title: Automatic segmentation of lung parenchyma in the presence of diseases based on curvature of ribs.
abstract: OBJECTIVE
  Segmentation of lungs using high-resolution computer tomographic images in the setting of diffuse lung diseases is a major challenge in medical image analysis. Threshold-based techniques tend to leave out lung regions that have increased attenuation, such as in the presence of interstitial lung disease. In contrast, streak artifacts can cause the lung segmentation to "leak" into the chest wall. The purpose of this work was to perform segmentation of the lungs using a technique that selects an optimal threshold for a given patient by comparing the curvature of the lung boundary to that of the ribs.
  
  
  METHODS
  Our automated technique goes beyond fixed threshold-based approaches to include lung boundary curvature features. One would expect the curvature of the ribs and the curvature of the lung boundary around the ribs to be very close. Initially, the ribs are segmented by applying a threshold algorithm followed by morphologic operations. The lung segmentation scheme uses a multithreshold iterative approach. The threshold value is verified until the curvature of the ribs and the curvature of the lung boundary are closely matched. The curve of the ribs is represented using polynomial interpolation, and the lung boundary is matched in such a way that there is minimal deviation from this representation. Performance of this technique was compared with conventional (fixed threshold) lung segmentation techniques on 25 subjects using a volumetric overlap fraction measure.
  
  
  RESULTS
  The performance of the rib segmentation technique was significantly different from conventional techniques with an average higher mean volumetric overlap fraction of about 5%.
  
  
  CONCLUSIONS
  The technique described here allows for accurate quantification of volumetric computed tomography and more advanced segmentation of abnormal areas.
authors.affiliation: Thoracic Imaging Research Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California, 924 Westwood Blvd., Suite 650, Los Angeles, CA 90024, USA. mithunp@cse.unsw.edu.au
authors.forename: Mithun N
authors.initials: MN
authors.lastname: Prasad
authors.forename: Matthew S
authors.initials: MS
authors.lastname: Brown
authors.forename: Shama
authors.initials: S
authors.lastname: Ahmad
authors.forename: Fereidoun
authors.initials: F
authors.lastname: Abtin
authors.forename: Jared
authors.initials: J
authors.lastname: Allen
authors.forename: Irene
authors.initials: I
authors.lastname: da Costa
authors.forename: Hyun J
authors.initials: HJ
authors.lastname: Kim
authors.forename: Michael F
authors.initials: MF
authors.lastname: McNitt-Gray
authors.forename: Jonathan G
authors.initials: JG
authors.lastname: Goldin
mesh_terms: D000465:Algorithms; D006801:Humans; D008168:Lung / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D008171:Lung Diseases / Q000000981:diagnostic imaging; D012272:Ribs / Q000033:anatomy & histology*; D014057:Tomography, X-Ray Computed / Q000379:methods*