Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images

Truong X. Tran, Marc L. Pusey, Ramazan S. Aygun

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The accuracy of detecting protein crystals for fluorescence microscopy images is very critical for high throughput and automated systems. Although the trace fluorescent labeling method could highlight protein crystals, reflection and emission from the fluorescence dye is not always due to crystal regions. Therefore, the analysis of the peak wavelength in the emission spectra of a fluorophore may not always yield effective results. In this paper, we show that using the subordinate color intensity corresponding to longer wavelengths than the peak wavelength of the emission spectra could improve the accuracy of protein crystal detection. Hence, we have built a segmentation method based on the percentile intensity of the subordinate color for trace fluorescently labeled (TFL’d) protein crystallization trial images. Compared to using the dominant color channel, our segmentation method on subordinate color channel was able to reduce the misclassification rate of likely-leads or crystals as non-crystals by the percentage of from 9.71% to 2.02% depending on the classifier. Similarly, the accuracy of classifiers were increased by the percentage of from 1.77% to 5.53%. Our method reached around 94% accuracy while keeping misclassification of likely-leads and crystals as non-crystals below 1%. Moreover, to evaluate the generalizability of our method, we have conducted new wet lab experiments on two proteins, Concanavalin A (Con A) and Ab inorganic pyrophosphate (AbIPPase), and the misclassification rate was below 1%. Our experiments show that using the subordinate channel may be more helpful for TFL’d protein trial image classification.

Original languageEnglish (US)
Pages (from-to)637-656
Number of pages20
JournalJournal of Fluorescence
Issue number3
StatePublished - May 1 2020

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Clinical Psychology
  • Social Sciences (miscellaneous)
  • Sociology and Political Science
  • Spectroscopy
  • Clinical Biochemistry
  • Law


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