Rapid and accurate analysis of an x-ray fluorescence microscopy data set through gaussian mixture-based soft clustering methods

Jesse Ward*, Rebecca Marvin, Thomas O'Halloran, Chris Jacobsen, Stefan Vogt

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


X-ray fluorescence (XRF) microscopy is an important tool for studying trace metals in biology, enabling simultaneous detection of multiple elements of interest and allowing quantification of metals in organelles without the need for subcellular fractionation. Currently, analysis of XRF images is often done using manually defined regions of interest (ROIs). However, since advances in synchrotron instrumentation have enabled the collection of very large data sets encompassing hundreds of cells, manual approaches are becoming increasingly impractical. We describe here the use of soft clustering to identify cell ROIs based on elemental contents, using data collected over a sample of the malaria parasite Plasmodium falciparum as a test case. Soft clustering was able to successfully classify regions in infected erythrocytes as parasite, food vacuole, host, or background. In contrast, hard clustering using the k-means algorithm was found to have difficulty in distinguishing cells from background. While initial tests showed convergence on two or three distinct solutions in 60% of the cells studied, subsequent modifications to the clustering routine improved results to yield 100% consistency in image segmentation. Data extracted using soft cluster ROIs were found to be as accurate as data extracted using manually defined ROIs, and analysis time was considerably improved.

Original languageEnglish (US)
Pages (from-to)1281-1289
Number of pages9
JournalMicroscopy and Microanalysis
Issue number5
StatePublished - Oct 2013


  • Gaussian mixture models
  • Plasmodium falciparum
  • X-ray fluorescence microscopy
  • bioinorganic chemistry
  • cluster analysis
  • expectation maximization
  • hard clustering
  • image segmentation
  • k-means clustering
  • soft clustering

ASJC Scopus subject areas

  • Instrumentation


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