I am seeking a methodology to detect particle clustering in a specific region of an image. Although visible to the naked eye, an objective analysis is required. What are the options available-
Thank you-
A straightforward way would be to examine squared neighborhoods in your image with a moving window of a size that is suitable for your application and image resolution (e.g., 11x11 or 21x21 etc.), and then compute the sum of the intensity of pixels that fall into this window. So if there are many particles in some part of the image, and if the background is more or less homogeneous and/or has a different intensity than the particles, it will be seen in the sum computed with a moving window.
If the particles are less bright than the background, you should use -sum instead of sum.
You can then find the regions where the sum of intensities is maximal (e.g., find local maxima in the obtained accumulator array). And then you could examine the squared neghborhoods centered around those local maxima. And compute other statistical metrics, if you wish, e.g. principal axes of particle clusters, kurtosis, etc.
All of the above can be written in Python with sklearn or skimage, or you can use opencv (for Python or whatever language), there are some building blocks already available there for your task.
Dear Mohazzam Saeed ,
Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image.
Article A comprehensive survey of image segmentation: clustering met...
Regards,
Shafagat
There are several methods for quantifying and analyzing the clustering of particles in an image. Here are some of the most effective options available:
These methods can be used alone or in combination to quantify and analyze the clustering of particles in an image. The most appropriate method(s) will depend on the specific characteristics of the image and the research question at hand.
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