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Hierarchical Cluster Analysis to Aid Diagnostic Image Data Visualization of MS and Other Medical Imaging Modalities

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Imaging Mass Spectrometry

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1618))

Abstract

Perceiving abnormal regions in the images of different medical modalities plays a crucial role in diagnosis and subsequent treatment planning. In medical images to visually perceive abnormalities’ extent and boundaries requires substantial experience. Consequently, manually drawn region of interest (ROI) to outline boundaries of abnormalities suffers from limitations of human perception leading to inter-observer variability. As an alternative to human drawn ROI, it is proposed the use of a computer-based segmentation algorithm to segment digital medical image data.

Hierarchical Clustering-based Segmentation (HCS) process is a generic unsupervised segmentation process that can be used to segment dissimilar regions in digital images. HCS process generates a hierarchy of segmented images by partitioning an image into its constituent regions at hierarchical levels of allowable dissimilarity between its different regions. The hierarchy represents the continuous merging of similar, spatially adjacent, and/or disjoint regions as the allowable threshold value of dissimilarity between regions, for merging, is gradually increased.

This chapter discusses in detail first the implementation of the HCS process, second the implementation details of how the HCS process is used for the presentation of multi-modal imaging data (MALDI and MRI) of a biological sample, third the implementation details of how the process is used as a perception aid for X-ray mammogram readers, and finally the implementation details of how it is used as an interpretation aid for the interpretation of Multi-parametric Magnetic Resonance Imaging (mpMRI) of the Prostate.

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Author information

Authors and Affiliations

  1. Materials and Engineering Research Institute (MERI), Sheffield Hallam University, City Campus, Howard Street, Sheffield S1 1WB, UK

    Arul N. Selvan

  2. The Center for Mass Spectrometry Imaging, Biomolecular Science Research Center, Sheffield Hallam University, Sheffield, South Yorkshire, UK

    Laura M. Cole

  3. Department of Psychology, Sociology and Politics, Sheffield Hallam University, Sheffield, UK

    Lynne Spackman

  4. Department of Health and Wellbeing, Sheffield Hallam University, Sheffield, UK

    Sarah Naylor

  5. Allied Health Sciences, London South Bank University, 103 Borough Rd., London SE1 0AA, UK

    Chris Wright

Authors
  1. Arul N. Selvan
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  2. Laura M. Cole
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  3. Lynne Spackman
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  4. Sarah Naylor
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  5. Chris Wright
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Corresponding author

Correspondence to Arul N. Selvan .

Editor information

Editors and Affiliations

  1. The Center for Mass Spectrometry Imaging Biomolecular Science Research Center, Sheffield Hallam University, Sheffield, South Yorkshire, United Kingdom

    Laura M. Cole

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Selvan, A.N., Cole, L.M., Spackman, L., Naylor, S., Wright, C. (2017). Hierarchical Cluster Analysis to Aid Diagnostic Image Data Visualization of MS and Other Medical Imaging Modalities. In: Cole, L. (eds) Imaging Mass Spectrometry . Methods in Molecular Biology, vol 1618. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7051-3_10

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  • DOI: https://doi.org/10.1007/978-1-4939-7051-3_10

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7050-6

  • Online ISBN: 978-1-4939-7051-3

  • eBook Packages: Springer Protocols

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