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Due to a recent focus on cell based therapies in biomedical applications, the ability to accurately track cellular behavior has become an increasingly crucial research tool. Cell motility is of particular importance as it influences basic cellular interactions, effecting macroscale processes such as general tissue development, wound healing, and disease progression. Traditional particle tracking systems focus on intensity thresholds as a means of identifying particles and removing noise. This work presents a contour-based particle tracking algorithm capable of identifying and sorting cells based on nuclear stained images. The two functions presented act cohesively to accurately identify individual cells as particles, capitalizing on a new technique for more accurate depiction of variably stained cells. Based on full-width half maximum theorem fitting, preliminary results demonstrate that this tool has strong potential for use in cell based biomedical applications. |
