TY - JOUR
T1 - Rapid and accurate analysis of an x-ray fluorescence microscopy data set through gaussian mixture-based soft clustering methods
AU - Ward, Jesse
AU - Marvin, Rebecca
AU - O'Halloran, Thomas
AU - Jacobsen, Chris
AU - Vogt, Stefan
PY - 2013/10
Y1 - 2013/10
N2 - 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.
AB - 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.
KW - Gaussian mixture models
KW - Plasmodium falciparum
KW - X-ray fluorescence microscopy
KW - bioinorganic chemistry
KW - cluster analysis
KW - expectation maximization
KW - hard clustering
KW - image segmentation
KW - k-means clustering
KW - soft clustering
UR - http://www.scopus.com/inward/record.url?scp=84884562709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84884562709&partnerID=8YFLogxK
U2 - 10.1017/S1431927613012737
DO - 10.1017/S1431927613012737
M3 - Article
C2 - 23924688
AN - SCOPUS:84884562709
SN - 1431-9276
VL - 19
SP - 1281
EP - 1289
JO - Microscopy and Microanalysis
JF - Microscopy and Microanalysis
IS - 5
ER -