I have a stack of microscopic images of composite materials and need them segmented in ImageJ/Fiji so that the fibre distribution is isolated cleanly from the matrix and any interfacial noise. The end goal is to quantify that distribution for material-science analysis, so pixel-accurate masks and a reproducible workflow are essential. You’ll start from raw TIFFs (bright-field and a few cross-polarised shots). I’d like you to design or adapt a segmentation pipeline in Fiji—thresholding, filtering, maybe some machine-learning plugins if needed—and package it as a macro or script I can run on other datasets. Please annotate each processing step in the code and include a short read-me explaining parameter choices. Deliverables: • Fiji/ImageJ macro or script with inline comments • A folder containing the segmented binary masks for my sample images • One-page document (PDF or Markdown) summarising the workflow, key settings, and tips for future batches I’ll test the macro on new composite images; acceptance is met once the script runs without errors and produces masks that match the visual quality of your sample output. If you’re comfortable with plugins like Trainable Weka Segmentation or MorpholibJ, mention that in your proposal—those skills will be a plus.