



@conference {2338,
	title = {Automatic cell segmentation from confocal microscopy images of the Arabidopsis root},
	booktitle = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBIIEEE Int. Symp. Biomed. Imaging: Nano Macro, Proc., ISBI},
	series = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI},
	year = {2008},
	note = {Conference code: 73366Cited By (since 1996): 4Export Date: 21 November 2012Source: ScopusArt. No.: 4541095doi: 10.1109/ISBI.2008.4541095Language of Original Document: EnglishCorrespondence Address: Marcuzzo, M.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUPPortugalReferences: Campilho, A., Garcia, B., Toorn, H., Wijk, H., Campilho, A., Scheres, B., Time-lapse analysis of stem-cell divisions in the arabidopsis thaliana root meristem (2006) The Plant Journal, 48, pp. 619-627;Iwamoto, A., Satoh, D., Furutani, M., Maruyama, S., Ohba, H., Sugiyama, M., Insight into the basis of root growth in arabidopsis thaliana provided by a simple mathematical model (2006) J. Plant Res, 119, pp. 85-93; 
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Beemster, G., Baskin, T., Analysis of cell division and elongation underlying the developmental acceleration of root growth in arabidopsis thaliana (1998) Plant Physiology, 116, pp. 1515-1526; 
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Sponsors: Institute of Electrical and Electronic Engineers; Engineering in Medicine and Biology Society; Signal Processing Society},
	month = {2008///},
	pages = {712 - 715},
	address = {Paris},
	abstract = {In vivo observation and tracking of cell division in the Arabidopsis thaliana root meristem, by time-lapse confocal microscopy, is central to biology research. The research herein described is based on large amount of image data, which must be analyzed to determine the location and state of cells. The possibility of automating the process of cell detection/marking is an important step to provide research tools to the biologists in order to ease the search for a special event as cell division. This paper discusses an automatic cell segmentation method, which selects the best cell candidates from a starting watershed based image segmentation. The selection of individual cells is obtained using a Support Vector Machine (SVM) classifier, based on the shape and edge strength of the cells{\textquoteright} contour. The resulting segmentation is largely pruned of badly segmented cells, which can reduce the false positive detection of cell division. This is a good result on its own and a starting point for improvement of cell segmentation methodology. {\textcopyright}2008 IEEE.},
	keywords = {Biological cells, Biomedical image processing, Cell divisions, cell proliferation, Cell segmentation, Cells, confocal microscopy, image processing, Image segmentation, Microscopic examination, Support vector machines, Technical presentations},
	isbn = {9781424420032 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-51049123600\&partnerID=40\&md5=9e4652fa61936d105b4059db95e63f78},
	author = {Marcuzzo, M. and Quelhas, P. and Campilho, A. and Mendon{\c c}a, A.M. and Campilho, A.}
}
