Re: Segmentation of DIC or Hoffman Modulation Contrast images - help please

Posted by Jacqueline Ross on May 03, 2015; 1:40am
URL: http://imagej.273.s1.nabble.com/Segmentation-of-DIC-or-Hoffman-Modulation-Contrast-images-help-please-tp5012661p5012679.html

Hi Kenneth,

Thanks for your suggestions. The cells are primarily almost circular. You are correct in your comments about the edges. It's easy to segment out the bright or dark crescent shapes but then the circles are incomplete.

I'm not sure what you mean by a higher level approach - something like machine learning?

I'll have to keep persevering.

Kind regards,

Jacqui
________________________________________
From: ImageJ Interest Group [[hidden email]] on behalf of Kenneth R Sloan [[hidden email]]
Sent: 02 May 2015 02:36
To: [hidden email]
Subject: Re: Segmentation of DIC or Hoffman Modulation Contrast images - help please

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.

--
Kenneth Sloan
[hidden email]
Vision is the art of seeing what is invisible to others.

> 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: [phone]
>> Fax: [phone]
>>
>> http://www.fmhs.auckland.ac.nz/sms/biru/
>>
>>
>> --
>> ImageJ mailing list: http://imagej.nih.gov/ij/list.html
>>
>
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