http://imagej.273.s1.nabble.com/Segmentation-of-DIC-or-Hoffman-Modulation-Contrast-images-help-please-tp5012661p5012668.html
Again - I’ll promote taking a higher level approach. Simple SEGMENTATION should be able to pull out “Bright blobs” and “Dark blobs” - segmenting the CELLS requires combining that evidence (and then perhaps going back to the image data in “verification vision” mode - making predictions about what the cell boundary will look like, and localizing it. But, this is probably beyond the scope of a script combining standard image processing operators.
> On May 1, 2015, at 09:10 , JOEL B. SHEFFIELD <
[hidden email]> wrote:
>
> Hi Jacqui,
>
> These images are particularly difficult to segment because the edges are
> assymetric --dark on one side, and light on the other. I was able to get
> some enhancement by using an unsharp mask (on your image, pixel radius of
> 100, weight .60), followed by the "find edges" convolution. It wasn't
> perfect, but might help.
>
> Joel
>
>
>
> Joel B. Sheffield, Ph.D
> Department of Biology
> Temple University
> Philadelphia, PA 19122
> Voice: 215 204 8839
> e-mail:
[hidden email]
> URL: *
http://tinyurl.com/khbouft <
http://tinyurl.com/khbouft>*
>
> On Fri, May 1, 2015 at 12:37 AM, Jacqui Ross <
[hidden email]>
> wrote:
>
>> Hi All,
>>
>> I'm helping a PhD student with analysing some Hoffman modulation contrast
>> images of cells. She's primarily interested in changes in diameter. The
>> cells are embedded in a 3D matrix and compression is being applied.
>>
>> In the images, there are nice cells in focus with clear boundaries, plus
>> others which are out of focus which we don't want to measure as any
>> measurements won't be accurate.
>> These kind of images are really tricky to segment as anyone who has tried,
>> already knows. I've tried lots of different filters (edge, etc.) , FFT
>> filtering and the Trainable Weka Segmentation but have been unable to
>> achieve good enough results to be able to then threshold the cells
>> automatically.
>>
>> I've come to the end of the line for now so am asking for your expert help
>> in case anyone has some suggestions:). I note that in 2006 Monique Vasseur
>> offered some DIC images to a PhD student called Daniel Mauch in Germany but
>> I'm not sure if anything came of that project. There are a few papers out
>> there (some mention Hilbert Transform, FFT) but I haven't been successful
>> in implementing anything from those papers as yet.
>>
>> In the meantime, my solution is to use the Pseudo flat field correction
>> plugin from Jan Brocher's BioVoxxel Toolbox (Thanks Jan!) with a very small
>> radius (5) to flatten the background and out of focus cells while
>> preserving the in focus cell outlines. We can then use the Cell Magic Wand
>> (Thanks Theo!) to create selections that can be loaded into the ROI Manager
>> and then measured. This works really well but requires that the cells be
>> selected manually.
>>
>> The Cell Magic Wand Tool works on the colour or grayscale so we can also
>> split the channels from the colour image if needed and use the channel
>> image with the most contrast.
>>
>> I've attached an image in case anyone has any ideas. The image has been
>> cropped out of a larger image so that it's not too big and it's pink
>> because there's cell culture medium there (in case anyone was wondering..).
>>
>> Look forward to hearing any suggestions!
>>
>> Kind regards,
>>
>> Jacqui
>> Jacqueline Ross
>> Biomedical Imaging Microscopist
>> Biomedical Imaging Research Unit
>> School of Medical Sciences
>> Faculty of Medical & Health Sciences
>> The University of Auckland
>> Private Bag 92019
>> Auckland 1142, NEW ZEALAND
>>
>> Tel: 64 9 923 7438
>> Fax: 64 9 373 7484
>>
>>
http://www.fmhs.auckland.ac.nz/sms/biru/>>
>>
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