Classification of Lung Disease Pattern Using Seeded Region Growing

  • James S. J. Wong
  • Tatjana Zrimec
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


Honeycombing is a disease pattern seen in High-Resolution Computed Tomography which allows a confident diagnosis of a number of diseases involving fibrosis of the lung. An accurate quantification of honeycombing allows radiologists to determine the progress of the disease process. Previous techniques commonly applied a classifier over the whole lung image to detect lung pathologies. This resulted in spurious classifications of honeycombing in regions where the presence of honeycombing was highly improbable. In this paper, we present a novel technique which uses a seeded region growing algorithm to guide the classifier to regions with potential honeycombing. We show that the proposed technique improves the accuracy of the honeycombing detection. The technique was tested using ten-fold cross validation on forty two images over eight different patients. The proposed technique classified regions of interests with an accuracy of 89.7%, sensitivity of 96.6% and a specificity of 88.6%.


Feature Selection Seeded Region Lung Region Lung Image Attenuation Level 
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 2006

Authors and Affiliations

  • James S. J. Wong
    • 1
  • Tatjana Zrimec
    • 2
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  2. 2.Centre for Health InformaticsUniversity of New South WalesSydneyAustralia

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