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Time Series Case Based Reasoning for Image Categorisation

  • Ashraf Elsayed
  • Mohd Hanafi Ahmad Hijazi
  • Frans Coenen
  • Marta García-Fiñana
  • Vanessa Sluming
  • Yalin Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6880)

Abstract

This paper describes an approach to Case Based Reasoning (CBR) for image categorisation. The technique is founded on a time series analysis mechanism whereby images are represented as time series (curves) and compared using time series similarity techniques. There are a number of ways in which images can be represented as time series, this paper explores two. The first considers the entire image whereby the image is represented as a sequence of histograms. The second considers a particular feature (region of interest) contained across an image collection, which can then be represented as a time series. The proposed techniques then use dynamic time warping to compare image curves contained in a case base with that representing a new image example. The focus for the work described is two medical applications: (i) retinal image screening for Age-related Macular Degeneration (AMD) and (ii) the classification of Magnetic Resonance Imaging (MRI) brain scans according to the nature of the corpus callosum, a particular tissue feature that appears in such images. The proposed technique is described in detail together with a full evaluation in terms of the two applications.

Keywords

Case Based Reasoning Image Analysis Time Series Analysis Dynamic Time warping 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ashraf Elsayed
    • 1
  • Mohd Hanafi Ahmad Hijazi
    • 1
    • 5
  • Frans Coenen
    • 1
  • Marta García-Fiñana
    • 2
  • Vanessa Sluming
    • 3
  • Yalin Zheng
    • 4
  1. 1.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK
  2. 2.Centre for Medical Statistics and Health EvaluationUniversity of LiverpoolLiverpoolUK
  3. 3.School of Health SciencesUniversity of LiverpoolLiverpoolUK
  4. 4.Department of Eye and Vision Science, Institute of Ageing and Chronic DiseaseUniversity of LiverpoolLiverpoolUK
  5. 5.School of Engineering and Information TechnologyUniversiti Malaysia SabahKota KinabaluMalaysia

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