Abstract
The phasor approach is a well-established method for data visualization and image analysis in spectral and lifetime fluorescence microscopy. Nevertheless, it is typically applied in a user-dependent manner by manually selecting regions of interest on the phasor space to find distinct regions in the fluorescence images. In this paper we present our work on using machine learning clustering techniques to establish an unsupervised and automatic method that can be used for identifying populations of fluorescent species in spectral and lifetime imaging. We demonstrate our method using both synthetic data, created by sampling photon arrival times and plotting the distributions on the phasor plot, and real live cells samples, by staining cellular organelles with a selection of commercial probes.
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