Automated lung nodule segmentation using dynamic programming and EM based classification

Ning Xu, Narendra Ahuja, Ravi Bansal

Research output: Contribution to journalConference article


In this paper we present a robust and automated algorithm to segment lung nodules in three dimensional (3D) Computed Tomography (CT) volume dataset. The nodule is segmented out in slice-per-slice basis, that is, we first process each CT slice separately to extract two dimensional (2D) contours of the nodule which can then be stacked together to get the whole 3D surface. The extracted 2D contours are optimal as we utilize dynamic programming based optimization algorithm. To extract each 2D contour, we utilize a shape based constraint. Given a physician specified point on the nodule, we blow a circle which gives us rough initialization of the nodule from where our dynamic programming based algorithm estimates the optimal contour. As a nodule can be calcified, we pre-process a small region-of-interest (ROI), around the physician selected point on the nodule boundary, using the Expectation Maximization (EM) based algorithm to classify and remove calcification. Our proposed approach can be consistently and robustly used to segment not only the solitary nodules but also the nodules attached to lung walls and vessels.

Original languageEnglish (US)
Pages (from-to)666-676
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4684 II
StatePublished - Jan 1 2002
EventMedical Imaging 2002: Image Processing - San Diego, CA, United States
Duration: Feb 24 2002Feb 28 2002


  • Calcification Pattern
  • Dynamic Programming
  • Expectation Maximization
  • Lung nodule
  • Segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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