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Global and Local Multi-valued Dissimilarity-Based Classification: Application to Computer-Aided Detection of Tuberculosis

  • Yulia Arzhaeva
  • Laurens Hogeweg
  • Pim A. de Jong
  • Max A. Viergever
  • Bram van Ginneken
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

In many applications of computer-aided detection (CAD) it is not possible to precisely localize lesions or affected areas in images that are known to be abnormal. In this paper a novel approach to computer-aided detection is presented that can deal effectively with such weakly labeled data. Our approach is based on multi-valued dissimilarity measures that retain more information about underlying local image features than single-valued dissimilarities. We show how this approach can be extended by applying it locally as well as globally, and by merging the local and global classification results into an overall opinion about the image to be classified. The framework is applied to the detection of tuberculosis (TB) in chest radiographs. This is the first study to apply a CAD system to a large database of digital chest radiographs obtained from a TB screening program, including normal cases, suspect cases and cases with proven TB. The global dissimilarity approach achieved an area under the ROC curve of 0.81. The combination of local and global classifications increased this value to 0.83.

Keywords

Training Image Vote Rule Abnormal Image Feature Histogram Local Image Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yulia Arzhaeva
    • 1
    • 2
  • Laurens Hogeweg
    • 1
  • Pim A. de Jong
    • 1
  • Max A. Viergever
    • 1
  • Bram van Ginneken
    • 1
  1. 1.Image Sciences InstituteUniversity Medical CenterUtrechtThe Netherlands
  2. 2.CSIRO Mathematical and Information SciencesAustralia

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