



@book {2201,
	title = {Automatic assessment of Leishmania infection indexes on in vitro macrophage cell cultures},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {7325 LNCS},
	year = {2012},
	note = {Conference code: 91199Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-31298-4_51Language of Original Document: EnglishCorrespondence Address: Leal, P.; Universidade Do Porto, Faculdade de Engenharia, Departamento de Engenharia Qu{\'\i}micaPortugalReferences: Castro, H., Sousa, C., Santos, M., Cordeiro-da-Silva, A., Floh{\'e}, L., Tom{\'a}s, A., Complementary antioxidant defence by cytoplasmic and mitochondrial peroxiredoxins in Leishmania infantum (2002) Free Radical Biology and Medicine, 33, pp. 1552-1562;Chen, X., Zhou, X., Wong, S., Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy IEEE Trans. on Biomedical Engineering, 53 (4), pp. 762-766; 
Marcuzzo, M., Quelhas, P., Campilho, A., Maria Mendon{\c c}a, A., Campilho, A., Automated arabidopsis plant root cell segmentation based on svm classification and region merging (2009) Computers in Biology and Medicine, 39 (9); 
Chen, Y., Ladi, E., Herzmark, P., Robey, E., Roysam, B., Automated 5-d analysis of cell migration and interaction in the thymic cortex from time-lapse sequences of 3-d multi-channel multi-photon images (2009) J. Immunol. Methods, 340 (1), pp. 65-80; 
Esteves, T., Quelhas, P., Mendon{\c c}a, A.M., Campilho, A., Gradient convergence filters and a phase congruency approach for in vivo cell nuclei detection Machine Vision and Applications, , in press; 
Schmitt, O., Hasse, M., Morphologic multiscale decomposition of connected regions with emphasis on cell clusters (2009) CVIU, 113 (2), pp. 188-201; 
Usaj, M., Torkar, D., Kanduser, M., Miklavcic, D., Cell counting tool parameters optimization approach for electroporation efficiency determination of attached cells in phase contrast image (2010) Journal of Microscopy, 241 (3), pp. 303-314; 
Sezgin, M., Sankur, B., Survey over image thresholding techniques and quantitative performance evaluation (2003) Journal of Electronic Imaging, 13 (1), pp. 146-165; 
Lowe, D., Distinctive Image Features from Scale-Invariant Keypoints (2004) International Journal of Computer Vision; 
Lindberg, T., Scale-space theory: A basic tool for analysing structures at different scales (1994) Journal of Applied Statistics, pp. 11-24; 
Yan, P., Zhou, X., Shah, M., Wong, S.T.C., Automatic segmentation of high-throughput RNAi fluorescent cellular images (2008) IEEE Trans. Inf. Technol. Biomed., 12 (1), pp. 109-117; 
CellNote: Plataforma para An?alise Assistida de Imagens, Bruno Afonso Da Cruz Lopes, , Master Thesis, Faculty of Sciences of the University of Porto
},
	month = {2012///},
	pages = {432 - 439},
	address = {Aveiro},
	abstract = {Evaluation of parasite infection indexes on in vitro cell cultures is a practice commonly employed by biomedical researchers to address biological questions or to test the efficacy of novel anti-parasitic compounds. In the particular case of Leishmania infantum, a unicellular parasite that parasitizes macrophages, infection indexes are usually determined either by visual inspection of cells directly under the microscope or by counting digital images using appropriate software. In either case assessment of infection indexes is time consuming, thus motivating the creation of automatic image analysis approaches that allow large scale studies of Leishmania-infected macrophage cultures. We propose a fully automated method for automatic evaluation of parasite infection indexes through the segmentation of individual macrophages nucleus and cytoplasm, as well as the segmentation and co-localization of the parasites in the image. To perform such analysis with robustness and increased performance we propose the use of local image filters tuned to the specific size of the objects to detect, in conjunction with image segmentation approaches. The objects size estimation is then improved through a learning feedback loop. Cytoplasm is detected by seeded watershed segmentation. Our approach obtains, for 86 images from 4 experiments, an average parasite infection index evaluation error of 2.3\%. {\textcopyright} 2012 Springer-Verlag.},
	keywords = {Automated methods, Automatic assessment, Automatic evaluation, Automatic image analysis, Cell culture, Cell nuclei detection, cell nucleus, Colocalization, Digital image, Enzyme inhibition, Feed-back loop, Image analysis, Image filters, Image segmentation, In-vitro, Leishmania, Leishmania infantum, Macrophage cells, Macrophage culture, Macrophages, microscopy image segmentation, Microscopy images, Parasite infection, Parasite-, Size estimation, Visual inspection, Watershed segmentation},
	isbn = {03029743 (ISSN); 9783642312977 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84864131020\&partnerID=40\&md5=71523f4ff6de3e6ecc717c2fce28284a},
	author = {Leal, P. and Ferro, L. and Marques, M. and Rom{\~a}o, S. and Cruz, T. and Tom{\'a}, A.M. and Castro, H. and Quelhas, P.}
}
