http://imagej.273.s1.nabble.com/retinal-vasculature-segmentation-tp5007798p5007802.html
> One of my current projects involves segmenting out the vasculature in
> images of
> human retina (these happen to be autofluorescence images, where the blood
> vessels
> appear as nearly black).
>
> Histograms of even very local pieces of image show two
> very overlapping populations of pixels - I have seen recently reported
> methods
> which simply choose a threshold based on these histograms.
> This is “close, but no cigar”, for our purposes.
>
> My current, crude approach uses auto local thresholding plus a bit of
> mathematical morphology,
> followed by manual editing. My current push is to make the manual
> editing phase a bit
> easier - but that part is under control.
>
> This approach is adequate for my collaborators’ current needs, so I’m now
> free to explore
> an implementation that will be completely automatic.
>
> While I’m happy with our current results, I’d like to compare with (and
> perhaps learn from)
> existing implementations done elsewhere.
>
> If you have an existing (successful) implementation in ImageJ, can you
> please point
> me at results (publications would be outstanding) and/or implementation
> (ImageJ macros
> or plugins) - or even just clues about the approach you have used.
>
> Single image, standard (or at least known) position and pose. The center
> of the fovea is known and near the center of the image, the position of the
> edge of the optic disc is known and near the edge (either to the right, or
> the left - if it helps the orientation can be trivially standardized).
> 8-bit gray input; binary output. Known issues are the overlap in pixel
> values for the two categories (blood vessel/NOT blood vessel), a noisy,
> dark avascular zone around the center of the fovea, and some falloff in
> both the signal AND the optics in the corners (and, to a lesser extent, at
> the edges).
> Ideally, I’d like to find all pixels completely covered by a blood
> vessel…and no others. Oh yes…there are also dark patches of image that are
> due to disease, and not blood vessels - so some shape analysis appears to
> be necessary.
> That’s what we use the manual editing for, now - both to thicken and
> extend blood vessels that are missed by the automatic procedure AND to
> erase patches accepted by the automatic procedure which are “obviously” NOT
> blood vessels.
> Unfortunately, this latter category is (of course) the most interesting,
> and where all the “results” will be found.
>
> --
> Kenneth Sloan
>
[hidden email]
>
> --
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