@inproceedings {349,
	title = {Watershed deconvolution for cell segmentation},
	booktitle = {30th Annual Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2008},
	abstract = {<p>Cell segmentation and/or localization is the first stage of a (semi)automatic tracking system. We addressed the cell localization problem in our previous work where we characterized a typical blood stem cell in a microscopic image as an approximately circular object with dark interior and bright boundary. We also addressed the modelling of adjacent and dividing cells in our previous work as a deconvolution method to model individual blood stem cell as well as adjacent and dividing blood stem cells where an optimization algorithm was combined with a template matching method to segment cell regions and locate the cell centers. Our previous cell deconvolution method is capable of modelling different cell types with changes in the model parameters. However in cases where either a complex parameterized shape is needed to model a specific cell type, or in place of cell center localization, an exact cell segmentation is needed, this method will not be effective. In this paper we propose a method to achieve cell boundary segmentation. Considering cell segmentation as an inverse problem, we assume that cell centers are located in advance. Then, the cell segmentation will be solved by finding cell regions for optimal representation of cell centers while a template matching method is effectively employed to localize cell</p>
},
	keywords = {Algorithms, Artificial Intelligence, Automated, Cells, Computer-Assisted, Cultured, hematopoietic stem cells, Humans, image enhancement, image interpretation, Microscopy, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity},
	doi = {http://dx.doi.org/10.1109/IEMBS.2008.4649168},
	author = {N Nezamoddin Kachouie and P Fieguth and E Jervis}
}
@article {356,
	title = {Extended-Hungarian-JPDA: Exact single-frame stem cell tracking},
	journal = {IEEE Transactions on Biomedical Engineering},
	volume = {54},
	year = {2007},
	pages = {2011 - 2019},
	abstract = {<p>The fields of bioinformatics and biotechnology rely on the collection, processing and analysis of huge numbers of biocellular images, including cell features such as cell size, shape, and motility. Thus, cell tracking is of crucial importance in the study of cell behaviour and in drug and disease research. Such a multitarget tracking is essentially an assignment problem, NP-hard, with the solution normally found in practice in a reduced hypothesis space. In this paper we introduce a novel approach to find the exact association solution over time for single-frame scan-back stem cell tracking. Our proposed method employs a class of linear programming optimization methods known as the Hungarian method to find the optimal joint probabilistic data association for nonlinear dynamics and non-Gaussian measurements. The proposed method, an optimal joint probabilistic data association approach, has been successfully applied to track hematopoietic stem cells.</p>
},
	keywords = {Algorithms, Animals, biocellular images, bioinformatics, biological techniques, biotechnology, cell motility, cell shape, cell size, Cells, cellular biophysics, Computer-Assisted, Data Interpretation, extended-Hungarian-joint probabilistic data association, hematopoietic stem cells, Humans, image enhancement, image interpretation, linear programming optimization methods, Microscopy, multitarget tracking, nonGaussian measurements, nonlinear dynamics, optimal joint probabilistic data association, principal component analysis, Reproducibility of Results, Sensitivity and Specificity, single-frame scan-back stem cell tracking, Statistical, Video},
	issn = {0018-9294},
	doi = {http://dx.doi.org/10.1109/TBME.2007.895747},
	author = {N Nezamoddin Kachouie and P Fieguth}
}
