Nathaly De La Rosa, Niccolò Peruzzi, Till Dreier, My Truong, Ulf Johansson, Sebastian Kalbfleisch, Isabel Gonçalves, Martin Bech
{"title":"Clustering Methods for the Characterization of Synchrotron Radiation X-Ray Fluorescence Images of Human Carotid Atherosclerotic Plaque","authors":"Nathaly De La Rosa, Niccolò Peruzzi, Till Dreier, My Truong, Ulf Johansson, Sebastian Kalbfleisch, Isabel Gonçalves, Martin Bech","doi":"10.1002/aisy.202400052","DOIUrl":null,"url":null,"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>","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":null,"pages":null},"PeriodicalIF":6.8000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400052","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202400052","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
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 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.
本研究采用计算算法自动识别 X 射线荧光(XRF)显微镜图像中的特征并对其进行分类。该研究采用主成分分析(PCA)和无监督机器学习算法(如高斯混合(GM)聚类)来标记人类动脉粥样硬化斑块样本的 XRF 图谱集合上的特征。这项研究涉及 MAX IV 同步辐射设施的硬 X 射线纳米探针(NanoMAX),利用了扫描透射 X 射线显微镜(STXM)和 XRF 技术。分析范围包括光束以 200 纳米的步长扫描的感兴趣区域,得出钙、铁和锌等元素的 XRF 图谱。这些地图显示了不适合人工标记的复杂结构。不过,可以使用 GM 自动对它们进行准确分类。在进行聚类之前,先使用 PCA 处理重复模式和背景区域。由此产生的聚类与不同类型的特征相关联,这些特征可通过组织学确认为特定的组织。在样本中发现了磷、硫、钙和铁的高浓度区域。这些区域在 STXM 结果中也可观察到,通常只与钙沉积物相关的低透射点。