Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

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
Download PDF
⟨ Previous Next ⟩

Abstract

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.

흉부 컴퓨터단층영상에서의 얻어진 폐 영상은 볼륨과 형태 등의 정량적인 정보들로서 진단과 수술 계획 등에 있어서 필연적 정보를 제공한다. 일반적인 영상분할은 이미지를 구성 요소영역이나 목적물에 따라 나누는 방법이다. 그러나 재분할을 하는 단계에서 최종영상은 에너지 최소화를 해결하는 정도에 의존하며, 분할은 응용대상의 관심 영역에서 객체나 물체의 경계에서 정지하게 된다. 가변형 능동모델은 컴퓨터 비젼, 영상처리 분야에서 광범위하게 사용되고 있다. 또한 영역 분할은 현재까지 많은 연구가 되고 있으며, Xu에 의해서 GVF라는 새로운 형태의 외부힘이 제안되고 있다. 본 논문에서 제안하는 알고리듬은 흉부 컴퓨터단층영상에서 실질을 자동 분할하기 위해서 에너지 최소화 방법을 사용하고, 영역분할을 위해 개선된 가변형 능동모델을 제안한다. 알고리듬은 정확한 영역분할을 위해서 기존 방법과 다른 개선된 외부힘을 정의하는 것이다. 임상의 실험은 흉부 컴퓨터단층영상에서 진단에 필요로 하는 폐 실질의 분할이 성공적인 결과를 나타내었다.

Keywords

References

  1. M. Doller, H. Kosch, "The MPEG-7 multimedia database system(MPEG-7 MMDB)," Journal of Systems and Software, vol. 81, pp.1559-1580, 2008 https://doi.org/10.1016/j.jss.2006.03.051
  2. T. McInerney, D. Terzopoulos, "Deformable models in medical image Analysis: A survey," Medical Image Analysis, vol. 1, no.2, pp.91-108; 1996 https://doi.org/10.1016/S1361-8415(96)80007-7
  3. 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 https://doi.org/10.1016/j.media.2005.02.002
  4. 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
  5. 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
  6. 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
  7. 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 https://doi.org/10.1016/S1076-6332(03)80517-2
  8. 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)
  9. C. Gonzalez, E. Woods, Digital Image Processing, 3rd Edition, Pearson Education Inc, 2008
  10. S. Choi, M. Kim, "Improvement of snake model for image segmentation," Proc. Coriference on Institute of embedded Engineering of Korea, pp.384-388,2008
  11. Chenyang Xu, Jerry L. Prince, "Generalized gradient vector flow external forces for active contours," Signal Processing, vol. 71, pp.131-139, 1998 https://doi.org/10.1016/S0165-1684(98)00140-6