propagate {EBImage} | R Documentation

## Voronoi-based segmentation on image manifolds

### Description

R implementation of the Voronoi-based image segmentation on image manifolds [2].

### Usage

      ## S4 method for signature 'Image, IndexedImage':
      propagate(x, seeds, mask=NULL, lambda=0.1,
         ext=1, seed.centers=FALSE, ...)

### Arguments

`x` | An object of `Image` to be segmented, in the `Grayscale` mode.
`seeds` | An object of `IndexedImage` of the same size as x in all three dimensions. This image provides seed points for object detection.
`mask` | An object of `Image` of the same size as `x` in all three dimensions; in the `Grayscale` mode. All zero regions will be excluded from object detection.
`lambda` | A numeric value. The regularisation parameter for the distance calculations, determines the trade-off between the Euclidian distance in the image plane and the contribution of the gradient of the values in `x`. See details.
`ext` | Extension of the neighborhood to estimate image gradient, in pixels in every direction from the central point, i.e. ext=1 means a 3x3 neighborhood.
`seed.centers` | If `TRUE`, only centers of the seed points are left in the seeds image supplied to the propagate algorithm.
`...` | Reserved.

### Details

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

The metric is a Riemannian metric defined in terms of the image I and a regularization parameter lambda. With this metric the distance between pixels used to let the given seeds grow outwards (propagate) is

    d^2 = (grad(I)^2 + lambda * (dx^2 + dy^2)) / (lambda + 1)

The above formulation was proposed by Carpenter et al, however in the calculation we use a modified distance measture, in which sharp gradients are downregulated and large distances additionally penalized. Effectively, we use the following formula:

    d = sqrt(grad(I)) + 1e-3*lambda * (dx^2+dy^2)^2

The denominator is left out for speed reasons, so is the square root of the distance.

The gradient is calculated on a neighborhood of pixels (the width of which is controlled by the argument `ext`) to avoid relying on single (noisy) pixels. Lambda controls the weight of the Euclidian distance term. In case of large lambda, d turns into Euclidian distance in the `(x,y)`-plane. For small lambda, the distance will be dominated by the intensity gradient.

### Value

An image of `Image`, with the same object indexing as `seeds`. No new objects are created, only those specified by seeds are propagated. Use `getFeatures` to assign the feature matrix.

### License

The underlying `C++` code is based on code from CellProfiler [1,3]. An LGPL license was granted by Thouis Jones to use this part of CellProfiler's code for the `propagate` function.

### Author(s)

Original CellProfiler code: Anne Carpenter <carpenter@wi.mit.edu>, Thouis Jones <thouis@csail.mit.edu>, In Han Kang <inthek@mit.edu>.

Port for this package: Oleg Sklyar <osklyar@ebi.ac.uk> and Wolfgang Huber <huber@ebi.ac.uk>.

### References

`[1]` 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

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

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

### See Also

`IndexedImage, Image, watershed, getFeatures`

### Examples

      ## load images of nuclei (seed points later)
      f <- paste( system.file(package="EBImage"), "images/Gene1_G.tif", sep="/" )
      ii = readImage(f)
      ## normalize images
      ii = normalize(ii, separate=TRUE)
      ## segment
      mask = thresh(ii, 25, 25, 0.02)
      ## refine segmentation with morphology filters
      mk3 = morphKern(3)
      mk5 = morphKern(5)
      mask = dilate(erode(closing(mask, mk5), mk3), mk5)
      ## index objects with 'watershed'
      io = watershed( distmap(mask), 1.5, 1)
      if (interactive()) display(io)

      ## load images of cells (the ones to segment with propagate)
      f <- paste( system.file(package="EBImage"), "images/Gene1_R.tif", sep="/" )
      xi = readImage(f)
      ## normalize images
      xi = normalize(xi, separate=TRUE)
      ## segment
      mask = thresh(xi, 40, 40, 0.0)
      ## refine segmentation with morphology filters
      mk7 = morphKern(7)
      mask = dilate(erode(closing(mask, mk7), mk5), mk7)
      ## index objects of xi with 'propagate' using ii as seeds
      xo = propagate(xi, io, mask, 1e-2, 2)
      if (interactive()) display(xo)

[Package EBImage version 2.6.0 Index]