Object segmentation, quantification, counting, and tracking

Abstract

A complete system for object segmentation, counting, quantification, and tracking from microscopic images was implemented. We found that image deconvolution and reconstruction operations are essential to the success of any general-purpose segmentation algorithm and hence are of paramount importance for a counting and tracking software system. Wavelet-based image enhancement, background equalization, and noise suppression routines are the components in our novel general-purpose segmentation algorithm. Simple object recognition based on averages and preset tolerances suffices for most applications. As expected, boundary smoothing is important if watershed-based blob separation is to be used. One of the challenges of a general-purpose counting and tracking system is the need for a large number of object quantification components (features). In tracking we found that incorporating weighted features into an error function improves the accuracy over just the path coherence criterion and that evaluating correspondences over multiple time frames improves the accuracy over using only two consecutive time frames.