@inproceedings {332, title = {Background estimation for microscopic cellular images}, booktitle = {15th IEEE International Conference on Image Processing, 2008}, year = {2008}, abstract = {

Background modelling is a key task in tracking applications. Our interest in this paper is the accurate estimation of static backgrounds in scientific imaging, such as those in automated stem cell tracking. In this paper, an effective background estimation method is proposed. First, the segmentation results are used to remove the foreground objects, then the background is robustly estimated over the resultant 3-Dimensional residual image sequence. We do spatio-temporal background estimation over a local neighbourhood with a robust trimmed mean. The experimental results generated by the proposed method are quite promising.

}, keywords = {automated stem cell tracking, Biomedical Imaging, cellular biophysics, foreground objects removal, image segmentation, image sequence, image sequences, medical image processing, microscopic cellular images, Scientific Imaging, spatio-temporal background estimation, static backgrounds, tracking applications}, doi = {http://dx.doi.org/10.1109/ICIP.2008.4712436}, author = {N Nezamoddin Kachouie and P Fieguth} } @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 = {

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.

}, 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} } @inproceedings {369, title = {Stem-cell localization: a deconvolution problem}, booktitle = {29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2007}, abstract = {

Hematopoietic Stem Cells (HSCs) form blood and immune cells and are responsible for the constant renewal of blood. To produce new blood cells, HSCs proliferate and differentiate to different blood cell types continuously during their lifetime. Hence they are of substantial interest in stem cell therapy and cancer research. To classify HSCs to different groups, they must be observed/tracked over time and their key features including cell size, shape, and motility must be extracted. The manual tracking is an onerous task and automated methods are in high demand. The first stage of an semi-automatic/automatic tracking system is cell segmentation. In our previous work we addressed the cell segmentation/localization problem. Modelling adjacent or splitting cells is very challenging and our previous methods might fail to accurately model a group of adjacent cells or a splitting cell. In this paper we address this issue and propose a deconvolution method to precisely model individual HSCs as well as adjacent (splitting) HSCs. An optimization algorithm is combined with a template matching method to segment cell regions and locate the cell centers.

}, keywords = {adjacent cells, biomedical optical imaging, blood, cell segmentation, cellular biophysics, deconvolution, deconvolution problem, hematopoietic stem cells, image segmentation, immune cells, medical image processing, optimisation, optimization algorithm, stem-cell localization, template matching method}, doi = {http://dx.doi.org/10.1109/IEMBS.2007.4353597}, author = {N Nezamoddin Kachouie and P Fieguth and E Jervis} } @inproceedings {408, title = {A statistical thresholding method for cell tracking}, booktitle = {2006 IEEE International Symposium on Signal Processing and Information Technology}, year = {2006}, abstract = {

Tracking the motion of cells in culture is a task, which often still Is undertaken manually, and for which automated methods are strongly desirable. Researchers visually perform cell motion analysis, observe cell movements and cell shape changes for hours to discover when, where and how fast It moves, splits or dies. Hematopoletlc stem cells (HSCs) proliferate and differentiate to different blood cell types continuously during their lifetime, and are of substantial interest in gene therapy, cancer, and stem-cell research. In this paper a statistical method is introduced to track HSCs over time. A statistical thresholding method is combined with joint probabilistic data association in the proposed HSC tracker

}, keywords = {blood, blood cell, cancer, cell motion analysis, cell tracking, cellular biophysics, gene therapy, hematopoletlc stem cells proliferate, image motion analysis, image segmentation, medical image processing, statistical analysis, statistical thresholding method, stem-cell research}, doi = {http://dx.doi.org/10.1109/ISSPIT.2006.270801}, author = {J Ramunas and N Nezamoddin Kachouie and P Fieguth and E Jervis} } @inproceedings {422, title = {A probabilistic living cell segmentation model}, booktitle = {International Conference on Image Analysis and Recognition}, year = {2005}, 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 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.

}, 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} }