The analysis was done entirely using the core FIJI functions (as long as you use the 3D Object counter for the segmentation rather than the classic watershed). The advantage to this method is that it is fairly fast, is specific for straight lines, and the resolution of the process can be adjusted to trade-off speed vs. To analyze them individually, simply threshold out the line of interest, or take a MIP of the whole stack to get back a 2D image with the segmented lines. With the segmented stack, each line object will be one of the lines from the image. This means you can run you algorithm of choice to segment out the 3D objects (such as classic watershed or 3D objects counter). Now the lines are nicely segmented in 3D angle space. Normalize the stack intensity, and then threshold the stack such that suppressed lines are removed.Įxtract the lines in 3D “angle space” by: Invert the stack (so lines are white), create a maximum intensity projection, use image calculator to generate a difference stack between the invert and the maximum intensity projection (only suppressed lines at given angles left), and use a 1x1 median to clean up artifacts. Take the inverse FFT to get the image where any lines aligned orthogonal to the angle will be suppressed. Use the mask to crop a single band from the FFT of the image in 1 degree increments.

I solved the problem by sweeping the FFT to segment the lines out via a macro.Ĭreate a bar mask to filter the FFT for line-like objectsĬreate a 180 slice stack of the mask being rotated in 1 degree increments. It should be no problem to acquire real world images with much higher spatial resolution. And yes, this makes a big difference for an approach that was conceptualized with respect to the test data! It is not life that is unfair but people who are careless. The resolution of the test image is at least 5 times higher. The spatial resolution of the rods is poor and perhaps the absolute minimum possible. In other words, the result consists of 336 images (isolated rods of measured lengths) but only 281 ROIs which, with much involved maths, tells us that in 55 of the 281 ROIs two rods were analyzed. The restriction can easily be recognized from the posted results: There are at most two images with the same ROI (number).

Consequently, I’ve limited the analysis to two rods per ROI and of course this limitation will introduce a certain bias. real-world image contains joints or crossings of more than two rods.Īny analysis based on “Ultimate Points” needs more effort, if more than two rods are to be analyzed, that I’m not willing to spend. If more han two rods overlap, the problem becomes more involved. Thanks again for your ideas–I’ll keep following your interesting discussion! If I may make one suggestion to those posters who solved the simplistic examples with 7 lines at the top of this thread, is to try to apply the proposed methodology to a real-life situation perfectly reflected in the top posting (greenish-background image) posted at this is what I and others in the composite materials field have to deal with. So, I’m impressed with example! However, can I realistically expect the program to deal efficiently with 500-2000 objects, such as the example mentioned below? I’d like to add that I have recently tried CellProfiler as well–and even contacted one of the co-authors, but I was informed that the program has not been designed and fine-tuned to resolve and measure such linear and low-L/d objects. Just wanted to thank everybody participating in this thread for your interest, time and valuable suggestions for the moment, I’ll stay at the sidelines as I can’t really contribute any technical insights, just judge the practicality and accuracy of the proposed method from a point of view of an non-expert user.

Cellprofiler curved chord length