Methods for automatic microarray image segmentation

Katzer M, Kummert F, Sagerer G (2003)
IEEE Transactions on Nanobioscience 2(4): 202-214.

Journal Article | Published | English

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This paper describes image processing methods for automatic spotted microarray image analysis. Automatic gridding is important to achieve constant data quality and is, therefore, especially interesting for large-scale experiments as well as for integration of microarray expression data from different sources. We propose a Markov random field (MRF) based approach to high-level grid segmentation, which is robust to common problems encountered with array images and does not require calibration. We also propose an active contour method for single-spot segmentation. Active contour models describe objects in images by properties of their boundaries. Both MRFs and active contour models have been used in various other computer vision applications. The traditional active contour model must be generalized for successful application to microarray spot segmentation. Our active contour model is employed for spot detection in the MRF score functions as well as for spot signal segmentation in quantitative array image analysis. An evaluation using several image series from different sources shows the robustness of our methods.
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Katzer M, Kummert F, Sagerer G. Methods for automatic microarray image segmentation. IEEE Transactions on Nanobioscience. 2003;2(4):202-214.
Katzer, M., Kummert, F., & Sagerer, G. (2003). Methods for automatic microarray image segmentation. IEEE Transactions on Nanobioscience, 2(4), 202-214.
Katzer, M., Kummert, F., and Sagerer, G. (2003). Methods for automatic microarray image segmentation. IEEE Transactions on Nanobioscience 2, 202-214.
Katzer, M., Kummert, F., & Sagerer, G., 2003. Methods for automatic microarray image segmentation. IEEE Transactions on Nanobioscience, 2(4), p 202-214.
M. Katzer, F. Kummert, and G. Sagerer, “Methods for automatic microarray image segmentation”, IEEE Transactions on Nanobioscience, vol. 2, 2003, pp. 202-214.
Katzer, M., Kummert, F., Sagerer, G.: Methods for automatic microarray image segmentation. IEEE Transactions on Nanobioscience. 2, 202-214 (2003).
Katzer, Mathias, Kummert, Franz, and Sagerer, Gerhard. “Methods for automatic microarray image segmentation”. IEEE Transactions on Nanobioscience 2.4 (2003): 202-214.
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16 Citations in Europe PMC

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Automatic microarray spot segmentation using a Snake-Fisher model.
Ho J, Hwang WL., IEEE Trans Med Imaging 27(6), 2008
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Probe classification of on-off type DNA microarray images with a nonlinear matching measure.
Ryu M, Kim JD, Min BG, Kim J, Kim YY., J Biomed Opt 11(1), 2006
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WaveRead: automatic measurement of relative gene expression levels from microarrays using wavelet analysis.
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Storage and transmission of microarray images.
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Impact of microarray technology in nutrition and food research.
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Segmentation of cDNA microarray spots using markov random field modeling.
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A multichannel order-statistic technique for cDNA microarray image processing.
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33 References

Data provided by Europe PubMed Central.


Sequential quadratic programming
Boggs, Acta Numerica (), 1995

SchÜrmann, Pattern Classification (), 1996
A tutorial on support vector machines for pattern recognition
Burges, Data Mining Knowl. Discovery 2(2), 1998

Chang, LIBSVM: A library for support vector machines (), 2001
Gene discovery using the maize genome database ZmDB.
Gai X, Lal S, Xing L, Brendel V, Walbot V., Nucleic Acids Res. 28(1), 2000
PMID: 10592191
The Stanford Microarray Database.
Sherlock G, Hernandez-Boussard T, Kasarskis A, Binkley G, Matese JC, Dwight SS, Kaloper M, Weng S, Jin H, Ball CA, Eisen MB, Spellman PT, Brown PO, Botstein D, Cherry JM., Nucleic Acids Res. 29(1), 2001
PMID: 11125075


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