Abstract
A better understanding of cell behavior is very important in drug and disease research. Cell size, shape, and motility may play a key role in stem-cell specialization or cancer development. However the traditional method of inferring these values manually is such an onerous task that automated methods of cell tracking and segmentation are in high demand. Image cytometry is a practical approach to measure and extract cell properties from large volumes of microscopic cell images. As an important application of image cytometry, this paper presents a probabilistic model based cell tracking method to locate and associate HSCs in phase contrast microscopic images. The proposed cell tracker has been successfully applied to track HSCs based on the most probable identified cell locations and probabilistic data association.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bauman, I., Nenninger, R., Harms, H., Zwierzina, H., Wilms, K., Feller, A., Meulen, V., Muller-Hermelink, H.: Image analysis detects lineage-specific morphologic markers in leukemia blast cells. American Journal of Clinical Pathology 105(1), 23–30 (1995)
Comaniciu, D., Foran, D., Meer, P.: Shape-based image indexing and retrieval for diagnostic pathology. In: Int’l Conf. on Pattern Recognition, pp. 902–904 (1998)
Campo, E., Jaffe, E.S.: Mantle cell lymphoma. Arch. Pathology Lab. Med. 120(1), 12–14 (1996)
Markiewicz, T., Osowski, S., Moszczyski, L., Satat1, R.: Myelogenous leukemia cell image preprocessing for feature generation. In: 5th International Workshop on Computational Methods in Electrical Engineering, pp. 70–73 (2003)
Wu, K., Gauthier, D., Levine, M.: Live cell image segmentation. IEEE Transactions on Biomedical Engineering 42(1), 1–12 (1995)
Glasbey, C.: An analysis of histogram-based thresholding algorithm. Graphical Models and Image Processing 55(6), 532–537 (1993)
Comaniciu, D., Meer, P.: Cell image segmentation for diagnostic pathology. Advanced algorithmic approaches to medical image segmentation: State-of-the-art applications in cardiology, neurology, mammography and pathology, 541–558 (2001)
Geusebroek, J., Smeulders, A., Cornelissen, F.: Segmentation of cell clusters by nearest neighbour graphs. In: Proceedings of the third annual conference of the Advanced School for Computing and Imaging, pp. 248–252 (1997)
Meas-Yedid, V., Cloppet, F., Roumier, A., Alcover, A., Olivo-Marin, J.-C., Stamon, G.: Quantitative microscopic image analysis by active contours. In: VI 2001 Vision Interface Annual Conference - Medical Applications (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kachouie, N.N., Fieguth, P., Ramunas, J., Jervis, E. (2005). A Model-Based Hematopoietic Stem Cell Tracker. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_105
Download citation
DOI: https://doi.org/10.1007/11559573_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29069-8
Online ISBN: 978-3-540-31938-2
eBook Packages: Computer ScienceComputer Science (R0)