



@article {2206,
	title = {Gradient convergence filters and a phase congruency approach for in vivo cell nuclei detection},
	journal = {Machine Vision and ApplicationsMach Vision Appl},
	volume = {23},
	year = {2012},
	note = {Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusCODEN: MVAPEdoi: 10.1007/s00138-012-0407-7Language of Original Document: EnglishCorrespondence Address: Quelhas, P.; INEB-Instituto de Engenharia Biom{\'e}dica, Oporto, Portugal; email: quelhas@fe.up.ptReferences: Bleau, A., Leon, L.J., Watershed-based segmentation and region merging (2000) Computer Vision and Image Understanding, 77 (3), pp. 317-370. , DOI 10.1006/cviu.1999.0822;Bresson, X., Vandergheynst, P., Thiran, J., A priori information in image segmentation: Energy functional based on shape statistical model and image information (2003) Proceedings of International Conference on Image Processing, pp. 428-428. , ICIP; 
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},
	month = {2012///},
	pages = {623 - 638},
	abstract = {Computational methods used in microscopy cell image analysis have largely augmented the impact of imaging techniques, becoming fundamental for biological research. The understanding of cell regulation processes is very important in biology, and in particular confocal fluorescence imaging plays a relevant role for the in vivo observation of cells. However, most biology researchers still analyze cells by visual inspection alone, which is time consuming and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cells. While the classic approach for automatic cell analysis is to use image segmentation, for in vivo confocal fluorescence microscopy images of plants, such approach is neither trivial nor is it robust to image quality variations. To analyze plant cells in in vivo confocal fluorescence microscopy images with robustness and increased performance, we propose the use of local convergence filters (LCF). These filters are based in gradient convergence and as such can handle illumination variations, noise and low contrast.We apply a range of existing convergence filters for cell nuclei analysis of the Arabidopsis thaliana plant root tip. To further increase contrast invariance, we present an augmentation to local convergence approaches based on image phase information. Through the use of convergence index filters we improved the results for cell nuclei detection and shape estimation when compared with baseline approaches. Using phase congruency information we were able to further increase performance by 11\% for nuclei detection accuracy and 4\% for shape adaptation. Shape regularization was also applied, but with no significant gain, which indicates shape estimation was good for the applied filters. {\textcopyright} 2011 Springer-Verlag.},
	keywords = {Arabidopsis thaliana, Biological research, Cell analysis, Cell images, cell nucleus, Cell regulation, Cell segmentation, Cells, Confocal fluorescence imaging, Confocal fluorescence microscopy, Convergence filters, cytology, Detection accuracy, Estimation, Fluorescence microscopy, Illumination variation, Image analysis, Image filtering, Image segmentation, Imaging techniques, In-vivo, Information use, Local Convergence, Local image filtering, Medical applications, Phase congruency, Phase information, Plant cells, Plant roots, Plants (botany), Quality variation, Shape adaptation, Shape estimation, Shape regularization, Visual inspection},
	isbn = {09328092 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84865374061\&partnerID=40\&md5=8e2b458028b5492266f085546a6804d9},
	author = {Esteves, T. and Quelhas, P. and Mendon{\c c}a, A.M. and Campilho, A.}
}
