NON-INVASIVE IMAGE BASED SUPPORT VECTOR MACHINE CLASSIFICATION OF HUMAN EMBRYONIC STEM CELLS

We present a non-invasive, non-destructive automatable image-based methodology for classifying human embryonic stem cell (hESC) colonies. In contrast to differentiated colonies, pluripotent colonies contain homogeneous tight textures, thus allowing a statistical analysis of the coefficients obtained from a wavelet based texture decomposition to discriminate between the colonies. Similarly, borders of undifferentiated cell colonies are sharp, and circular, while those of differentiated colonies are not. We confine our description in this paper to texture analysis, which relies on a parametric and non-parametric hierarchical statistical classification. Parametric classification relies on probability models for texture wavelet coefficients, while non-parametric classification makes use of support vector machines. Preliminary implementation using a truth set yielded a 96% rate of successful colony classification between distant classes, while for intermediate classes of colonies, with mixed population, the success rate was at least 86%. The texture analysis was also validated using individual egg cell images