



@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},
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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.}
}
@article {2267,
	title = {Cell nuclei and cytoplasm joint segmentation using the sliding band filter},
	journal = {IEEE Transactions on Medical ImagingIEEE Trans. Med. Imaging},
	volume = {29},
	year = {2010},
	note = {Cited By (since 1996): 6Export Date: 21 November 2012Source: ScopusArt. No.: 5477157CODEN: ITMIDdoi: 10.1109/TMI.2010.2048253Language of Original Document: EnglishCorrespondence Address: Quelhas, P.; Instituto de Engenharia Biom{\'e}dica (INEB), 4200-465 Porto, Portugal; email: pedro.quelhas@gmail.comChemicals/CAS: DNA, 9007-49-2; Actins; DNA, 9007-49-2References: Bleau, A., Leon, L., Watershed-based segmentation and region merging (2000) Comput. Vis. Image Understand., 77 (3), pp. 317-370;Clocksin, W., Automatic segmentation of overlapping nuclei with high background variation using robust estimation and flexible contour models (2003) Proc. 12th Int. Conf. Image Anal. Process., pp. 682-687; 
Cloppet, F., Boucher, A., Segmentation of overlapping/aggregating nuclei cells in biological images (2008) Proc. 19th Int. Conf. Pattern Recognit. (ICPR 2008), pp. 1-4; 
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Fok, Y., Chan, J., Chin, R., Automated analysis of nerve-cell images using active contour models (1996) IEEE Trans. Med. Imag., 15 (3), pp. 353-368. , Jun; 
Harder, N., M-Bermudez, F., Godinez, W., Ellenberg, J., Eils, R., Rohr, K., Automated analysis of mitotic cell nuclei in 3-D fluorescence microscopy image sequences (2008) Workshop Bio-Image Inf.: Biol. Imag., Comput. Vis. Data Mining, pp. 17-18. , Santa Barbara, CA; 
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},
	month = {2010///},
	pages = {1463 - 1473},
	abstract = {Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells{\textquoteright} locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter{\textquoteright}s design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells{\textquoteright} nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95\%/69\% and 82\%/90\% for nuclei and cytoplasm detection respectively and an overall accuracy of 76\%. {\textcopyright} 2010 IEEE.},
	keywords = {actin, Actins, animal, Animals, article, Band filters, Biological research, cell aggregation, Cell analysis, Cell culture, Cell detection, Cell images, cell nucleus, Cell segmentation, Cell Shape, Cell shapes, Cell size, Cells, chemistry, Computer vision, Convergence filters, Convex shapes, cytoplasm, Data sets, Databases, Factual, Digital image storage, DNA, Drosophila melanogaster, factual database, Fluorescence, Fluorescence microscopy, Fundamental tools, High variability, Image analysis, Image datasets, image processing, image processing in biology, Image Processing, Computer-Assisted, Image quality, Image segmentation, Imaging systems, Improving performance, Individual cells, Joint segmentation, Location, methodology, Microscopy, Fluorescence, Multivariate analysis, multivariate image processing, New approaches, Nuclear detection, Overlap correction, reproducibility, Reproducibility of Results, Semi-automated, Shape estimation, Shape regularization, ultrastructure, Visual communication, Visual inspection},
	isbn = {02780062 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77955339493\&partnerID=40\&md5=f8cea406f7b2fcf901dc34fa940522b1},
	author = {Quelhas, P. and Marcuzzo, M. and Mendon{\c c}a, A.M. and Campilho, A.}
}
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	year = {2009},
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P. Kovesi, Symmetry and asymmetry from local phase, in: Proceedings of the 10th Australian Joint Conference on Artificial Intelligence, December 1997, pp. 185-190P. Kovesi, Edges are not just steps, in: Proceedings of the Fifth Asian Conference on Computer Vision, January 2002, pp. 822-827Liao, L., Noble, W.S., Combining pairwise sequence similarity and support vector machines for remote protein homology detection (2002) Proceedings of the Sixth Annual International Conference on Computational Molecular Biology, pp. 225-232. , ACM Press; 
Luc, V., Soille, P., Watersheds in digital spaces: an efficient algorithm based on immersion simulations (1991) IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (6), pp. 583-598; 
Mao, K., Zhao, P., Tan, P.-H., Supervised learning-based cell image segmentation for p53 immunohistochemistry (2006) IEEE Transactions on Biomedical Engineering, 53 (6), pp. 1153-1163; 
M. Marcuzzo, P. Quelhas, A. Campilho, A.M. Mendon{\c c}a, A.C. Campilho, Automatic cell segmentation from confocal microscopy images of the Arabidopsis root, in: IEEE International Symposium on Biomedical Imaging, May 2008, pp. 712-715Marcuzzo, M., Quelhas, P., Campilho, A., Mendon{\c c}a, A.M., Campilho, A.C., A hybrid approach for cell image segmentation (2008) Springer Lecture Notes in Computer Science, 5112, pp. 739-749. , Proceedings of the International Conference on Image Analysis and Recognition, Springer, Berlin; 
Meyer, F., Beucher, S., Morphological segmentation (1990) Journal of Visual Communication and Image Representation, 1 (1), pp. 21-46; 
Perona, P., Malik, J., Scale-space and edge detection using anisotropic diffusion (1990) IEEE Transactions on Pattern Analysis and Machine Intelligence, 7 (12), pp. 629-639; 
T. Roberts, S. Mckenna, N. Wuyts, T. Valentine, A. Bengough, Performance of low-level motion estimation methods for confocal microscopy of plant cells in vivo, in: IEEE Workshop on Motion \& Video Computing, February 2007, pp. 13-21Sathiya Keerthi, S., Lin, C.-J., Asymptotic behaviors of support vector machines with Gaussian kernel (2003) Neural Computation, 15 (7), pp. 1667-1689; 
Vapnik, V., (1998) Statistical Learning Theory, , Wiley, New York
},
	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.}
}
@conference {2338,
	title = {Automatic cell segmentation from confocal microscopy images of the Arabidopsis root},
	booktitle = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBIIEEE Int. Symp. Biomed. Imaging: Nano Macro, Proc., ISBI},
	series = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI},
	year = {2008},
	note = {Conference code: 73366Cited By (since 1996): 4Export Date: 21 November 2012Source: ScopusArt. No.: 4541095doi: 10.1109/ISBI.2008.4541095Language of Original Document: EnglishCorrespondence Address: Marcuzzo, M.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUPPortugalReferences: 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 (2007) Motion07, pp. 13-13; 
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; 
Garcia, B., Campilho, A., Scheres, B., Campilho, A., Automatic tracking of arabidopsis thaliana root meristem in confocal microscopy (2004) Proc. of the Inter. Conf. on Image Analysis and Recognition, pp. 166-174; 
Perona, P., Malik, J., Scale-space and edge detection using anisotropic diffusion (1990) IEEE Trans. on PAMI, 7 (12), pp. 629-639; 
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K., Image denoising by sparse 3d transform-domain collaborative filtering (2007) IEEE Trans. on Image Processing, 16 (8), pp. 2080-2095. , August; 
Luc, V., Soille, P., Watersheds in digital spaces: An efficient algorithm based on immersion simulations IEEE Trans. on PAMI, 13 (6); 
Meyer, F., Beucher, S., Morphological segmentation (1990) JVCIR, 1 (1), pp. 21-46. , Sept; 
Bleau, A., Leon, L., Watershed-based segmentation and region merging (2000) Comput. Vis. Image Underst, 77 (3), pp. 317-370; 
Kovesi, P., Symmetry and asymmetry from local phase (1997) Proc. of the Tenth Australian Joint Conf. on Artificial Intelligence, , December; 
Burges, C., A tutorial on support vector machines for pattern recognition (1998) Data Mining and Knowledge Discovery, 2 (2), pp. 121-167
Sponsors: Institute of Electrical and Electronic Engineers; Engineering in Medicine and Biology Society; Signal Processing Society},
	month = {2008///},
	pages = {712 - 715},
	address = {Paris},
	abstract = {In vivo observation and tracking of cell division in the Arabidopsis thaliana root meristem, by time-lapse confocal microscopy, is central to biology research. The research herein described is based on large amount of image data, which must be analyzed to determine the location and state of cells. The possibility of automating the process of cell detection/marking is an important step to provide research tools to the biologists in order to ease the search for a special event as cell division. This paper discusses an automatic cell segmentation method, which selects the best cell candidates from a starting watershed based image segmentation. The selection of individual cells is obtained using a Support Vector Machine (SVM) classifier, based on the shape and edge strength of the cells{\textquoteright} contour. The resulting segmentation is largely pruned of badly segmented cells, which can reduce the false positive detection of cell division. This is a good result on its own and a starting point for improvement of cell segmentation methodology. {\textcopyright}2008 IEEE.},
	keywords = {Biological cells, Biomedical image processing, Cell divisions, cell proliferation, Cell segmentation, Cells, confocal microscopy, image processing, Image segmentation, Microscopic examination, Support vector machines, Technical presentations},
	isbn = {9781424420032 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-51049123600\&partnerID=40\&md5=9e4652fa61936d105b4059db95e63f78},
	author = {Marcuzzo, M. and Quelhas, P. and Campilho, A. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2346,
	title = {A hybrid approach for arabidopsis root cell image segmentation},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5112 LNCS},
	year = {2008},
	note = {Conference code: 72695Cited By (since 1996): 3Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-540-69812-8_73Language of Original Document: EnglishCorrespondence Address: Marcuzzo, M.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUPPortugalReferences: 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, pp. 13-13 (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; 
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, eds, ICIAR 2004, Springer, Heidelberg; 
Perona, P., Malik, J., Scale-space and edge detection using anisotropic diffusion (1990) IEEE Trans. on PAMI, 7 (12), pp. 629-639; 
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K., Image denoising by sparse 3d transform-domain collaborative filtering (2007) IEEE Trans. on Image Processing, 16 (8), pp. 2080-2095; 
Luc, V., Soille, P., Watersheds in digital spaces: An efficient algorithm based on immersion simulations (1991) IEEE Trans. on PAMI, 13 (6), pp. 583-598; 
Meyer, F., Beucher, S., Morphological segmentation (1990) JVCIR, 1 (1), pp. 21-46; 
Bleau, A., Leon, L., Watershed-based segmentation and region merging (2000) Comput. Vis. Image Underst, 77 (3), pp. 317-370; 
Kovesi, P.: Symmetry and asymmetry from local phase. In: Proc. of the Tenth Australian Joint Conf. on Artificial Intelligence (December 1997)Burges, C., A tutorial on support vector machines for pattern recognition (1998) Data Mining and Knowledge Discovery, 2 (2), pp. 121-167
},
	month = {2008///},
	pages = {739 - 749},
	address = {Povoa de Varzim},
	abstract = {In vivo observation and tracking of the Arabidopsis thaliana root meristem, by time-lapse confocal microscopy, is important to understand mechanisms like cell division and elongation. The research herein described is based on a large amount of image data, which must be analyzed to determine the location and state of cells. The automation of the process of cell detection/marking is an important step to provide research tools for the biologists in order to ease the search for special events, such as cell division. This paper discusses a hybrid approach for automatic cell 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 the shape and edge strength of the cells{\textquoteright} contour. The merging criterion is based on edge strength along the line that connects adjacent cells{\textquoteright} centroids. The resulting segmentation is largely pruned of badly segmented and over-segmented cells, thus facilitating the study of cell division. {\textcopyright} 2008 Springer-Verlag Berlin Heidelberg.},
	keywords = {Arabidopsis (Arabidopsis thaliana), Arabidopsis thaliana (ELIP), Cell divisions, Cell image segmentation, cell proliferation, Cell segmentation, Cells, confocal microscopy, Digital image storage, Heidelberg (CO), Hybrid approaches, Image analysis, Image data, image processing, Image segmentation, Imaging techniques, In vivo observation, Individual (PSS 544-7), International conferences, Merging, Microscopic examination, Research tools, Root meristem, Strength (IGC: D5/D6), Support Vector Machine (SVM), Support vector machines, Time lapse confocal microscopy},
	isbn = {03029743 (ISSN); 3540698116 (ISBN); 9783540698111 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-47749134578\&partnerID=40\&md5=3b05fc0d5d004216e681d9c3e2199aac},
	author = {Marcuzzo, M. and Quelhas, P. and Campilho, A. and Mendon{\c c}a, A.M. and Campilho, A.}
}
