propagate {EBImage} | R Documentation

## 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, ext, seed.centers)

### Arguments

`x` | An `Image` object or an array, containing the image to segment.
`seeds` | An `Image` object or an array, containing the seeding objects of the already identified regions.
`mask` | An optional `Image` object or an array, containing the binary image mask of the regions that can be segmented. If missing, the whole image is segmented.
`lambda` | A numeric value. The regularisation parameter used in the metric, determining the trade-off between the Euclidian distance in the image plane and the contribution of the gradient of `x`. See details.
`ext` | Deprecated.
`seed.centers` | Deprecated.

### 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 + lambda*t(dv)*dv )/(lambda + 1)

, where g is the gradient of image `x` at point v.

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

To avoid to rely too much on single noisy pixels, 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 artefacts coming from the metric approximation.

### Value

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

### License

The implementation is based on CellProfiler C++ source code [2, 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, Gregoire Pau, Wolfgang Huber

### 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

### See Also

`bwlabel`, `watershed`

### 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
    }

    if(interactive()){
      display(x, title='Image')
      display(seeds/max(seeds), title='Seeds')
      display(segmented, title="Voronoi regions")
    }

[Package EBImage version 3.0.5 Index]