Migration behavior of stromal cells underlies response to injury, and abnormal cell migration contributes to disease pathologies such as cancer, angiogenesis, vascular stenosis and arthritis. The dispersion behaviors of cancer cells inform features of the malignant progression that greatly impacts clinical outcome for patients. As the molecular and biochemical descriptions of pathological specimens become more refined, the need for new perspective on laboratory models of cellular phenotypes under controlled conditions stands to enhance and accelerate the utility of in vitro models of human biology for better diagnosis and therapy development [1].

The method presented here improves the identification of laboratory cell migration patterns and determines rates of dispersion. Results are obtained from analysis of 4,000 images of 34 different cell lines of bladder cancer interacting on different matrix substrates at two time points (16 and 40 hours). Cells only occupy a fraction of the image and are usually clustered within a Region of Interest (ROI) of the image. A significant portion of the images corresponds to non-relevant background information that significantly consumes the bit budget and dramatically increases the demand for storage, transmission, and processing of the data.

Currently, medical professionals and biologists devote critical manpower to the tedious and potentially nonreproducible process of manually specifying the relevant ROIs. We present a method for automatic identification of these ROIs in images of migrating bladder cancer cells.