Cell phase identification using fuzzy Gaussian mixture models

Abstract

Fuzzy Gaussian mixture modeling method is proposed in this paper for the computerized classification of cell nuclei in different mitotic phases. A mixture of Gaussian distributions was used to represent the cell data in multi-dimensional cell feature space. Gaussian parameters were estimated using fuzzy c-means estimation. The method was tested with the data set containing 379519 cells in 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. Experimental results have shown that the proposed method is more effective than the Gaussian mixture modeling method.