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	title = {Cell nuclei and cytoplasm joint segmentation using the sliding band filter},
	journal = {IEEE Transactions on Medical ImagingIEEE Trans. Med. Imaging},
	volume = {29},
	year = {2010},
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},
	month = {2010///},
	pages = {1463 - 1473},
	abstract = {Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells{\textquoteright} locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter{\textquoteright}s design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells{\textquoteright} nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95\%/69\% and 82\%/90\% for nuclei and cytoplasm detection respectively and an overall accuracy of 76\%. {\textcopyright} 2010 IEEE.},
	keywords = {actin, Actins, animal, Animals, article, Band filters, Biological research, cell aggregation, Cell analysis, Cell culture, Cell detection, Cell images, cell nucleus, Cell segmentation, Cell Shape, Cell shapes, Cell size, Cells, chemistry, Computer vision, Convergence filters, Convex shapes, cytoplasm, Data sets, Databases, Factual, Digital image storage, DNA, Drosophila melanogaster, factual database, Fluorescence, Fluorescence microscopy, Fundamental tools, High variability, Image analysis, Image datasets, image processing, image processing in biology, Image Processing, Computer-Assisted, Image quality, Image segmentation, Imaging systems, Improving performance, Individual cells, Joint segmentation, Location, methodology, Microscopy, Fluorescence, Multivariate analysis, multivariate image processing, New approaches, Nuclear detection, Overlap correction, reproducibility, Reproducibility of Results, Semi-automated, Shape estimation, Shape regularization, ultrastructure, Visual communication, Visual inspection},
	isbn = {02780062 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77955339493\&partnerID=40\&md5=f8cea406f7b2fcf901dc34fa940522b1},
	author = {Quelhas, P. and Marcuzzo, M. and Mendon{\c c}a, A.M. and Campilho, A.}
}
