



@book {2263,
	title = {Optical flow based Arabidopsis thaliana root meristem cell division detection},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {6112 LNCS},
	year = {2010},
	note = {Conference code: 81097Cited By (since 1996): 1Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-13775-4_22Language of Original Document: EnglishCorrespondence Address: Quelhas, P.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUPPortugalReferences: Campilho, A., Garcia, B., Toorn, H.V.D., Wijk, H.V., Campilho, A., Scheres, B., Time-lapse analysis of stem-cell divisions in the Arabidopsis thaliana root meristem (2006) Plant Journal, 48 (4), pp. 619-627. , DOI 10.1111/j.1365-313X.2006.02892.x;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 (2007) Motion{\textquoteright}07, p. 13; 
Beemster, G.T.S., Baskin, T.I., Analysis of Cell Division and Elongation Underlying the Developmental Acceleration of Root Growth in Arabidopsis thaliana (1998) Plant Physiology, 116 (4), 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; 
Harder, N., M-Bermudez, F., Godinez, W., Ellenberg, J., Rohr, K., Automated analysis of mitotic cell nuclei in 3rd fluorescence microscopy image sequences Workshop on Bio-Image Inf.: Bio. Imag., Comp. Vision and Data Mining (2008); 
Yang, F., Mackey, M.A., Ianzini, F., Gallardo, G., Sonka, M., Cell segmentation, tracking, and mitosis detection using temporal context (2005) Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 302-309; 
Marcuzzo, M., Quelhas, P., Campilho, A., Mendon{\c c}a, A.M., Campilho, A., Automated arabidopsis plant root cell segmentation based on svm classification and region merging (2009) Comp. in Biology and Medicine, 39, pp. 785-793; 
Marcuzzo, M., Guichard, T., Quelhas, P., Maria Mendon{\c c}a, A., Campilho, A., Cell division detection on the arabidopsis thaliana root (2009) LNCS, 5524, pp. 168-175. , Araujo, H., Mendon{\c c}a, A.M., Pinho, A.J., Torres, M.I. (eds.) IbPRIA 2009. Springer, Heidelberg; 
Pereira, C.S., Mendon{\c c}a, A.M., Campilho, A., Evaluation of contrast enhancement filters for lung nodule detection (2007) LNCS, 4633, pp. 878-888. , Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. Springer, Heidelberg; 
Garcia, B., Campilho, A., Scheres, B., Campilho, A., Automatic tracking of arabidopsis thaliana root meristem in confocal microscopy (2004) LNCS, 3212, pp. 166-174. , Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. Springer, Heidelberg; 
Hartley, R., Zisserman, A., (2003) Multiple View Geometry in Computer Vision, , Cambridge University Press, Cambridge; 
Lowe, D.G., Distinctive image features from scale-invariant keypoints (2004) International Journal of Computer Vision, 60, pp. 91-110; 
Brox, T., Bruhn, A., Papenberg, N., Weickert, J., High accuracy optical flow estimation based on a theory for warping (2004) LNCS, 3024, pp. 25-36. , Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. Springer, Heidelberg
},
	month = {2010///},
	pages = {217 - 226},
	address = {Povoa de Varzim},
	abstract = {The study of cell division and growth is a fundamental aspect of plant biology research. In this research the Arabidopsis thaliana plant is the most widely studied model plant and research is based on in vivo observation of plant cell development, by time-lapse confocal microscopy. The research herein described is based on a large amount of image data, which must be analyzed to determine meaningful transformation of the cells in the plants. Most approaches for cell division detection are based on the morphological analysis of the cells{\textquoteright} segmentation. However, cells are difficult to segment due to bad image quality in the in vivo images. We describe an approach to automatically search for cell division in the Arabidopsis thaliana root meristem using image registration and optical flow. This approach is based on the difference of speeds of the cell division and growth processes (cell division being a much faster process). With this approach, we can achieve a detection accuracy of 96.4\%. {\textcopyright} 2010 Springer-Verlag.},
	keywords = {Arabidopsis thaliana, Biology, Biology image processing, cell division detection, Cell divisions, cell proliferation, confocal microscopy, Detection accuracy, Growth process, Image data, Image quality, Image segmentation, Imaging systems, In-vivo, In-vivo images, Metadata, Morphological analysis, Optical data processing, Optical flows, Plant biology, Plant cells, research, Root meristem},
	isbn = {03029743 (ISSN); 3642137741 (ISBN); 9783642137747 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77955346994\&partnerID=40\&md5=22ef87a91388dfe96acf9e4ac6ad4377},
	author = {Quelhas, P. and Mendon{\c c}a, A.M. and Campilho, A.}
}
