



@article {2300,
	title = {Automated Arabidopsis plant root cell segmentation based on SVM classification and region merging},
	journal = {Computers in Biology and MedicineComput. Biol. Med.},
	volume = {39},
	year = {2009},
	note = {Cited By (since 1996): 9Export Date: 21 November 2012Source: ScopusCODEN: CBMDAdoi: 10.1016/j.compbiomed.2009.06.008PubMed ID: 19604506Language of Original Document: EnglishCorrespondence Address: Marcuzzo, M.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUP, Portugal; email: marcuzzo@fe.up.ptReferences: Baeza-Yates, R., Ribeiro-Neto, B., (1999) Modern Information Retrieval, , ACM Press;Bleau, A., Leon, L., Watershed-based segmentation and region merging (2000) Computer Vision and Image Understanding, 77 (3), pp. 317-370; 
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},
	month = {2009///},
	pages = {785 - 793},
	abstract = {To obtain development information of individual plant cells, it is necessary to perform in vivo imaging of the specimen under study, through time-lapse confocal microscopy. Automation of cell detection/marking process is important to provide research tools in order to ease the search for special events, such as cell division. In this paper we discuss an automatic cell detection approach for Arabidopsis thaliana based on segmentation, which selects the best cell candidates from a starting watershed-based image segmentation and improves the result by merging adjacent regions. The selection of individual cells is obtained using a support vector machine (SVM) classifier, based on a cell descriptor constructed from the shape and edge strength of the cells{\textquoteright} contour. In addition we proposed a novel cell merging criterion based on edge strength along the line that connects adjacent cells{\textquoteright} centroids, which is a valuable tool in the reduction of cell over-segmentation. The result is largely pruned of badly segmented and over-segmented cells, thus facilitating the study of cells. When comparing the results after merging with the basic watershed segmentation, we obtain 1.5\% better coverage (increase in F-measure) and up to 27\% better precision in correct cell segmentation. {\textcopyright} 2009 Elsevier Ltd. All rights reserved.},
	keywords = {accuracy, Arabidopsis, Arabidopsis plant, Arabidopsis thaliana, article, artificial intelligence, automation, Bioinformatics, Biological cells, Biomedical image processing, Cell detection, Cell divisions, Cell membranes, Cell merging, cell proliferation, Cell segmentation, Cell Shape, Cell Wall, Computer simulation, confocal microscopy, controlled study, Descriptor, F-measure, Image analysis, image processing, Image Processing, Computer-Assisted, Image segmentation, Imaging systems, In-Vivo imaging, Individual cells, Landforms, Merging, Models, Biological, nonhuman, Over segmentation, Plant cells, plant root, Plant roots, Plants (botany), priority journal, Region-merging, Research tools, root morphology, Segmented cell, signal noise ratio, support vector machine, Support vector machines, SVM classification, Watershed segmentation, Watersheds},
	isbn = {00104825 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-67849119398\&partnerID=40\&md5=8029bce67aff136ccc5e0f7e4f9e0ded},
	author = {Marcuzzo, M. and Quelhas, P. and Campilho, A. and Maria Mendon{\c c}a, A. and Campilho, A.}
}
