Image Processing

The principal objective of this research is to investigate whether there is a correlation between the features of the images taken by an electron microscope, the production conditions of the bio-polymers, and their macroscopic properties.


Example of an image taken with a electron microscope that shows a flake of bio-polymers or nanotubes. We processs such an image through different steps in order to equalize its background, to reduce its noice, to binarize the gray scales into black&white, and eventually to compute its skeleton.

thinned image

Example of a skeleton image of a bio-polymer. Such an image is further processed in order to find its connected components and to calculate certain features such as length or width distributions.

Example of the width distribution of the nanotubes of the main component in the image as shown above.

As a side effect within this research, we have developed a general improvement for iterative and parallelizable thinning algorithms. We call the new method guided thinning and the skeletons obtained with the modification are much better centralized within the shapes. The results that we achieve when the method is incorporated into standard iterative thinning algorithms are shown in this image gallery. The corresponding research article is currently under peer review.

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