Fiber Separation (EM)
Accurately analyzing the length and number of fibers in a field of view can be challenging due to overlapping structures, which often make the process time-consuming and complex. Overlapping fibers can obscure important details, complicating segmentation and reducing the reliability of measurements.
The Image-Pro Fiber Separation protocol simplifies this task by breaking the workflow into manageable steps. It automatically separates overlapping fibers with precision, allowing users to extract accurate measurements such as fiber length, count, and orientation. Designed for efficiency, the protocol supports high-throughput analysis of large datasets, including complex formats like multi-well plates or multiple image folders, all with minimal image analysis expertise required.
Techniques: SEM, TEM
How it works
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Select Channel
Select the channel that contains fibers.
Separate from Background
Separate fibers from the background using a deep learning model, machine learning, threshold segmentation, or a custom algorithm.
Identify Fibers
Automatically resolve overlaps and identify each individual fiber.
Quantitative results
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Automatically generate tables, heat maps, charts and even complex bespoke reports.
Measurement parameters supported
- • Fiber Length
- • Fiber Angle
- • Fiber Thickness
- • Fiber Count
- • Fiber Length (Sum)
- • Fiber Length (Mean)
- • Fiber Length (Std Dev)
- • Fiber Angle (Mean)
Solution requirements
Required Modules
Base
2D Automated Analysis
Materials EM Protocols Collection
Fiber Separation (EM)
Recommended Package
Literature spotlight
- Rubín de Celis Leal, D., Nguyen, D., Vellanki, P., Li, C., Rana, S., Thompson, N., ... & Sutti, A. (2019). Efficient bayesian function optimization of evolving material manufacturing processes. ACS omega, 4(24), 20571-20578.
- Subianto, S., Li, C., Rubin de Celis Leal, D., Rana, S., Gupta, S., He, R., ... & Sutti, A. (2019). Optimizing a high-entropy system: software-assisted development of highly hydrophobic surfaces using an amphiphilic polymer. ACS omega, 4(14), 15912-15922.
- Kärkönen, A., Korpinen, R., Järvenpää, E., Aalto, A., & Saranpää, P. (2022). Properties of Oat and Barley Hulls and Suitability for Food Packaging Materials. Journal of Natural Fibers, 19(16), 13326–13336.