Linear operation have the disadvantage to do always something. The
problems with your method occur with neighbored cells and cells with
varying intensity profile. Or think about a ramp intensity background with
cells sitting on it. You might experience surprising results.
As long you need only a cell centre and not an exact mask of the cell
there might be no problem. However, take a look to the non-linear
operators.
KR
Am Wed, 07 Feb 2007 18:05:41 +0100 schrieb Brian Larsen
<balarsen@gmail.com>:
> In the past I have taken a different tack at this. I knew something
> about the shape and size of my blobs and about the character of the
> background. This gives all sorts of advantages in the background
> removal process and the blog detection.
>
> In my example the blobs were cells in a microscope picture and so they
> have a definite character, the camera was really bad making signal to
> noise like 1.2 or so. I needed to find the centers of the cells. I
> accomplished this by a Gaussian convolution over the image since the
> cell look kinda Gaussian and background did not.
>
> ; convolve the data with a gaussian kernel to look FOR gaussian like
> ; things cells are close enough for this
> ; Simple Gaussian kernel
> kernel = [$
> [ 1, 8, 15, 8, 1], $
> [ 8, 63,127, 63, 8], $
> [15,127,255,127,15], $
> [ 8, 63,127, 63, 8], $
> [ 1, 8, 15, 8, 1]]
> result = CONVOL( dat1, kernel, 4 )
>
> This has the affect of making the cells really bright and the
> background really dim. I could then subtract the background at 2
> sigma.
> bkgd = mean(result, /nan)
> bkgd_std = stddev(result, /nan)
> ;; all the noise should be less than mean+2stddev
> result -= (bkgd+2*bkgd_std)
>
> Leaving pretty close to just the cells.
>
> Then let label_region do all the work:
> mask = a ge 150
> ;; this names connected regions 0, 1, 2
> regions = label_region(mask)
> ind = where(regions eq 1)
> ;; find the center
> center = [mean(ind mod 256), mean(ind / 256)]
>
> and bang I had the centers really well. This seemed to work without
> fail on these images. Could be worth a look.
>
> Brian
>
> ------------------------------------------------------------ ---------
> Brian A. Larsen
> Dept. of Physics
> Space Science and Engineering Lab (SSEL)
> Montana State University - Bozeman
> Bozeman, MT 59717
>
>
>
>
>
> On Feb 6, 2:47 pm, "Karsten Rodenacker" <karsten.rodenac...@gsf.de>
> wrote:
>> You could also use morphological operations. E.G. to detect your blobs
>> apply a morph_tophat and an appropriate threshold. kernel or structuring
>> element should be slightly larger than your blobs. tophat consits of a
>> morphological smoothing (open) to generate so to say the background
>> which
>> is than subtracted from the original.
>> Tophat is relatively unknown but surprisingly effective.
>>
>> Regards
>> karsten
>>
>> Am Tue, 06 Feb 2007 18:25:24 +0100 schrieb rpert...@gmail.com
>> <rpert...@gmail.com>:
>>
>>
>>
>>> Hello,
>>> I am doing some image analysis, and my image consists of several
>>> bright spots that I need to detect. I was able to write a program that
>>> would do just that...find the pixels that are larger than a threshold,
>>> group close pixels together and label different blobs as different
>>> spots by marking a 'plus sign' on the spot. Except, it does not 'see'
>>> all the spots, and lowering the threshold results in 'seeing' spots
>>> that are not there. Therefore, I am considering some filtering that I
>>> need to do to my background as it is not uniform and was suggested to
>>> perform a gauss 1d or 2d to the background to subtract it (and exclude
>>> the spots as I do that), and then see if i can 'see' all the spots....
>>
>>> I am not sure how to do a gauss fit to background though...any
>>> suggestions?
>>> Thanks!
>>> rp
>>
>> --
>> Erstellt mit Operas revolutionï¿œrem E-Mail-Modul:http://www.opera.com/m2/
>
>
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Erstellt mit Operas revolutionï¿œrem E-Mail-Modul: http://www.opera.com/m2/
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