@inproceedings {422,
	title = {A probabilistic living cell segmentation model},
	booktitle = {International Conference on Image Analysis and Recognition},
	year = {2005},
	abstract = {<p>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 from image sequences manually is such an onerous task that automated methods of cell tracking and segmentation are in high demanded, especially given the increasing amount of cell data being collected. In this paper, a novel probabilistic cell model is designed to segment the individual hematopoietic stem cells (HSCs) extracted from mice bone marrow cells. The proposed cell model has been successfully applied to HSC segmentation, identifying the most probable cell locations in the image on the basis of cell brightness and morphology.</p>
},
	keywords = {bone marrow cells, cancer development, cellular biophysics, disease research, hematopoietic stem cells, image segmentation, image sequences, medical image processing, probabilistic living cell segmentation model, probability, stem-cell specialization},
	doi = {http://dx.doi.org/10.1109/ICIP.2005.1529956},
	author = {N Nezamoddin Kachouie and L J Lee and P Fieguth}
}
