Re: Advice on improving a analysis on images of nanoparticles
Thanks for the help Stefan. Here is a direct link that anyone can view: http://www.scribd.com/doc/185189653/Scikit-Test-Notable To download without an account, I am not familiar with any hosting solution, but if you guys have any recommendations I'd love to hear them. Thank you for you help, I will test out the methods you suggested On Sunday, November 17, 2013 4:27:12 AM UTC-5, Stefan van der Walt wrote:
Hi Adam
On Fri, Nov 15, 2013 at 7:02 PM, Adam Hughes <hughes...@gmail.com<javascript:>> wrote:
The images are in a .pdf can be downloaded directly here. (Just a hair too big to attach)
This download requires a login--could you please provide us with a direct link / HTML page showing the images?
LOWCONTRAST - Can the contrast in these images be enhanced automatically in SKI?
We have several histogram equalization algorithms, including Contrast Limited Histogram Equalization, which should do the trick.
NONCIRC - Particles appear non-circular due to stigmation offset in microscope. Is it possible to reshape them/make them more circular?
It depends on what you mean by "make them more circular". We have geometric transformation utilities in `skimage.transform`.
WARPED - Images that have artifacts, or uneven contrast, due to aberrations in SEM beam during imaging. I'm especially interested in removing uneven contrast.
Localized contrast equalization in the `skimage.filter.rank` module might help.
WATERSHED - These images have overlapping AuNPs, and I had hoped that SKI's watershedding routines might help disentangle them. The watershed segmentation guide indicates that there are several ways to approach this problem.
See Juan's post here: https://groups.google.com/d/msg/scikit-image/4z-hPiFFDj8/2gIDMxfGrU4J
Regards Stéfan
Adam, On Wed, Nov 20, 2013 at 7:44 AM, Adam Hughes <hughesadam87@gmail.com> wrote:
To download without an account, I am not familiar with any hosting solution, but if you guys have any recommendations I'd love to hear them.
Dropbox can be used for this... Thank you for you help, I will test out the methods you suggested
Is the goal only to count particles? In that case, I think a local thresholding (threshold_adaptive) would work on all these images. Then, just do a labelling (scipy.ndimage.label) and draw a histogram of particle sizes. You'll get a sharp peak around the true particle size, with bigger peaks for clumps. Once you have the mean particle size you can estimate the number of particles in each clump (barring occlusion in 3D, in which case you're stuffed anyway), and then the total number of particles in your image. Looking at your images, I don't think watershed (or anything else that I know) will do very well with the clumps. The contrast between adjacent particles is too low. Low-contrast-4 looks tricky... Are the smaller "points" particles of different sizes or just image noise? Finally, Watershed-f3 also looks hard, because it appears all the particles are touching... Again, I don't think watershed will help you here, nor anything else that doesn't have an a-priori knowledge of the particle size. Juan.
participants (2)
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Adam Hughes -
Juan Nunez-Iglesias