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Automation of biological cell tracking processes from time-lapse microscopy is increasingly being preferred due to time efficiency and avoidance of human inconsistencies. This paper presents development of algorithms to track the stochastic motion of the cells in micro-fluidic devices to aid the study of angiogenesis. Angiogenesis is the process of the growth of new blood vessels from a tissue monolayer. This process is important in several fields such as cancer treatment, tissue engineering and wound healing. Challenges with our data include low signal to noise ratio, complex cellular topologies such as close contact of cells and associating data over images acquired at different time. Our methodology of tracking of cells and the monolayer involves two steps; segmentation of the cells from images and tracking of these segmented cells over a span of time. The contribution of our work is the application of level set segmentation and the Bhattacharya distance measures for associating cells over sequential time frames. Results demonstrated on experimental data show that cells and monolayer are segmented and tracked effectively.