Dear Scikit-Image (SKI) team, I have over a hundred scanning electron microscope (SEM) images of gold nanoparticles on glass surfaces, and I've generated several scripts in ImageJ/Python to batch analyze them. The analysis is fairly crude, and consists mostly of users manually thresholding to make a binary image, applying a simple noise filter and performing ImageJ's particle counting routine. Afterwards, my scripts use Python to do plotting, statistics and then output .txt, excel and .tex files. Eventually, I'd like to remove the ImageJ portion altogether and refactor the code to use SKI exclusively; however, for now, I am mainly interested in improving the results with some features of SKI. The images are in a .pdf can be downloaded directly here<http://www.4shared.com/office/xX37rOfh/SCIKIT_TEST_Notable.html>. (Just a hair too big to attach) What I'd like to do is to look at a subset of our images and see if SKI can enhance the image/remove defects. I've chosen 10 images to represent various cases and attached a summary via googledrive. The images are categorized as follows (preliminary questions are in blue): *NICE* - Image is about as good as we can get, and shouldn't have many artifacts. Can these be further enhanced? *LOWCONTRAST *- Can the contrast in these images be enhanced automatically in SKI? *NONCIRC* - Particles appear non-circular due to stigmation offset in microscope. Is it possible to reshape them/make them more circular? *WARPED* - Images that have artifacts, or uneven contrast, due to aberrations in SEM beam during imaging. * I'm especially interested in removing uneven contrast.* *WATERSHED* - These images have overlapping AuNPs, and I had hoped that SKI's watershedding routines might help disentangle them. The *watershed segmentation guide*<http://scikit-image.org/docs/dev/auto_examples/plot_watershed.html>indicates that there are several ways to approach this problem. On the attached PDF, each page shows the original SEM image (converted from highres.tiff to png), a binary image, our manually chosen adjustment threshold, and two estimates of the particle diameter distribution (don't worry about details of this). I was really hoping that some SKI experts would examine these images and suggest some algorithms or insights to address the aforementioned concerns. *The overall goal is to survey the most common problems in SEM imaging of nanoparticles, give examples of each, and demonstrate how SKI can improve the particle counting.* Thanks for you time, and for making a really nice open-source package.