Image Segmentation of Lung Parenchyma using Improved Deformable Model on Chest Computed Tomography

개선된 가변형 능동모델을 이용한 흉부 컴퓨터단층영상에서 폐 실질의 분할

  • 김창수 (부산카톨릭대학교 보건과학대학 방사선학과) ;
  • 최석윤 (고려대학교 대학원 의공학협동)
  • Published : 2009.10.31


We present an automated, energy minimized-based method for Lung parenchyma segmenting Chest Computed Tomography(CT) datasets. Deformable model is used for energy minimized segmentation. Quantitative knowledge including expected volume, shape of Chest CT provides more feature constrain to diagnosis or surgery operation planning. Segmentation subdivides an lung image into its consistent regions or objects. Depends on energy-minimizing, the level detail image of subdivision is carried. Segmentation should stop when the objects or region of interest in an application have been detected. The deformable model that has attracted the most attention to date is popularly known as snakes. Snakes or deformable contour models represent a special case of the general multidimensional deformable model theory. This is used extensively in computer vision and image processing applications, particularly to locate object boundaries, in the mean time a new type of external force for deformable models, called gradient vector flow(GVF) was introduced by Xu. Our proposed algorithm of deformable model is new external energy of GVF for exact segmentation. In this paper, Clinical material for experiments shows better results of proposal algorithm in Lung parenchyma segmentation on Chest CT.


  1. D. Bartz, D. Mayer, J. Fischer, S. Ley, et aI, "Hybrid Segmentation and Exploration of the Human Lungs," Proc. of IEEE Visualization, pp.177-184, 2003
  2. Chenyang Xu, Jerry L. Prince, "Generalized gradient vector flow external forces for active contours," Signal Processing, vol. 71, pp.131-139, 1998
  3. S.G. Armato, M.L. Giger, C.J. Moran, J.T. Blackburn, K. Doi, H. MacMahon, "Computerized Detection of Pulmonary Nodules on CT Scans," Radiographics, vol.19,pp.1303-1311,1999
  4. T. McInerney, D. Terzopoulos, "Deformable models in medical image Analysis: A survey," Medical Image Analysis, vol. 1, no.2, pp.91-108; 1996
  5. C. Gonzalez, E. Woods, Digital Image Processing, 3rd Edition, Pearson Education Inc, 2008
  6. A.P. Kiraly, W.E. Higgins, G. Mclennan, E.A. Hoffman, J.M. Reinhardt, "Three-dimensional Human Airway Segmentation Methods for Clinical Virtual Bronchoscopy," Academic Radiology, vol. 9,pp.1153-1168,2002
  7. S. Choi, C.S. Kim, "Automatic Initialization snake active contours for the segmentation of chest wall images," Journal of Korean Society of Medical Informatics, 2008(in press)
  8. S. Choi, M. Kim, "Improvement of snake model for image segmentation," Proc. Coriference on Institute of embedded Engineering of Korea, pp.384-388,2008
  9. B.V. Ginneken, M.B. Stegmann, M. Loog, "Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database," Medical Image Analysis, vol. 10, pp.19-40, 2006
  10. M.S. Brown, M.F. McNitt-Gray, N.l Mankovich, J.G. Goldin, J. Hiller, L.S. Wilson, D.R. Aberle, "Method for Segmenting Chest CT hnage Data Using an Anatomical Model: Preliminary Results," IEEE Transactions on Medical Imaging, vol. 16, no.6, pp.829-839, 1997
  11. M. Doller, H. Kosch, "The MPEG-7 multimedia database system(MPEG-7 MMDB)," Journal of Systems and Software, vol. 81, pp.1559-1580, 2008