Adding bg option to watershed

[rtobar]

The work in this pull request comes from the fact that we are currently doing a watershed over slightly big images that need to be filtered first to cancel out some background noise.

To do that we do something like this:

> image[image < 1.5] = 0
> EBImage::watershed(image, tolerance=0)

The problem is that the cost of doing this filtering ourselves is a fair amount of the total cost:

> dim(image)
[1] 2848 2848

> microbenchmark({image[image < 1.5]=0}, times=100)
Unit: milliseconds
                       expr     min      lq     mean   median       uq     max neval
 { image[image < 1.5] = 0 } 62.7114 70.7209 105.7809 88.69216 101.6094 276.556   100

> microbenchmark(EBImage::watershed(image, tolerance = 0))
Unit: milliseconds
                                     expr      min       lq     mean   median       uq      max neval
 EBImage::watershed(image, tolerance = 0) 629.0318 660.6444 687.8925 676.6159 699.1388 865.2657   100

Based on the median and mean values of these measurements we are observing an approximate filtering cost of 10%-15%.

Doing the watershed with the internal filtering (what we are proposing in this pull request) we should be able to do:

> EBImage::watershed(image, tolerance = 0, bg=1.5)

The cost is negligible, as the current watershed algorithm is already filtering positive pixels:

> microbenchmark(EBImage::watershed(image, tolerance = 0, bg=1.5))
Unit: milliseconds
                                          expr      min       lq     mean   median       uq      max neval
 EBImage::watershed(image, tolerance=0, bg=1.5) 581.5359 645.1499 664.3851 660.9021 675.6802 770.4231   100