I am unsure how to proceed with the images to get the best image analysis out of them. The problem is, it’s a series of sem images where again and again single images have an increased background noise (see dropbox link). First I tried to normalize all images using the histogram function/Interactive 3D surface plot to avoid image loss.
run(“Bio-Formats”, “open=C:/Users/hofmanpa/Desktop/all_Images/Kontrollgruppe/1.1_new.tiff autoscale color_mode=Default rois_import=[ROI manager] view=Hyperstack stack_order=XYCZT”);
run(“Kuwahara Filter”, “sampling=1”);
run(“Convert to Mask”);
The image shows a polished zirconia surface (streaking in the background) with particles on top (sometimes more, sometimes less). My goal is to obtain all overlying particles in size and shape and to determine the total surface area of the particles.
When processing the images, I tried to follow the tips in the video tutorial on FIJI for segmentation.
To make the foreground (particles) stand out better from the background (scratches) I tried several filters. Since I want to preserve the particle shapes, I used the Kuwahara filter.
In selecting a suitable thresholding method, through trial and error, I chosed a global thresholding method (RenyiEntropy fits the best)
- Clean up
I tried to improve the segmentation by eroding, dilating and filling holes.
Thanks for your help!