Fusion of local and global detection systems to detect tuberculosis in chest radiographs

  • Authors:

  • Laurens Hogeweg;Christian Mol;Pim A. de Jong;Rodney Dawson;Helen Ayles;Bram van Ginneken

  • Affiliations:

  • Image Sciences Institute, University Medical Center Utrecht, The Netherlands and Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, The Netherlands;Image Sciences Institute, University Medical Center Utrecht, The Netherlands and Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, The Netherlands;Department of Radiology, University Medical Center Utrecht, The Netherlands;University of Cape Town Lung Institute, Cape Town, South Africa;Department of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom;Image Sciences Institute, University Medical Center Utrecht, The Netherlands and Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, The Netherlands

  • Venue:
  • MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

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.