| Re: Label_region and Erosion [message #13408 is a reply to message #13283] |
Wed, 04 November 1998 00:00   |
Struan Gray
Messages: 178
Registered: December 1995
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Senior Member |
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Lisa Bryan, lbryanNOSPAM@arete-az.com writes:
> Now if I can just get dilate to work on a
> greyscale image.
I'm not sure you really need any help, but here are some random
thoughts.
As a general rule, IDL is much happier, and programs run much
faster, if you use as much memory and as few loops and sub-arrays as
possible. Unfortunately, the duty sadists at RSI have seen to it that
you have no decent tools within IDL to keep track of your memory
usage. However, your images are not too large, so keeping extra masks
and working copies of the image in memory is not going to be a problem
on most platforms, even if like me you need to keep users with
elderly, RAM-poor, PC-class machines in mind.
So you can always make a binary mask from your greyscale image,
and use that to define your regions. The most obvious way is to
manually set pixel values using the output of a WHERE call, but often
it is possible, and faster, to use a array comparison like:
mask = image > threshold_val
where threshold_val distinguishes between the regions (100 or so
in your example) and mask becomes a binary image containing 0s where
the condition is false and 1s where it is true. Then you can erode
and dilate this mask to get rid of single-pixel spikes and feed the
result to LABEL_REGION to find the regions themselves.
At this point I find it useful to make a *second* mask, which is a
n-times dilated version of the first. By subtracting the first from
the second I get a mask containing a n-pixel wide ribbon of boundary
pixels for the regions I want to filter. I can then use the ribbon to
fill the relevant pixels of a temporary copy of the original image
with values appropriate to whatever filter I want to apply (region
average, nearest value, NANs, whatever). Having filtered the copy of
the image containing the ad-hoc ribbon values I copy the filtered
regions back into the final image using the first mask as a guide.
This sounds complex, but is not hard to implement and is
particularly useful for things like differentiation filters where the
example you gave would produce nasty effects at the edges of the
dilated but unaltered regions where the values go from 300+/-10 to
0+/-10. My way puts all the nasties outside the region you are
actually going to use. Downsides are that if you want to filter both
the 300+/-10 areas and the 0+/-10 ones you have to do the whole thing
twice, and that the choice of pixel values to put into the ribbons,
while often perfectly logical and scientifically respectable, is a
fudge that might lead unsophisticated users astray, though the degree
to which that occurs depends on both the nature of your data, and of
your users.
Enough waffle. I hope this helps.
Struan
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