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Data Analytics for Protein Crystallization pp 177–197Cite as

Crystal Image Region Segmentation

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Part of the book series: Computational Biology ((COBO,volume 25))

Abstract

In general, a single thresholding technique is developed or enhanced to separate foreground objects from the background for a domain of images. This idea may not generate satisfactory results for all images in a dataset, since different images may require different types of thresholding methods for proper binarization or segmentation. To overcome this problem, this chapter explains “super-thresholding” method that utilizes a supervised classifier to decide an appropriate thresholding method for a specific image. This method provides a generic framework that allows selection of the best thresholding method among different thresholding techniques that are beneficial for the problem domain. A classifier model is built using features extracted priori from the original image only or posteriori by analyzing the outputs of thresholding methods and the original image. This model is applied to identify the thresholding method for new images of the domain.

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Notes

  1. 1.

    https://code.google.com/p/randomforest-matlab/.

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Acknowledgements

\(\copyright \)2017 IEEE. Reprinted with Permission, from I. Dinç, S. Dinç, M. Sigdel, M. S. Sigdel, M. L. Pusey; R. S. Aygün, “Super-thresholding: Supervised Thresholding of Protein Crystal Images,” in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 14, no. 4, pp. 986–998, July–Aug. 1 2017. doi: https://doi.org/10.1109/TCBB.2016.2542811.

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Authors and Affiliations

  1. iXpressGenes, Inc., Huntsville, AL, USA

    Marc L. Pusey

  2. University of Alabama in Huntsville, Huntsville, AL, USA

    Ramazan Savaş Aygün

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  1. Marc L. Pusey
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  2. Ramazan Savaş Aygün
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Correspondence to Marc L. Pusey .

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Pusey, M.L., Aygün, R.S. (2017). Crystal Image Region Segmentation. In: Data Analytics for Protein Crystallization. Computational Biology, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-58937-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-58937-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58936-7

  • Online ISBN: 978-3-319-58937-4

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