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Fusion of Local and Global Detection Systems to Detect Tuberculosis in Chest Radiographs

  • Laurens Hogeweg
  • Christian Mol
  • Pim A. de Jong
  • Rodney Dawson
  • Helen Ayles
  • Bram van Ginneken
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6363)

Abstract

Automatic detection of tuberculosis (TB) on chest radiographs is a difficult problem because of the diverse presentation of the disease. A combination of detection systems for abnormalities and normal anatomy is used to improve detection performance. A textural abnormality detection system operating at the pixel level is combined with a clavicle detection system to suppress false positive responses. The output of a shape abnormality detection system operating at the image level is combined in a next step to further improve performance by reducing false negatives. Strategies for combining systems based on serial and parallel configurations were evaluated using the minimum, maximum, product, and mean probability combination rules. The performance of TB detection increased, as measured using the area under the ROC curve, from 0.67 for the textural abnormality detection system alone to 0.86 when the three systems were combined. The best result was achieved using the sum and product rule in a parallel combination of outputs.

Keywords

Receiver Operator Characteristic Curve Chest Radiograph Linear Discriminant Analysis Abnormal Image Training Database 
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 2010

Authors and Affiliations

  • Laurens Hogeweg
    • 1
    • 3
  • Christian Mol
    • 1
    • 3
  • Pim A. de Jong
    • 2
  • Rodney Dawson
    • 4
  • Helen Ayles
    • 5
  • Bram van Ginneken
    • 1
    • 3
  1. 1.Image Sciences InstituteUniversity Medical Center UtrechtThe Netherlands
  2. 2.Department of RadiologyUniversity Medical Center UtrechtThe Netherlands
  3. 3.Diagnostic Image Analysis GroupRadboud University Nijmegen Medical CentreThe Netherlands
  4. 4.University of Cape Town Lung InstituteCape TownSouth Africa
  5. 5.Department of Infectious and Tropical DiseasesLondon School of Hygiene & Tropical MedicineLondonUnited Kingdom

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