How can I properly extract the features of a ferning pattern using image processing?

Using feature extraction, what can I do to distinguish the ferning pattern in a positive fern test? Attached is a sample image from https://commons.wikimedia.org/wiki/File:Positive_Fern_Test_.jpg.

[cr]

You mean to say you want to detect whether or not there is a fern pattern? Do you have images without the fern? Also depends on what other patterns are there that shouldn't result in positive? Training a neural network might be easier.

Answer 1

[Tridib]

Hi @Maria Gabriella Andrea, to get started with extracting features from a ferning pattern, these steps might be helpful:

• If the image is in color, first convert it to grayscale so you are only working with intensity values.
• Focus on the main area of the image, which is typically a circular region in microscope images. Create a mask to isolate this main area and exclude the background.
• Enhance the image contrast to make the ferning pattern stand out more clearly against the background.
• Convert the enhanced image to black and white to highlight the ferning structures.
• Use an automatic thresholding method, such as Otsu's method, to separate the pattern from the background.
• Remove any small spots or noise that are not part of the actual pattern. Fill in any small holes or gaps within the pattern to improve its shape.
• Finally, thin the pattern down to its skeleton to clearly reveal the branching structure.
• Use region property tools to measure features like the area covered by the pattern, the length of its branches, and its overall shape. You can also examine texture features such as contrast and smoothness to further describe the pattern’s appearance.

Hope this helps!