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Clustering Methods for the Characterization of Synchrotron Radiation X-Ray Fluorescence Images of Human Carotid Atherosclerotic Plaque

De La Rosa, Nathaly LU ; Peruzzi, Niccolò LU orcid ; Dreier, Till LU orcid ; Truong, My LU orcid ; Johansson, Ulf LU ; Kalbfleisch, Sebastian LU ; Gonçalves, Isabel LU orcid and Bech, Martin LU orcid (2024) In Advanced Intelligent Systems 6(9).
Abstract

This study employs computational algorithms to automatically identify and classify features in X-Ray fluorescence (XRF) microscopy images. Principal component analysis (PCA) and unsupervised machine learning algorithms, such as Gaussian mixture (GM) clustering, are implemented to label features on a collection of XRF maps of human atherosclerotic plaque samples. The investigation involves the hard X-Ray nanoprobe (NanoMAX) at MAX IV synchrotron radiation facility, utilizing scanning transmission X-Ray microscopy (STXM) and XRF techniques. The analysis covers regions of interest scanned by the beam with a step size of 200 nm, yielding XRF maps of elements like calcium, iron, and zinc. These maps reveal intricate structures unsuitable for... (More)

This study employs computational algorithms to automatically identify and classify features in X-Ray fluorescence (XRF) microscopy images. Principal component analysis (PCA) and unsupervised machine learning algorithms, such as Gaussian mixture (GM) clustering, are implemented to label features on a collection of XRF maps of human atherosclerotic plaque samples. The investigation involves the hard X-Ray nanoprobe (NanoMAX) at MAX IV synchrotron radiation facility, utilizing scanning transmission X-Ray microscopy (STXM) and XRF techniques. The analysis covers regions of interest scanned by the beam with a step size of 200 nm, yielding XRF maps of elements like calcium, iron, and zinc. These maps reveal intricate structures unsuitable for manual labeling. However, they can be accurately classified in an automated fashion using GM. Prior to clustering, PCA is used to deal with repeated patterns and background areas. The resulting clusters are associated with different types of features, which can be identified as specific tissues confirmed by histology. Regions of high concentrations of phosphorus, sulfur, calcium, and iron are found in the samples. These regions are also observed in the STXM results as spots of low transmission that typically are associated with calcium deposits only.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
atherosclerosis, carotid plaque, clustering methods, unsupervised machine learning, X-Ray fluorescence microanalysis
in
Advanced Intelligent Systems
volume
6
issue
9
article number
2400052
publisher
John Wiley & Sons Inc.
external identifiers
  • scopus:85198633232
ISSN
2640-4567
DOI
10.1002/aisy.202400052
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2024 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH.
id
671a613b-31eb-4ff5-b2a3-c19713b7f5b0
date added to LUP
2024-11-27 12:05:56
date last changed
2025-04-04 15:04:59
@article{671a613b-31eb-4ff5-b2a3-c19713b7f5b0,
  abstract     = {{<p>This study employs computational algorithms to automatically identify and classify features in X-Ray fluorescence (XRF) microscopy images. Principal component analysis (PCA) and unsupervised machine learning algorithms, such as Gaussian mixture (GM) clustering, are implemented to label features on a collection of XRF maps of human atherosclerotic plaque samples. The investigation involves the hard X-Ray nanoprobe (NanoMAX) at MAX IV synchrotron radiation facility, utilizing scanning transmission X-Ray microscopy (STXM) and XRF techniques. The analysis covers regions of interest scanned by the beam with a step size of 200 nm, yielding XRF maps of elements like calcium, iron, and zinc. These maps reveal intricate structures unsuitable for manual labeling. However, they can be accurately classified in an automated fashion using GM. Prior to clustering, PCA is used to deal with repeated patterns and background areas. The resulting clusters are associated with different types of features, which can be identified as specific tissues confirmed by histology. Regions of high concentrations of phosphorus, sulfur, calcium, and iron are found in the samples. These regions are also observed in the STXM results as spots of low transmission that typically are associated with calcium deposits only.</p>}},
  author       = {{De La Rosa, Nathaly and Peruzzi, Niccolò and Dreier, Till and Truong, My and Johansson, Ulf and Kalbfleisch, Sebastian and Gonçalves, Isabel and Bech, Martin}},
  issn         = {{2640-4567}},
  keywords     = {{atherosclerosis; carotid plaque; clustering methods; unsupervised machine learning; X-Ray fluorescence microanalysis}},
  language     = {{eng}},
  number       = {{9}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Advanced Intelligent Systems}},
  title        = {{Clustering Methods for the Characterization of Synchrotron Radiation X-Ray Fluorescence Images of Human Carotid Atherosclerotic Plaque}},
  url          = {{http://dx.doi.org/10.1002/aisy.202400052}},
  doi          = {{10.1002/aisy.202400052}},
  volume       = {{6}},
  year         = {{2024}},
}