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@ARTICLE{OrosPeusquens:16489,
      author       = {Oros-Peusquens, A.M. and Matusch, A. and Becker, J.S. and
                      Shah, N.J.},
      title        = {{A}utomatic segmentation of tissue sections using the
                      multielement information provided by {LA}-{ICP}-{MS} imaging
                      and k-means cluster analysis},
      journal      = {International journal of mass spectrometry},
      volume       = {307},
      issn         = {1387-3806},
      address      = {[S.l.]},
      publisher    = {Elsevier Science},
      reportid     = {PreJuSER-16489},
      year         = {2011},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {Laser Ablation Inductively Coupled Plasma Mass Spectrometry
                      (LA-ICP-MS) is an established and powerful tool to analyse
                      the distribution of elements in tissue sections. Among other
                      applications, the technique is expected to play a central
                      role in the understanding of normal and pathological element
                      distributions in brain tissue.In order to interpret the
                      distribution of elements such as the bio-metals Cu, Zn, Fe
                      and Mn and proceed to an element-based comparison between
                      groups of samples, it is necessary to anatomically parcel
                      the tissue section into regions-of-interest and to average
                      element signals across these regions. This categorization,
                      also termed segmentation, can be done manually, but the
                      support of automated procedures is highly desirable,
                      especially in order to (1) identify groups of pixels with
                      similar elemental fingerprint, termed clusters, and to
                      determine which degree of discrimination is reasonable; (2)
                      segment anatomical structures known to exhibit substructure
                      but without clearly defined borders, such as the healthy
                      cortex, zones of tumours or ischemic lesions, in an
                      observer-independent way; and (3) to investigate correlation
                      between the distribution of elements in tissue and phenomena
                      which incorporate contributions from several elements in a
                      convoluted way, such as the origin of contrast in magnetic
                      resonance imaging (MRI) experiments.The multi-parametric
                      information provided by LA-ICP-MS lends itself naturally to
                      multivariate analysis. This study provides a new way to
                      synthesise the information distributed over many element
                      images by demonstrating the possibility to segment tissue
                      sections into biologically meaningful substructures. This
                      data-driven, observer-independent categorization was based
                      on k-means clustering. The optimal number of clusters was
                      determined based on the silhouette method.Segmentation of
                      healthy tissue resulted in a set of substructures in perfect
                      congruence to the anatomical architecture. Segmentation of
                      ischemic lesions identified a number of regions with
                      different fingerprints of C, P. Fe, Cu and Zn deposits.
                      Clustering provides a promising way of combining the
                      information present in several element images and reveals
                      structure which is not entirely present in any isolated
                      image.As a useful by-product of this study we have found a
                      promising method for investigating the optimal line length
                      within the process of image reconstruction from the
                      continuous stream of raw data points. Images were
                      characterized by their tensor of inertia, in image- as well
                      as in Fourier dual-space (k-space) and changes in the ratio
                      of the intrinsic moments of inertia or the orientation of
                      the principal axes were found to closely describe the
                      optimum orientation. The first results look very
                      encouraging, but the method must be extensively tested
                      before it can be used as an automatic procedure.In
                      conclusion, cluster analysis of mass spectrometric imaging
                      data allows one to define the fingerprint element
                      distribution of different anatomically or functionally
                      distinct regions and opens a new way for the study of
                      correlation between the element distribution and related
                      phenomena. (C) 2011 Elsevier B.V. All rights reserved.},
      keywords     = {J (WoSType)},
      cin          = {ZCH / INM-4 / INM-2},
      ddc          = {530},
      cid          = {I:(DE-Juel1)ZCH-20090406 / I:(DE-Juel1)INM-4-20090406 /
                      I:(DE-Juel1)INM-2-20090406},
      pnm          = {Funktion und Dysfunktion des Nervensystems (FUEK409) /
                      89573 - Neuroimaging (POF2-89573)},
      pid          = {G:(DE-Juel1)FUEK409 / G:(DE-HGF)POF2-89573},
      shelfmark    = {Physics, Atomic, Molecular $\&$ Chemical / Spectroscopy},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000295864100034},
      doi          = {10.1016/j.ijms.2011.03.014},
      url          = {https://juser.fz-juelich.de/record/16489},
}