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Rapid and Accurate Analysis of an X-Ray Fluorescence Microscopy Data Set through Gaussian Mixture-Based Soft Clustering Methods

Published online by Cambridge University Press:  07 August 2013

Jesse Ward ,
Rebecca Marvin ,
Thomas O'Halloran ,
Chris Jacobsen  and
Stefan Vogt
Jesse Ward
Affiliation:
X-Ray Science Division, Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
Rebecca Marvin
Affiliation:
Department of Chemistry and Chemistry of Life Processes, Northwestern University, Evanston, IL 60208, USA
Thomas O'Halloran
Affiliation:
Department of Chemistry and Chemistry of Life Processes, Northwestern University, Evanston, IL 60208, USA Interdepartmental Biological Sciences, Northwestern University, Evanston, IL 60208, USA
Chris Jacobsen
Affiliation:
X-Ray Science Division, Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
Stefan Vogt*
Affiliation:
X-Ray Science Division, Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
*
*Corresponding author. E-mail: svogt@aps.anl.gov
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Abstract

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.

Keywords

X-ray fluorescence microscopycluster analysissoft clusteringhard clusteringGaussian mixture modelsk-means clusteringexpectation maximizationimage segmentationbioinorganic chemistryPlasmodium falciparum
Type
Techniques and Software Development
Information
Microscopy and Microanalysis , Volume 19 , Issue 5 , October 2013 , pp. 1281 - 1289
Copyright
Copyright © Microscopy Society of America 2013 

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