Cell phase classification using Markov and Gaussian mixture models

Abstract

We present Gaussian mixture and Markov modelling methods for the computerized classification of cell nuclei in different mitotic phases. The methods were tested with the data set containing 379519 cells in 892 cell sequences for 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective and have potential for higher performance with better cellular feature extraction strategy.