A generic approach to pathological lung segmentation

Awais Mansoor, Ulas Bagci*, Ziyue Xu, Brent Foster, Kenneth N. Olivier, Jason M. Elinoff, Anthony F. Suffredini, Jayaram K. Udupa, Daniel J. Mollura

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

132 Scopus citations


In this study, we propose a novel pathological lung segmentation method that takes into account neighbor prior constraints and a novel pathology recognition system. Our proposed framework has two stages; during stage one, we adapted the fuzzy connectedness (FC) image segmentation algorithm to perform initial lung parenchyma extraction. In parallel, we estimate the lung volume using rib-cage information without explicitly delineating lungs. This rudimentary, but intelligent lung volume estimation system allows comparison of volume differences between rib cage and FC based lung volume measurements. Significant volume difference indicates the presence of pathology, which invokes the second stage of the proposed framework for the refinement of segmented lung. In stage two, texture-based features are utilized to detect abnormal imaging patterns (consolidations, ground glass, interstitial thickening, tree-inbud, honeycombing, nodules, and micro-nodules) that might have been missed during the first stage of the algorithm. This refinement stage is further completed by a novel neighboring anatomy-guided segmentation approach to include abnormalities with weak textures, and pleura regions. We evaluated the accuracy and efficiency of the proposed method on more than 400 CT scans with the presence of a wide spectrum of abnormalities. To our best of knowledge, this is the first study to evaluate all abnormal imaging patterns in a single segmentation framework. The quantitative results show that our pathological lung segmentation method improves on current standards because of its high sensitivity and specificity and may have considerable potential to enhance the performance of routine clinical tasks.

Original languageEnglish (US)
Article number6850014
Pages (from-to)2293-2310
Number of pages18
JournalIEEE Transactions on Medical Imaging
Issue number12
StatePublished - Dec 1 2014
Externally publishedYes


  • Anatomy-guided segmentation
  • computed tomography
  • fuzzy connectedness
  • pathological lung segmentation
  • random forest

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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