = matrix(seq(0, 1, len=300), nc=300, nr=300)
m = abind::abind(w, t(w), along=3)
z = Image(m, colormode='Color')
display(normalize(z))

y = Image(matrix(c('red', 'violet', '#ff51a5', 'yellow'), nrow=10, ncol=10))
display(y, interpolate=FALSE)

## colorMode example
x = readImage(system.file('images', 'nuclei.tif', package='EBImage'))
x = x[,,1:3]
display(x, title='Cell nuclei')
colorMode(x) = Color
display(x, title='Cell nuclei in RGB')

Combine Image Arrays

Description

Methods for function abind from package abind useful for combining Image arrays.

Value

An Image object or an array, containing the combined data arrays of the input objects.

Seealso

combine provides a more convenient interface to merging images into an image sequence. Use tile to lay out images next to each other in a regular grid.

Author

Andrzej Oleś, andrzej.oles@embl.de , 2017

Examples

f = system.file("images", "sample-color.png", package="EBImage")
x = readImage(f)

## combine images horizontally
y = abind(x, x, along=1)
display(y)

## stack images one on top of the other
z = abind(x, x, along=2)
display(z)

Binary segmentation

Description

Labels connected (connected sets) objects in a binary image.

Usage

bwlabel(x)

Arguments

Details

All pixels for each connected set of foreground (non-zero) pixels in x are set to an unique increasing integer, starting from 1. Hence, max(x) gives the number of connected objects in x .

Value

A Grayscale Image object or an array, containing the labelled version of x .

Seealso

computeFeatures , propagate , watershed , paintObjects , colorLabels

Author

Gregoire Pau, 2009

Examples

## simple example
x = readImage(system.file('images', 'shapes.png', package='EBImage'))
x = x[110:512,1:130]
display(x, title='Binary')
y = bwlabel(x)
display(normalize(y), title='Segmented')

## read nuclei images
x = readImage(system.file('images', 'nuclei.tif', package='EBImage'))
display(x)

## computes binary mask
y = thresh(x, 10, 10, 0.05)
y = opening(y, makeBrush(5, shape='disc'))
display(y, title='Cell nuclei binary mask')

## bwlabel
z = bwlabel(y)
display(normalize(z), title='Cell nuclei')
nbnuclei = apply(z, 3, max)
cat('Number of nuclei=', paste(nbnuclei, collapse=','),'
')

## paint nuclei in color
cols = c('black', sample(rainbow(max(z))))
zrainbow = Image(cols[1+z], dim=dim(z))
display(zrainbow, title='Cell nuclei (recolored)')

Color and image color mode conversions

Description

channel handles color space conversions between image modes. rgbImage combines Grayscale images into a Color one. toRGB is a wrapper function for convenient grayscale to RGB color space conversion; the call toRGB(x) returns the result of channel(x, 'rgb') .

Usage

channel(x, mode)
rgbImage(red, green, blue)
toRGB(x)

Arguments

Details

Conversion modes: list(" ", " ", list(list(list("rgb")), list("Converts a ", list("Grayscale"), " image or an array ", " into a ", list("Color"), " image, replicating RGB channels.")), " ", " ", " ", list(list(list("gray, grey")), list("Converts a ", list("Color"), " image into a ", " ", list("Grayscale"), " image, using uniform 1/3 RGB weights.")), " ", "
", " ", list(list(list("luminance")), list("Luminance-preserving ", list("Color"), " to ", list("Grayscale"), " conversion ", " using CIE 1931 luminance weights: 0.2126 R + 0.7152 G + 0.0722 * B.")),

"

", " ", " ", list(list(list("red, green, blue")), list("Extracts the ", list("red"), ", ", list("green"), " or ", " ", list("blue"), " channel from a ", list("Color"), " image. Returns a ", " ", list("Grayscale"), " image.")), " ", " ", " ", list(list(list("asred, asgreen, asblue")), list("Converts a ", list("Grayscale"), " ", " image or an array into a ", list("Color"), " image of the specified hue.")), " ")

NOTE: channel changes the pixel intensities, unlike colorMode which just changes the way that EBImage renders an image.

Value

An Image object or an array.

Seealso

colorMode

Author

Oleg Sklyar, osklyar@ebi.ac.uk

Examples

x = readImage(system.file("images", "shapes.png", package="EBImage"))
display(x)
y = channel(x, 'asgreen')
display(y)

## rgbImage
x = readImage(system.file('images', 'nuclei.tif', package='EBImage'))
y = readImage(system.file('images', 'cells.tif', package='EBImage'))
display(x, title='Cell nuclei')
display(y, title='Cell bodies')

cells = rgbImage(green=1.5*y, blue=x)
display(cells, title='Cells')

Contrast Limited Adaptive Histogram Equalization

Description

Improve contrast locally by performing adaptive histogram equalization.

Usage

clahe(x, nx = 8, ny = nx, bins = 256, limit = 2, keep.range = FALSE)

Arguments

Details

Adaptive histogram equalization (AHE) is a contrast enhancement technique which overcomes the limitations of standard histogram equalization. Unlike ordinary histogram equalization the adaptive method redistributes the lightness values of the image based on several histograms, each corresponding to a distinct section of the image. It is therefore useful for improving the local contrast and enhancing the definitions of edges in each region of an image. However, AHE has a tendency to overamplify noise in relatively homogeneous regions of an image. Contrast limited adaptive histogram equalization (CLAHE) prevents this by limiting the amplification.

The function is based on the implementation by Karel Zuiderveld [1]. This implementation assumes that the X- and Y image dimensions are an integer multiple of the X- and Y sizes of the contextual regions. The input image x should contain pixel values in the range from 0 to 1, inclusive; values lower than 0 or higher than 1 are clipped before applying the filter. Internal processing is performed in 16-bit precision. If the image contains multiple channels or frames, the filter is applied to each one of them separately.

Value

An Image object or an array, containing the filtered version of x .

Seealso

equalize

Note

The interpolation step of the original implementation by Karel Zuiderveld [1] was modified to use double precision arithmetic in order to make the filter rotationally invariant for even-sized contextual regions, and the result is properly rounded rather than truncated towards 0 in order to avoid a systematic shift of pixel values.

Author

Andrzej Oleś, andrzej.oles@embl.de , 2017

References

[1] K. Zuiderveld: Contrast Limited Adaptive Histogram Equalization. In: P. Heckbert: Graphics Gems IV, Academic Press 1994

Examples

x = readImage(system.file("images", "sample-color.png", package="EBImage"))
y = clahe(x)
display(y)

Color Code Labels

Description

Color codes the labels of object masks by a random permutation.

Usage

colorLabels(x, normalize = TRUE)

Arguments

Details

Performs color coding of object masks, which are typically obtained using the bwlabel function. Each label from x is assigned an unique color. The colors are distributed among the labels using a random permutation. If normalize is set to TRUE the intensity values of the resulting image are mapped to the [0,1] range.

Value

An Image object containing color coded objects of x .

Seealso

bwlabel , normalize

Author

Bernd Fischer, Andrzej Oles, 2013-2014

Examples

x = readImage(system.file('images', 'shapes.png', package='EBImage'))
x = x[110:512,1:130]
y = bwlabel(x)
z = colorLabels(y)
display(z, title='Colored segmentation')

Map a Greyscale Image to Color

Description

Maps a greyscale image to color using a color palette.

Usage

colormap(x, palette = heat.colors(256L))

Arguments

Details

The colormap function first linearly maps the pixel intensity values of x to the integer range 1:length(palette) . It then uses these values as indices to the provided color palette to create a color version of the original image.

The default palette contains 256 colors, which is the typical number of different shades in a 8bit grayscale image.

Value

An Image object of color mode Color , containing the color-mapped version of x .

Author

Andrzej Oleś, andrzej.oles@embl.de , 2016

Examples

x = readImage(system.file("images", "sample.png", package="EBImage"))

## posterize an image using the topo.colors palette
y = colormap(x, topo.colors(8))

display(y, method="raster")

## mimic MatLab's 'jet.colors' colormap
jet.colors = colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
y = colormap(x, jet.colors(256))

display(y, method="raster")

Combine images

Description

Merges images to create image sequences.

Usage

combine(x, y, ...)

Arguments

Details

The function combine uses abind to merge multi-dimensional arrays along the dimension depending on the color mode of x . If x is a Grayscale image or an array, image objects are combined along the third dimension, whereas when x is a Color image they are combined along the fourth dimension, leaving room on the third dimension for color channels.

Value

An Image object or an array.

Seealso

The method abind provides a more flexible interface which allows to specify the dimension along which to combine the images.

Author

Gregoire Pau, Andrzej Oles, 2013

Examples

## combination of color images
img = readImage(system.file("images", "sample-color.png", package="EBImage"))[257:768,,]
x = combine(img, flip(img), flop(img))
display(x, all=TRUE)

## Blurred images
x = resize(img, 128, 128)
xt = list()
for (t in seq(0.1, 5, len=9)) xt=c(xt, list(gblur(x, s=t)))
xt = combine(xt)
display(xt, title='Blurred images', all=TRUE)

Compute object features

Description

Computes morphological and texture features from image objects.

Usage

computeFeatures(x, ref, methods.noref=c("computeFeatures.moment", "computeFeatures.shape"),
  methods.ref=c("computeFeatures.basic", "computeFeatures.moment", "computeFeatures.haralick"),
  xname="x", refnames, properties=FALSE, expandRef=standardExpandRef, ...)
  
computeFeatures.basic(x, ref, properties=FALSE, basic.quantiles=c(0.01, 0.05, 0.5, 0.95, 0.99), xs, ...)
computeFeatures.shape(x, properties=FALSE, xs, ...)
computeFeatures.moment(x, ref, properties=FALSE, xs, ...)
computeFeatures.haralick(x, ref , properties=FALSE, haralick.nbins=32, haralick.scales=c(1, 2), xs, ...)
standardExpandRef(ref, refnames, filter = gblob())

Arguments

Details

Features are named x.y.f, where x is the object layer, y the reference image layer and f the feature name. Examples include cell.dna.mean , indicating mean DNA intensity computed in the cell or nucleus.tubulin.cx , indicating the x center of mass of tubulin computed in the nucleus region.

The function computeFeatures computes sets of features. Features are organized in 4 sets, each computed by a different function. The function computeFeatures.basic computes spatial-independent statistics on pixel intensities:

• b.mean: mean intensity

• b.sd: standard deviation intensity

• b.mad: mad intensity

• b.q*: quantile intensity

The function computeFeatures.shape computes features that quantify object shape:

• s.area: area size (in pixels)

• s.perimeter: perimeter (in pixels)

• s.radius.mean: mean radius (in pixels)

• s.radius.sd: standard deviation of the mean radius (in pixels)

• s.radius.max: max radius (in pixels)

• s.radius.min: min radius (in pixels)

The function computeFeatures.moment computes features related to object image moments, which can be computed with or without reference intensities:

• m.cx: center of mass x (in pixels)

• m.cy: center of mass y (in pixels)

• m.majoraxis: elliptical fit major axis (in pixels)

• m.eccentricity: elliptical eccentricity defined by sqrt(1-minoraxis^2/majoraxis^2). Circle eccentricity is 0 and straight line eccentricity is 1.

• m.theta: object angle (in radians)

The function computeFeatures.haralick computes features that quantify pixel texture. Features are named according to Haralick's original paper.

Value

If properties if FALSE (by default), computeFeatures returns a matrix of n cells times p features, where p depends of the options given to the function. Returns NULL if no object is present.

If properties if TRUE , computeFeatures returns a matrix of p features times 2 properties (translation and rotation invariance). Feature properties are useful to filter out features that may not be needed for specific tasks, e.g. cell position when doing cell classification.

Seealso

bwlabel , propagate

Author

Gregoire Pau, gregoire.pau@embl.de , 2011

References

R. M. Haralick, K Shanmugam and Its'Hak Deinstein (1979). list("Textural Features for Image ", " Classification") . IEEE Transactions on Systems, Man and Cybernetics.

Examples

## load and segment nucleus
y = readImage(system.file("images", "nuclei.tif", package="EBImage"))[,,1]
x = thresh(y, 10, 10, 0.05)
x = opening(x, makeBrush(5, shape='disc'))
x = bwlabel(x)
display(y, title="Cell nuclei")
display(x, title="Segmented nuclei")

## compute shape features
fts = computeFeatures.shape(x)
fts

## compute features
ft = computeFeatures(x, y, xname="nucleus")
cat("median features are:
")
apply(ft, 2, median)

## compute feature properties
ftp = computeFeatures(x, y, properties=TRUE, xname="nucleus")
ftp

Image Display

Description

Display images in an interactive JavaScript viewer or using R's built-in graphics capabilities.

Usage

display(x, method, ...)
list(list("plot"), list("Image"))(x, ...)

Arguments

Details

The default method used for displaying images depends on whether called from and interactive R session. If interactive() is TRUE images are displayed with the "browser" method, otherwise the "raster" method is used. This dynamic behavior can be overridden by setting options("EBImage.display") to either "browser" or "raster" .

plot.Image S3 method is a wrapper for display(..., method="raster")

Seealso

display-shiny

Author

Andrzej Oles, andrzej.oles@embl.de , 2012-2017

Examples

## Display a single image
x = readImage(system.file("images", "sample-color.png", package="EBImage"))[257:768,,]
display(x)

## Display a thresholded sequence ...
y = readImage(system.file("images", "sample.png", package="EBImage"))[366:749, 58:441]
z = lapply(seq(from=0.5, to=5, length=6),
function(s) gblur(y, s, boundary="replicate")
)
z = combine(z)

## ... using the interactive viewer ...
display(z)

## ... or using R's build-in raster device
display(z, method = "raster", all = TRUE)

## Display the last frame
display(z, method = "raster", frame = numberOfFrames(z, type = "render"))

## Customize grid appearance
display(z, method = "raster", all = TRUE,
nx = 2, spacing = 0.05, margin = 20, bg = "black")

Shiny Bindings for display

Description

Output and render functions for using the interactive image viewer within Shiny applications and interactive R Markdown documents.

Usage

displayOutput(outputId, width = "100%", height = "500px")
renderDisplay(expr, env = parent.frame(), quoted = FALSE)

Arguments

Seealso

display

Examples

# Only run this example in interactive R sessions
if (interactive()) {
options(device.ask.default = FALSE)

require("shiny")

ui <- fluidPage(

# Application title
titlePanel("Image display"),

# Sidebar with a select input for the image
sidebarLayout(
sidebarPanel(
selectInput("image", "Sample image:", list.files(system.file("images", package="EBImage")))
),

# Show a plot of the generated distribution
mainPanel(
tabsetPanel(
tabPanel("Static raster", plotOutput("raster")),
tabPanel("Interactive browser", displayOutput("widget"))
)
)
)

)

server <- function(input, output) {

img <- reactive({
f = system.file("images", input$image, package="EBImage")
readImage(f)
})

output$widget <- renderDisplay({
display(img())
})

output$raster <- renderPlot({
plot(img(), all=TRUE)
})

}

# Run the application
shinyApp(ui = ui, server = server)
}

Distance map transform

Description

Computes the distance map transform of a binary image. The distance map is a matrix which contains for each pixel the distance to its nearest background pixel.

Usage

distmap(x, metric=c('euclidean', 'manhattan'))

Arguments

Details

A fast algorithm of complexity O(MNlog(max(M,N))), where (M,N) are the dimensions of x , is used to compute the distance map.

Value

An Image object or an array, with pixels containing the distances to the nearest background points.

Author

Gregoire Pau, gpau@ebi.ac.uk , 2008

References

M. N. Kolountzakis, K. N. Kutulakos. Fast Computation of the Euclidean Distance Map for Binary Images, Infor. Proc. Letters 43 (1992).

Examples

x = readImage(system.file("images", "shapes.png", package="EBImage"))
display(x)
dx = distmap(x)
display(dx/10, title='Distance map of x')

Draw a circle on an image.

Description

Draw a circle on an image.

Usage

drawCircle(img, x, y, radius, col, fill=FALSE, z=1)

Arguments

Value

An Image object or an array, containing the transformed version of img .

Author

Gregoire Pau, 2010

Examples

## Simple white circle
x = matrix(0, nrow=300, ncol=300)
y = drawCircle(x, 100, 200, 47, col=1)
display(y)

## Simple filled yellow circle
x = channel(y, 'rgb')
y = drawCircle(x, 200, 140, 57, col='yellow', fill=TRUE)
display(y)

Histogram Equalization

Description

Equalize the image histogram to a specified range and number of levels.

Usage

equalize(x, range = c(0, 1), levels = 256)

Arguments

Details

Histogram equalization is an adaptive image contrast adjustment method. It flattens the image histogram by performing linearization of the cumulative distribution function of pixel intensities.

Individual channels of Color images and frames of image stacks are equalized separately.

Value

An Image object or an array, containing the transformed version of x .

Seealso

clahe

Author

Andrzej Oles, andrzej.oles@embl.de , 2014

Examples

x = readImage(system.file('images', 'cells.tif', package='EBImage'))
hist(x)
y = equalize(x)
hist(y)
display(y, title='Equalized Grayscale Image')

x = readImage(system.file('images', 'sample-color.png', package='EBImage'))
hist(x)
y = equalize(x)
hist(y)
display(y, title='Equalized Grayscale Image')

Fill holes in objects

Description

Fill holes in objects.

Usage

fillHull(x)

Arguments

Details

fillHull fills holes in the objects defined in x , where objects are sets of pixels with the same unique integer value.

Value

An Image object or an array, containing the transformed version of x .

Seealso

bwlabel

Author

Gregoire Pau, Oleg Sklyar; 2007

Examples

x = readImage(system.file('images', 'nuclei.tif', package='EBImage'))
display(x)

y = thresh(x, 10, 10, 0.05)
display(y, title='Cell nuclei')

y = fillHull(y)
display(y, title='Cell nuclei without holes')

2D Convolution Filter

Description

Filters an image using the fast 2D FFT convolution product.

Usage

filter2(x, filter, boundary = c("circular", "replicate"))

Arguments

Details

Linear filtering is useful to perform low-pass filtering (to blur images, remove noise...) and high-pass filtering (to detect edges, sharpen images). The function makeBrush is useful to generate filters.

The default "circular" behaviour at boundaries is to wrap the image around borders. In the "replicate" mode pixels outside the bounds of the image are assumed to equal the nearest border pixel value. Numeric values of boundary yield linear convolution by padding the image with the given value(s).

If x contains multiple frames, the filter is applied separately to each frame.

Value

An Image object or an array, containing the filtered version of x .

Seealso

makeBrush , convolve , fft , blur

Author

Andrzej Oleś, Gregoire Pau

Examples

x = readImage(system.file("images", "sample-color.png", package="EBImage"))
display(x, title='Sample')

## Low-pass disc-shaped filter
f = makeBrush(21, shape='disc', step=FALSE)
display(f, title='Disc filter')
f = f/sum(f)
y = filter2(x, f)
display(y, title='Filtered image')

## Low-pass filter with linear padded boundary
y = filter2(x, f, boundary=c(0,.5,1))
display(y, title='Filtered image with linear padded boundary')

## High-pass Laplacian filter
la = matrix(1, nc=3, nr=3)
la[2,2] = -8
y = filter2(x, la)
display(y, title='Filtered image')

## High-pass Laplacian filter with replicated boundary
y = filter2(x, la, boundary='replicate')
display(y, title='Filtered image with replicated boundary')

Region filling

Description

Fill regions in images.

Usage

floodFill(x, pts, col, tolerance=0)

Arguments

Details

Flood fill is performed using the fast scan line algorithm. Filling starts at pts and grows in connected areas where the absolute difference of the pixels intensities (or colors) remains below tolerance .

Value

An Image object or an array, containing the transformed version of x .

Author

Gregoire Pau, Oleg Sklyar; 2007

Examples

x = readImage(system.file("images", "shapes.png", package="EBImage"))

## fill a shape with 50% shade of gray
y = floodFill(x, c(67, 146), 0.5)
display(y)

## fill with color
y = toRGB(y)
y = floodFill(y, c(48, 78), 'orange')
display(y)

## fill multiple shapes with different colors
y = y[110:512,1:130,]
points = rbind(c(50, 50), c(100, 50), c(150, 50))
colors = c("red", "green", "blue")
y = floodFill(y, points, colors)
display(y)

## area fill
x = readImage(system.file("images", "sample.png", package="EBImage"))
y = floodFill(x, rbind(c(200, 400), c(200, 325)), 1, tolerance=0.1)
display(y)

## application to image stacks
f = system.file("images", "nuclei.tif", package="EBImage")
x = readImage(f)[1:250,1:250,]
x = opening(thresh(x, 12, 12), makeBrush(5, shape='disc'))
xy = lapply(getFrames(bwlabel(x)), function(x) computeFeatures.moment(x)[,1:2])
y = floodFill(toRGB(x), xy, c("red", "green", "blue"))
display(y)

Low-pass Gaussian filter

Description

Filters an image with a low-pass Gaussian filter.

Usage

gblur(x, sigma, radius = 2 * ceiling(3 * sigma) + 1, ...)

Arguments

Details

The Gaussian filter is created with the function makeBrush .

Value

An Image object or an array, containing the filtered version of x .

Seealso

filter2 , makeBrush

Author

Oleg Sklyar, osklyar@ebi.ac.uk , 2005-2007

Examples

x = readImage(system.file("images", "sample.png", package="EBImage"))
display(x)

y = gblur(x, sigma=8)
display(y, title='gblur(x, sigma=8)')

Image I/O

Description

Read images from files and URLs, and write images to files.

Usage

readImage(files, type, all = TRUE, names = sub("\.[^.]*$", "", basename(files)), list())
writeImage(x, files, type, quality = 100, bits.per.sample, compression = "none", list())

Arguments

Details

readImage loads all images from the files vector and returns them stacked into a single Image object containing an array of doubles ranging from 0 (black) to 1 (white). All images need to be of the same type and have the same dimensions and color mode. If type is missing, the appropriate file format is determined from file name extension. Color mode is determined automatically based on the number of channels. When the function fails to read an image it skips to the next element of the files vector issuing a warning message. Non-local files can be read directly from a valid URL.

writeImage writes images into files specified by files , were the number of files needs to be equal 1 or the number of frames. Given an image containing multiple frames and a single file name either the whole stack is written into a single TIFF file, or each frame is saved to an individual JPEG/PNG file (for files = "image.*" frames are saved into image-X.* files, where X equals the frame number less one; for an image containing n frames this results in file names numbered from 0 to n-1 ).

When writing JPEG files the compression quality can be specified using quality . Valid values range from 100 (highest quality) to 1 (lowest quality). For TIFF files additional information about the desired number of bits per sample ( bits.per.sample ) and the compression algorithm ( compression ) can be provided. For a complete list of supported values please consult the documentation of the tiff package.

Value

readImage returns a new Image object.

writeImage returns an invisible vector of file names.

Seealso

Image , display , readJPEG / writeJPEG , readPNG / writePNG , readTIFF / writeTIFF

Note

Image formats have a limited dynamic range (e.g. JPEG: 8 bit, TIFF: 16 bit) and writeImage may cause some loss of accuracy. In specific, writing 16 bit image data to formats other than TIFF will strip the 8 LSB. When writing TIFF files a dynamic range check is performed and an appropriate value of bits.per.sample is set automatically.

Author

Andrzej Oles, andrzej.oles@embl.de , 2012

Examples

## Read and display an image
f = system.file("images", "sample-color.png", package="EBImage")
x = readImage(f)
display(x)

## Read and display a multi-frame TIFF
y = readImage(system.file("images", "nuclei.tif", package="EBImage"))
display(y)

## Read an image directly from a remote location by specifying its URL
try({
im = readImage("http://www-huber.embl.de/EBImage/ExampleImages/berlin.tif")
display(im, title = "Berlin Impressions")
})

## Convert a PNG file into JPEG
tempfile = tempfile("", , ".jpeg")
writeImage(x, tempfile, quality = 85)
cat("Converted '", f, "' into '", tempfile, "'.
", sep="")

## Save a frame sequence
files = writeImage(y, tempfile("", , ".jpeg"), quality = 85)
cat("Files created: ", files, sep="
")

Local Curvature

Description

Computes signed curvature along a line.

Usage

localCurvature(x, h, maxk)

Arguments

Details

localCurvature fits a local non-parametric smoothing line (polynomial of degree 2) at each point along the line segment, and computes the curvature locally using numerical derivatives.

Value

Returns a list containing the contour coordinates x , the signed curvature at each point curvature and the arc length of the contour length .

Seealso

ocontour

Author

Joseph Barry, Wolfgang Huber, 2013

Examples

## curvature goes as the inverse of the radius of a circle
range=seq(3.5,33.5,by=2)
plotRange=seq(0.5,33.5,length=100)
circleRes=array(dim=length(range))
names(circleRes)=range
for (i in  seq_along(1:length(range))) {
y=as.Image(makeBrush('disc', size=2*range[i]))
y=ocontour(y)[[1]]
circleRes[i]=abs(mean(localCurvature(x=y,h=range[i])$curvature, na.rm=TRUE))
}
plot(range, circleRes, ylim=c(0,max(circleRes, na.rm=TRUE)), xlab='Circle Radius', ylab='Curvature', type='p', xlim=range(plotRange))
points(plotRange, 1/plotRange, type='l')

## Calculate curvature
x = readImage(system.file("images", "shapes.png", package="EBImage"))[25:74, 60:109]
x = resize(x, 200)
y = gblur(x, 3) > .3
display(y)

contours = ocontour(bwlabel(y))
c = localCurvature(x=contours[[1]], h=11)
i = c$curvature >= 0
pos = neg = array(0, dim(x))
pos[c$contour[i,]+1]  = c$curvature[i]
neg[c$contour[!i,]+1] = -c$curvature[!i]
display(10*(rgbImage(pos, , neg)), title = "Image curvature")

2D constant time median filtering

Description

Process an image using Perreault's modern constant-time median filtering algorithm [1, 2].

Usage

medianFilter(x, size, cacheSize=512)

Arguments

Details

Median filtering is useful as a smoothing technique, e.g. in the removal of speckling noise.

For a filter of radius size , the median kernel is a 2*size+1 times 2*size+1 square.

The input image x should contain pixel values in the range from 0 to 1, inclusive; values lower than 0 or higher than 1 are clipped before applying the filter. Internal processing is performed using 16-bit precision. The behavior at image boundaries is such as the source image has been padded with pixels whose values equal the nearest border pixel value.

If the image contains multiple channels or frames, the filter is applied to each one of them separately.

Value

An Image object or an array, containing the filtered version of x .

Seealso

gblur

Author

Joseph Barry, joseph.barry@embl.de , 2012

Andrzej Oleś, andrzej.oles@embl.de , 2016

References

[1] S. Perreault and P. Hebert, "Median Filtering in Constant Time", IEEE Trans Image Process 16(9), 2389-2394, 2007

[2] http://nomis80.org/ctmf.html

Examples

x = readImage(system.file("images", "nuclei.tif", package="EBImage"))
display(x, title='Nuclei')
y = medianFilter(x, 5)
display(y, title='Filtered nuclei')

Perform morphological operations on images

Description

Functions to perform morphological operations on binary and grayscale images.

Usage

dilate(x, kern)
erode(x, kern)
opening(x, kern)
closing(x, kern)
whiteTopHat(x, kern)
blackTopHat(x, kern)
selfComplementaryTopHat(x, kern)
makeBrush(size, shape=c('box', 'disc', 'diamond', 'Gaussian', 'line'), step=TRUE, sigma=0.3, angle=45)

Arguments

Details

dilate applies the mask kern by positioning its center over every pixel of the image x , the output value of the pixel is the maximum value of x covered by the mask. In case of binary images this is equivalent of putting the mask over every background pixel, and setting it to foreground if any of the pixels covered by the mask is from the foreground.

erode applies the mask kern by positioning its center over every pixel of the image x , the output value of the pixel is the minimum value of x covered by the mask. In case of binary images this is equivalent of putting the mask over every foreground pixel, and setting it to background if any of the pixels covered by the mask is from the background.

opening is an erosion followed by a dilation and closing is a dilation followed by an erosion.

whiteTopHat returns the difference between the original image x and its opening by the structuring element kern .

blackTopHat subtracts the original image x from its closing by the structuring element kern .

selfComplementaryTopHat is the sum of the whiteTopHat and the blackTopHat , simplified the difference between the closing and the opening of the image.

makeBrush generates brushes of various sizes and shapes that can be used as structuring elements.

list(list("Processing Pixels at Image Borders (Padding Behavior)"), list(" ", " Morphological functions position the center of the structuring element over each pixel in the input image. For pixels close to the edge of an image, parts of the neighborhood defined by the structuring element may extend past the border of the image. In such a case, a value is assigned to these undefined pixels, as if the image was padded with additional rows and columns. The value of these padding pixels varies for dilation and erosion operations. For dilation, pixels beyond the image border are assigned the minimum value afforded by the data type, which in case of binary images is equivalent of setting them to background. For erosion, pixels beyond the image border are assigned the maximum value afforded by the data type, which in case of binary images is equivalent of setting them to foreground."))

Value

dilate , erode , opening , whiteTopHat , blackTopHat and selfComplementaryTopHat return the transformed Image object or array x , after the corresponding morphological operation.

makeBrush generates a 2D matrix containing the desired brush.

Note

Morphological operations are implemented using the efficient Urbach-Wilkinson algorithm [1]. Its required computing time is independent of both the image content and the number of gray levels used.

Author

Ilia Kats < ilia-kats@gmx.net > (2012), Andrzej Oles < andrzej.oles@embl.de > (2015)

References

[1] E. R. Urbach and M.H.F. Wilkinson, "Efficient 2-D grayscale morphological transformations with arbitrary flat structuring elements", IEEE Trans Image Process 17(1), 1-8, 2008

Examples

x = readImage(system.file("images", "shapes.png", package="EBImage"))
kern = makeBrush(5, shape='diamond')

display(x)
display(kern, title='Structuring element')
display(erode(x, kern), title='Erosion of x')
display(dilate(x, kern), title='Dilatation of x')

## makeBrush
display(makeBrush(99, shape='diamond'))
display(makeBrush(99, shape='disc', step=FALSE))
display(2000*makeBrush(99, shape='Gaussian', sigma=10))

Intensity values linear scaling

Description

Linearly scale the intensity values of an image to a specified range.

Usage

list(list("normalize"), list("Image"))(object, separate=TRUE, ft=c(0,1), inputRange)%list(list("normalize"), list("array"))(object, separate=TRUE, ft=c(0,1), inputRange)

Arguments

Details

normalize performs linear interpolation of the intensity values of an image to the specified range ft . If inputRange is not set the whole dynamic range of the image is used as input. By specifying inputRange the input intensity range of the image can be limited to [min, max]. Values exceeding this range are clipped, i.e. intensities lower/higher than min / max are set to min / max .

Value

An Image object or an array, containing the transformed version of object .

Author

Oleg Sklyar, osklyar@ebi.ac.uk , 2006-2007 Andrzej Oles, andrzej.oles@embl.de , 2013

Examples

x = readImage(system.file('images', 'shapes.png', package='EBImage'))
x = x[110:512,1:130]
y = bwlabel(x)
display(x, title='Original')

print(range(y))
y = normalize(y)
print(range(y))
display(y, title='Segmented')

Oriented contours

Description

Computes the oriented contour of objects.

Usage

ocontour(x)

Arguments

Value

A list of matrices, containing the coordinates of object oriented contours.

Author

Gregoire Pau, gpau@ebi.ac.uk , 2008

Examples

x = readImage(system.file("images", "shapes.png", package="EBImage"))
x = x[1:120,50:120]
display(x)
oc = ocontour(x)
plot(oc[[1]], type='l')
points(oc[[1]], col=2)

Calculate Otsu's threshold

Description

Returns a threshold value based on Otsu's method, which can be then used to reduce the grayscale image to a binary image.

Usage

otsu(x, range = c(0, 1), levels = 256)

Arguments

Details

Otsu's thresholding method [1] is useful to automatically perform clustering-based image thresholding. The algorithm assumes that the distribution of image pixel intensities follows a bi-modal histogram, and separates those pixels into two classes (e.g. foreground and background). The optimal threshold value is determined by minimizing the combined intra-class variance.

The threshold value is calculated for each image frame separately resulting in a output vector of length equal to the total number of frames in the image.

The default number of levels corresponds to the number of gray levels of an 8bit image. It is recommended to adjust this value according to the bit depth of the processed data, i.e. set levels to 2^16 = 65536 when working with 16bit images.

Value

A vector of length equal to the total number of frames in x . Each vector element contains the Otsu's threshold value calculated for the corresponding image frame.

Seealso

thresh

Author

Philip A. Marais philipmarais@gmail.com , Andrzej Oles andrzej.oles@embl.de , 2014

References

[1] Nobuyuki Otsu, "A threshold selection method from gray-level histograms". IEEE Trans. Sys., Man., Cyber. 9 (1): 62-66. doi:10.1109/TSMC.1979.4310076 (1979)

Examples

x = readImage(system.file("images", "sample.png", package="EBImage"))
display(x)

## threshold using Otsu's method
y = x > otsu(x)
display(y)

Mark objects in images

Description

Higlight objects in images by outlining and/or painting them.

Usage

paintObjects(x, tgt, opac=c(1, 1), col=c('red', NA), thick=FALSE, closed=FALSE)

Arguments

Value

An Image object or an array, containing the painted version of tgt .

Seealso

bwlabel , watershed , computeFeatures , colorLabels

Author

Oleg Sklyar, osklyar@ebi.ac.uk , 2006-2007 Andrzej Oles, andrzej.oles@embl.de , 2015

Examples

## load images
nuc = readImage(system.file('images', 'nuclei.tif', package='EBImage'))
cel = readImage(system.file('images', 'cells.tif', package='EBImage'))
img = rgbImage(green=cel, blue=nuc)
display(img, title='Cells')

## segment nuclei
nmask = thresh(nuc, 10, 10, 0.05)
nmask = opening(nmask, makeBrush(5, shape='disc'))
nmask = fillHull(nmask)
nmask = bwlabel(nmask)
display(normalize(nmask), title='Cell nuclei mask')

## segment cells, using propagate and nuclei as 'seeds'
ctmask = opening(cel>0.1, makeBrush(5, shape='disc'))
cmask = propagate(cel, nmask, ctmask)
display(normalize(cmask), title='Cell mask')

## using paintObjects to highlight objects
res = paintObjects(cmask, img, col='#ff00ff')
res = paintObjects(nmask, res, col='#ffff00')
display(res, title='Segmented cells')

Voronoi-based segmentation on image manifolds

Description

Find boundaries between adjacent regions in an image, where seeds have been already identified in the individual regions to be segmented. The method finds the Voronoi region of each seed on a manifold with a metric controlled by local image properties. The method is motivated by the problem of finding the borders of cells in microscopy images, given a labelling of the nuclei in the images.

Algorithm and implementation are from Jones et al. [1].

Usage

propagate(x, seeds, mask=NULL, lambda=1e-4)

Arguments

Details

The method operates by computing a discretized approximation of the Voronoi regions for given seed points on a Riemann manifold with a metric controlled by local image features.

Under this metric, the infinitesimal distance d between points v and v+dv is defined by: d^2 = ( (t(dv)g)^2 + lambdat(dv)*dv )/(lambda + 1) , where g is the gradient of image x at point v.

lambda controls the weight of the Euclidean distance term. When lambda tends to infinity, d tends to the Euclidean distance. When lambda tends to 0, d tends to the intensity gradient of the image.

The gradient is computed on a neighborhood of 3x3 pixels.

Segmentation of the Voronoi regions in the vicinity of flat areas (having a null gradient) with small values of lambda can suffer from artifacts coming from the metric approximation.

Value

An Image object or an array, containing the labelled objects.

Seealso

bwlabel , watershed

Author

The original CellProfiler code is from Anne Carpenter carpenter@wi.mit.edu, Thouis Jones thouis@csail.mit.edu, In Han Kang inthek@mit.edu. Responsible for this implementation: Greg Pau.

References

[1] T. Jones, A. Carpenter and P. Golland, "Voronoi-Based Segmentation of Cells on Image Manifolds", CVBIA05 (535-543), 2005

[2] A. Carpenter, T.R. Jones, M.R. Lamprecht, C. Clarke, I.H. Kang, O. Friman, D. Guertin, J.H. Chang, R.A. Lindquist, J. Moffat, P. Golland and D.M. Sabatini, "CellProfiler: image analysis software for identifying and quantifying cell phenotypes", Genome Biology 2006, 7:R100

[3] CellProfiler: http://www.cellprofiler.org

Examples

## a paraboloid mountain in a plane
n = 400
x = (n/4)^2 - matrix(
(rep(1:n, times=n) - n/2)^2 + (rep(1:n, each=n) - n/2)^2,
nrow=n, ncol=n)
x = normalize(x)

## 4 seeds
seeds = array(0, dim=c(n,n))
seeds[51:55, 301:305] = 1
seeds[301:305, 101:105] = 2
seeds[201:205, 141:145] = 3
seeds[331:335, 351:355] = 4

lambda = 10^seq(-8, -1, by=1)
segmented = Image(dim=c(dim(x), length(lambda)))

for(i in seq_along(lambda)) {
prop = propagate(x, seeds, lambda=lambda[i])
prop = prop/max(prop)
segmented[,,i] = prop
}

display(x, title='Image')
display(seeds/max(seeds), title='Seeds')
display(segmented, title="Voronoi regions", all=TRUE)

Object removal and re-indexation

Description

The rmObjects functions deletes objects from an image by setting their pixel intensity values to 0. reenumerate re-enumerates all objects in an image from 0 (background) to the actual number of objects.

Usage

rmObjects(x, index, reenumerate = TRUE)
  reenumerate(x)

Arguments

Value

An Image object or an array, containing the new objects.

Seealso

bwlabel , watershed

Author

Oleg Sklyar, osklyar@ebi.ac.uk , 2006-2007

Examples

## make objects
x = readImage(system.file('images', 'shapes.png', package='EBImage'))
x = x[110:512,1:130]
y = bwlabel(x)

## number of objects found
max(y)

display(normalize(y), title='Objects')

## remove every second letter
objects = list(
seq.int(from = 2, to = max(y), by = 2),
seq.int(from = 1, to = max(y), by = 2)
)
z = rmObjects(combine(y, y), objects)

display(normalize(z), title='Object removal')

## the number of objects left in each image
apply(z, 3, max)

## perform object removal without re-enumerating
z = rmObjects(y, objects, reenumerate = FALSE)

## labels of objects left
unique(as.vector(z))[-1L]

## re-index objects
z = reenumerate(z)
unique(as.vector(z))[-1L]

Spatial linear transformations

Description

The following functions perform all spatial linear transforms: reflection, rotation, translation, resizing, and general affine transform.

Usage

flip(x)
flop(x)
rotate(x, angle, filter = "bilinear", output.dim, output.origin, ...)
translate(x, v, filter = "none", ...)
resize(x, w, h, output.dim = c(w, h), output.origin = c(0, 0), antialias = FALSE, ...)
affine(x, m, filter = c("bilinear", "none"), output.dim, bg.col = "black", antialias = TRUE)

Arguments

Details

flip mirrors x around the image horizontal axis (vertical reflection).

flop mirrors x around the image vertical axis (horizontal reflection).

rotate rotates the image clockwise by the given angle around the origin specified in output.origin . If no output.origin is provided, the result will be centered in a recalculated bounding box unless output.dim is provided.

resize scales the image x to the desired dimensions. The transformation origin can be specified in output.origin . For example, zooming about the output.origin can be achieved by setting output.dim to a value different from c(w, h) .

affine returns the affine transformation of x , where pixels coordinates, denoted by the matrix px , are transformed to cbind(px, 1)%*%m .

All spatial transformations except flip and flop are based on the general affine transformation. Spatial interpolation can be either none , also called nearest neighbor, where the resulting pixel value equals to the closest pixel value, or bilinear , where the new pixel value is computed by bilinear approximation of the 4 neighboring pixels. The bilinear filter gives smoother results.

Value

An Image object or an array, containing the transformed version of x .

Seealso

transpose

Author

Gregoire Pau, 2012

Examples

x <- readImage(system.file("images", "sample.png", package="EBImage"))
display(x)

display( flip(x) )
display( flop(x) )
display( resize(x, 128) )
display( rotate(x, 30) )
display( translate(x, c(120, -20)) )

m <- matrix(c(0.6, 0.2, 0, -0.2, 0.3, 300), nrow=3)
display( affine(x, m) )

Places detected objects into an image stack

Description

Places detected objects into an image stack.

Usage

stackObjects(x, ref, combine=TRUE, bg.col='black', ext)

Arguments

Details

stackObjects creates a set of n images of size ( 2*ext+1 , 2*ext+1 ), where n is the number of objects in x , and places each object of x in this set.

If not specified, ext is estimated using the 98% quantile of m.majoraxis/2, where m.majoraxis is the semi-major axis descriptor extracted from computeFeatures.moment , taken over all the objects of the image x .

Value

An Image object containing the stacked objects contained in x . If x contains multiple images and if combine is TRUE , stackObjects returns a list of Image objects.

Seealso

combine , tile , computeFeatures.moment

Author

Oleg Sklyar, osklyar@ebi.ac.uk , 2006-2007

Examples

## simple example
x = readImage(system.file('images', 'shapes.png', package='EBImage'))
x = x[110:512,1:130]
y = bwlabel(x)
display(normalize(y), title='Objects')
z = stackObjects(y, normalize(y))
display(z, title='Stacked objects')

## load images
nuc = readImage(system.file('images', 'nuclei.tif', package='EBImage'))
cel = readImage(system.file('images', 'cells.tif', package='EBImage'))
img = rgbImage(green=cel, blue=nuc)
display(img, title='Cells')

## segment nuclei
nmask = thresh(nuc, 10, 10, 0.05)
nmask = opening(nmask, makeBrush(5, shape='disc'))
nmask = fillHull(bwlabel(nmask))

## segment cells, using propagate and nuclei as 'seeds'
ctmask = opening(cel>0.1, makeBrush(5, shape='disc'))
cmask = propagate(cel, nmask, ctmask)

## using paintObjects to highlight objects
res = paintObjects(cmask, img, col='#ff00ff')
res = paintObjects(nmask, res, col='#ffff00')
display(res, title='Segmented cells')

## stacked cells
st = stackObjects(cmask, img)
display(st, title='Stacked objects')

Adaptive thresholding

Description

Thresholds an image using a moving rectangular window.

Usage

thresh(x, w=5, h=5, offset=0.01)

Arguments

Details

This function returns the binary image resulting from the comparison between an image and its filtered version with a rectangular window. It is equivalent of doing { but faster. The function filter2 provides hence more flexibility than thresh .

Value

An Image object or an array, containing the transformed version of x .

Seealso

filter2

Author

Oleg Sklyar, osklyar@ebi.ac.uk , 2005-2007

Examples

x = readImage(system.file('images', 'nuclei.tif', package='EBImage'))
display(x)
y = thresh(x, 10, 10, 0.05)
display(y)

Tiling/untiling images

Description

Given a sequence of frames, tile generates a single image with frames tiled. untile is the inverse function and divides an image into a sequence of images.

Usage

tile(x, nx=10, lwd=1, fg.col="#E4AF2B", bg.col="gray")
untile(x, nim, lwd=1)

Arguments

Details

After object segmentation, tile is a useful addition to stackObjects to have an overview of the segmented objects.

Value

An Image object or an array, containing the tiled/untiled version of x .

Seealso

stackObjects

Author

Oleg Sklyar, osklyar@ebi.ac.uk , 2006-2007

Examples

## make a set of blurred images
img = readImage(system.file("images", "sample-color.png", package="EBImage"))[257:768,,]
x = resize(img, 128, 128)
xt = list()
for (t in seq(0.1, 5, len=9)) xt=c(xt, list(gblur(x, s=t)))
xt = combine(xt)
display(xt, title='Blurred images')

## tile
xt = tile(xt, 3)
display(xt, title='Tiles')

## untile
xu = untile(img, c(3, 3))
display(xu, title='Blocks')

Image Transposition

Description

Transposes an image by swapping its spatial dimensions.

Usage

transpose(x)

Arguments

Details

The transposition of an image is performed by swapping the X and Y indices of its array representation.

Value

A transformed version of x with its first two dimensions transposed.

Seealso

flip , flop , rotate

Note

The function is implemented using an efficient cash-oblivious algorithm which is typically faster than R's aperm and t functions.

Author

Andrzej Oles, andrzej.oles@embl.de , 2012-2017

Examples

x = readImage(system.file("images", "sample-color.png", package="EBImage"))
y = transpose(x)

display(x, title='Original')
display(y, title='Transposed')

## performing the transposition of an image twice should result in the original image
z = transpose(y)
identical(x, z)

Watershed transformation and watershed based object detection

Description

Watershed transformation and watershed based object detection.

Usage

watershed(x, tolerance=1, ext=1)

Arguments

Details

The algorithm identifies and separates objects that stand out of the background (zero). It inverts the image and uses water to fill the resulting valleys (pixels with high intensity in the source image) until another object or background is met. The deepest valleys become indexed first, starting from 1.

The function bwlabel is a simpler, faster alternative to segment connected objects from binary images.

Value

An Grayscale Image object or an array, containing the labelled version of x .

Seealso

bwlabel , propagate

Author

Oleg Sklyar, osklyar@ebi.ac.uk , 2007

Examples

x = readImage(system.file('images', 'shapes.png', package='EBImage'))
x = x[110:512,1:130]
display(x, title='Binary')
y = distmap(x)
display(normalize(y), title='Distance map')
w = watershed(y)
display(normalize(w), title='Watershed')