



@book {2302,
	title = {Cell division detection on the arabidopsis thaliana root},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5524 LNCS},
	year = {2009},
	note = {Conference code: 77027Cited By (since 1996): 5Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-02172-5_23Language of Original Document: EnglishCorrespondence Address: Marcuzzo, M.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUPReferences: 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; 
Roberts, T., Mckenna, S., Wuyts, N., Valentine, T., Bengough, A.: Performance of low-level motion estimation methods for confocal microscopy of plant cells in vivo. In: Motion 2007, p. 13 (2007)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; 
Chen, X., Zhou, X., Wong, S.T.C., Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy (2006) IEEE Trans. on Biomedical Engineering, 53 (4), pp. 762-766; 
Mao, K.Z., Zhao, P., Tan, P., Supervised learning-based cell image segmentation for p53 immunohistochemistry (2006) IEEE Trans. on Biomedical Engineering, 53 (6), pp. 1153-1163; 
Yang, X., Li, H., Zhou, X., Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and kalman filter in time-lapse microscopy (2006) IEEE Trans. on Circuits and Systems I-Regul. Papers, 53 (11), pp. 2405-2414; 
Harder, N., M-Bermudez, F., Godinez, W., Ellenberg, J., Eils, R., Rohr, K., Automated analysis of mitotic cell nuclei in 3d fluorescence microscopy image sequences (2008) Workshop on Bio-Image Informatics: Biological Imaging, Computer Vision and Data Mining; 
Kobatake, H., Hashimoto, S., Convergence index filter for vector fields (1999) IEEE Trans. on Image Processing, 8 (8); 
Wei, J., Hagihara, Y., Kobatake, H., Detection of rounded opacities on chest radiographs using convergence index filter (1999) Proceedings of the Int. Conference on Image Analysis and Processing, pp. 757-761; 
Pereira, C.S., Fernandes, H., Mendon{\c c}a, A.M., Campilho, A.C.: Detection of lung nodule candidates in chest radiographs. In: Mart{\'\i}, J., Bened{\'\i}, J.M., Mendon{\c c}a, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, 4478, pp. 170-177. Springer, Heidelberg (2007)Marcuzzo, M., Quelhas, P., Campilho, A., Mendon{\c c}a, A.M., Campilho, A., A hybrid approach for cell image segmentation (2008) LNCS, 5112, pp. 739-749. , Campilho, A, Kamel, M.S, eds, ICIAR 2008, Springer, Heidelberg
Sponsors: International Association for Pattern Recognition (IAPR); Fundacao para a Ciencia e a Tecnologia (FCT)},
	month = {2009///},
	pages = {168 - 175},
	address = {Povoa de Varzim},
	abstract = {The study of individual plant cells and their growth structure is an important focus of research in plant genetics. To obtain development information at cellular level, researchers need to perform in vivo imaging of the specimen under study, through time-lapse confocal microscopy. Within this research field it is important to understand mechanisms like cell division and elongation of developing cells. We describe a tool to automatically search for cell division in the Arabidopsis thaliana using information of nuclei shape. The nuclei detection is based on a convergence index filter. Cell division detection is performed by an automatic classifier, trained through cross-validation. The results are further improved by a stability criterion based on the Mahalanobis distance of the shape of the nuclei through time. With this approach, we can achieve a correct detection rate of 94.7\%. {\textcopyright} 2009 Springer Berlin Heidelberg.},
	keywords = {A-stability, Arabidopsis thaliana, Automatic classifiers, Bioinformatics, Cell divisions, Cell membranes, cell proliferation, Cellular levels, confocal microscopy, Cross validation, Detection rates, Growth structures, Image analysis, In-plants, In-Vivo imaging, Mahalanobis distances, Pattern recognition, Plant cells, research, Research fields, Stability criteria},
	isbn = {03029743 (ISSN); 3642021719 (ISBN); 9783642021718 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-68749115481\&partnerID=40\&md5=2c491950d3b00a9dd873aeaefdf5edcb},
	author = {Marcuzzo, M. and Guichard, T. and Quelhas, P. and Mendon{\c c}a, A.M. and Campilho, A.}
}
