Automatic Image Segmentation Based on Phasor Space Clustering

Abstract

We present our latest developments in automatic image segmentation for measurements on spectral and lifetime fluorescence imaging. The algorithm operates on the basis of the phasor approach and makes use of a well-established machine learning method for clustering N-dimensional data points, in our case the pixel data points on the phasor space. The technique is based on Gaussian mixture models solved by the use of the expectation-maximization algorithm. We show the method is the best candidate out of the many machine learning clustering techniques available due to the nature of the phasor data and also due to the spreading of points in the phasor space for the cases of low pixel photon counts. In order to quantify the power of the method we performed simulations with synthetic phasor data and we tested the method by measuring its performance against a series of established clustering techniques. We also show results on lifetime and spectral data obtained from samples of live cells. The samples were prepared by staining with a series of commercially-available organelle dyes in order to target distinct structures in the cells. We show how by taking a single spectral and lifetime acquisition we resolve up to six populations in the phasor space corresponding to the six cellular staining probes. Furthermore, we use this method in combination with our other recent work, where we make use of higher harmonics of the phasor transform in order to solve for the fractions of known components and also for lifetimes of unknown components. This multi-harmonic multi-component method is highly sensitive to noise and heterogeneous distributions and here is where our clustering method simplifies the problem by preprocessing the data and fragmenting it into pixels containing distinct lifetime/spectral characteristics. (Supported by NIH P41-GM103540).