Abdominal image segmentation using three-dimensional deformable models

Luomin Gao, David G. Heath, Elliot K. Fishman

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


RATIONALE AND OBJECTIVES. The authors develop a three-dimensional (3-D) deformable surface model-based segmentation scheme for abdominal computed tomography (CT) image segmentation. METHODS. A parameterized 3-D surface model was developed to represent the human abdominal organs. An energy function defined on the direction of the image gradient and the surface normal of the deformable model was introduced to measure the match between the model and image data. A conjugate gradient algorithm was adapted to the minimization of the energy function. RESULTS. Test results for synthetic images showed that the incorporation of surface directional information improved the results over those using only the magnitude of the image gradient. The algorithm was tested on 21 CT datasets. Of the 21 cases tested, 11 were evaluated visually by a radiologist and the results were judged to be without noticeable error. The other 10 were evaluated over a distance function. The average distance was less than 1 voxel. CONCLUSIONS. The deformable model-based segmentation scheme produces robust and acceptable outputs on abdominal CT images.

Original languageEnglish (US)
Pages (from-to)348-355
Number of pages8
JournalInvestigative radiology
Issue number6
StatePublished - 1998


  • Computed tomography
  • Image segmentation
  • Kidney
  • Three-dimensional imaging

ASJC Scopus subject areas

  • Medicine(all)


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