The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Comparison of quantitative methods for cell‐shape analysis
Abstract
Summary: Morphology is an important large‐scale manifestation of the global organizational and physiological state of cells, and is commonly used as a qualitative or quantitative measure of the outcome of various assays. Here we evaluate several different basic representations of cell shape – binary masks, distance maps and polygonal outlines – and different subsequent encodings of those representations – Fourier and Zernike decompositions, and the principal and independent components analyses – to determine which are best at capturing biologically important shape variation. We find that principal components analysis of two‐dimensional shapes represented as outlines provide measures of morphology which are quantitative, biologically meaningful, human interpretable and work well across a range of cell types and parameter settings.
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Cites
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,707
Independent component analysis: algorithms and applications
Hyvärinen, A., & Oja, E. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00026-5 · Cited by 8,705
Fast and robust fixed-point algorithms for independent component analysis
Hyvarinen, A. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.761722 · Cited by 6,246
Face recognition using eigenfaces
Turk, M. A., & Pentland, A. P. (2002) · Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1991.139758 · Cited by 5,197
CellProfiler: image analysis software for identifying and quantifying cell phenotypes
Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., Guertin, D. A., Chang, J. H., Lindquist, R. A., Moffat, J., et al. (2006) · Genome Biology · doi:10.1186/gb-2006-7-10-r100 · Cited by 5,157
Snakes, shapes, and gradient vector flow
Xu, C., & Prince, J. L. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.661186 · Cited by 4,001
The expectation-maximization algorithm
Moon, T. K. (1996) · IEEE Signal Processing Magazine · doi:10.1109/79.543975 · Cited by 3,181
Research works printed but not linked: 20
- Artac M. Jogan M.&Leonardis A.(2002)Incremental PCA for on‐line visual learning and recognition.16th Int. Conf. on Pattern Recognition. 11–15 August 2002 p.30781. Quebec Canada .
- Burges C., 2005, Data Mining and Knowledge Discovery Handbook: A Complete Guide for Researchers and Practitioners
- 10.1115/1.1577356 · doi:10.1115/1.1577356
- 10.1145/363156.363172 · doi:10.1145/363156.363172
- Dryden I.L., 1998, Statistical Shape Analysis
- Havran C. Hupet L. Czyz J. Lee J. Vandendorpe L.&Verleysen M.(2002)independent component analysis for face authentication.Conf. on Knowledge‐Based Intelligent Information and Engineering Systems. 16–18 September 2002 pp.1207–1211. Crema Italy .
- 10.1007/BF00022960 · doi:10.1007/bf00022960
- Hill A.&Taylor C.J.(1994)Automatic landmark generation for point distribution models.5th British Machine Vision Conf.13–16 September 1994 pp.429–438. York UK . · doi:10.5244/c.8.42
- 10.1016/j.jim.2004.04.011 · doi:10.1016/j.jim.2004.04.011
- Keogh E. Wei L. Xi X. Lee S.‐H.&Vlachos M.(2006)LB_Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures.32nd Int. Conf. on Very Large Data Bases 12–15 September 2006 pp.882–893. Seoul Korea .
- Kwon O.‐K. Sim D.‐G.&Park R.‐H.(1996)New Hausdorff distances based on robust statistics for comparing images.Int. Conf. on Image Processing 16–19 September 1996 Lausanne Switzerland. pp.21–24.
- 10.2307/2529937 · doi:10.2307/2529937
- 10.1016/j.ygeno.2005.12.009 · doi:10.1016/j.ygeno.2005.12.009
- 10.1111/j.1365-2818.2005.01521.x · doi:10.1111/j.1365-2818.2005.01521.x
- Mauch S., 2003, Applied and Computational Mathematics
- Ramón Y, 1960, Studies on Vertebrate Neurogenesis
- Rapport R.L., 2005, Nerve Endings: The Discovery of the Synapse
- 10.1162/153244304322972667 · doi:10.1162/153244304322972667
- 10.1177/153303460500400106 · doi:10.1177/153303460500400106
- 10.1364/AO.19.001510 · doi:10.1364/ao.19.001510
Cited by
Data-analysis strategies for image-based cell profiling
Caicedo, J. C., Cooper, S., Heigwer, F., Warchal, S., Qiu, P., Molnar, C., Vasilevich, A. S., Barry, J. D., Bansal, H. S., Kraus, O., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4397 · Cited by 739
Mechanism of shape determination in motile cells
Keren, K., Pincus, Z., Allen, G. M., Barnhart, E. L., Marriott, G., Mogilner, A., & Theriot, J. A. (2008) · Nature · doi:10.1038/nature06952 · Cited by 719
Self-citationAn Adhesion-Dependent Switch between Mechanisms That Determine Motile Cell Shape
Barnhart, E. L., Lee, K.-C., Keren, K., Mogilner, A., & Theriot, J. A. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001059 · Cited by 310
Self-citationIntegrated intracellular organization and its variations in human iPS cells
Viana, M. P., Chen, J., Knijnenburg, T. A., Vasan, R., Yan, C., Arakaki, J. E., Bailey, M., Berry, B., Borensztejn, A., Brown, E. M., et al. (2023) · Nature · doi:10.1038/s41586-022-05563-7 · Cited by 132
“2), adapting a standard principal component analysis (PCA)-based dimensional reduction approach 16 .”
Self-citationEvolution of cellular morpho-phenotypes in cancer metastasis.
Wu, P.-H., Phillip, J. M., Khatau, S. B., Chen, W.-C., Stirman, J., Rosseel, S., Tschudi, K., Van Patten, J., Wong, M., Gupta, S., et al. (2015) · Scientific Reports · doi:10.1038/srep18437 · Cited by 117
Metadata
- Authors
- PINCUS, Z., & THERIOT, J. A.
- Venue
- Journal of Microscopy
- Publisher
- Wiley
- Published
- 2007-08
- Volume
- 227
- Issue
- 2
- Pages
- 140-156
- ISSN
- 0022-2720 (ISSN-L)
- 1365-2818
- OpenAlex
- W2028201091
- Cited by
- 288
- References
- 44
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- closed
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