Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

Segmentation of Lung and Lung Lobes in EBT Medical Images

EBT 의료 영상에서 폐 영역 추출 및 폐엽 분할

  • Published : 2004.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper. we present methods that extract lung regions from chest EBT(electron beam tomography) images then segment the extracted lung region into lung lobes. We use histogram based thresholding and mathematical morphology for extracting lung regions. For detecting pulmonary fissures, we use edge detector and knowledge-based search method. We suggest this edge detector, which uses adaptive filter scale, to work very well for real edge and insensitive for edge by noise. Our experiments showed about 95% accuracy or higher in extracting lung regions and about 5 pixel distance error in detecting pulmonary fissures.

본 논문에서는 폐 질환 진단에 필요한 EBT(Electron Beam Tomography) 흉부 영상에서 폐 영역을 추출하고, 추출된 폐 영역에서 폐엽의 경계(pulmonary fissure)를 찾아 폐엽(lobe) 단위로 분할하는 방법을 제안하였다. EBT 흉부 영상을 분석하여 히스토그램을 기반으로 하는 임계치 방법과, 수학적형태학을 적용하여 폐 영역을 추출하였고 본 논문에서 제안한 adaptive filter scale을 사용한 에지 연산자와 폐엽 경계(pulmonary fissure)에 대한 해부학적 지식을 바탕으로 폐 영역을 폐엽 단위로 분할하였다. 본 논문에서 제안한 방법을 총 102개의 영상에 대해 실험한 결과는 폐 영역 추출에서 95% 이상의 정확도를 보여주었고 폐엽 경계선 추출에서 5 픽셀 이하의 거리오차를 나타내었다.

Keywords

References

  1. W. Stanford, and J. A. Rumbcrgcr, 'Ultrafast computed tomography in cardiac imaging ; Principles and practice,' Futura Publishing Company, Inc., 1992
  2. D. M. Denison, M. D. L. Morgan, and A. B. Millar, 'Estimation of regional gas and tissue volumes of the lung in supine man using computed tomography,' Thorax, vol. 41, pp. 620-628, 1986 https://doi.org/10.1136/thx.41.8.620
  3. M. S. Brown, M. F. McNitt-Gray, N. J. Mankovich, J. G. Goldin, J. Hiller, L. S. Wison, and D. R. Aberle, 'Method for segmenting chest CT image data using an anatomical model ; Preliminary results,' IEEE Trans. Medical Imaging, vol. 16, No.6, 1997 https://doi.org/10.1109/42.650879
  4. H. Adams, M. S. Bernard, and K. McConnochie, 'An appraisal of CT pulmonary density mapping in normal subjects,' Clin. Radiol, vol, 43, pp. 238-242, 1991 https://doi.org/10.1016/S0009-9260(05)80245-4
  5. M. kubo, N. Niki, S. Nakagawa, K. Eguchi, M. Kaneko, N. Moriyama, H. Omatsu, R. Kakinuma, and N. Yamaguchi, 'Extraction algorithm of pulmonary fissures from thin-section CT image based on linear features detector method,' IEEE Trans. Nuclear Science, vol.46, no. 6, Dec, 1999
  6. B. N. Raasch, E. W. Carsky, E. J. Lane, J. P. O'Callaghan, et al., 'Radiographic anatomy of the interlobar fissures: A study of 100 specimens,' AJR, vol. 138, pp. 1043-1049, 1982 https://doi.org/10.2214/ajr.138.6.1043
  7. L. R. Goodman, R. S. Golkow, R. M. Steiner, S. K. Teplick, et al., 'The right mid-lung window: A potential source of error in computed tomography of the lung,' Radiology, vol. 143, pp, 135-138, 1982 https://doi.org/10.1148/radiology.143.1.7063715
  8. A. V. Proto, and J. B. Ball Jr., 'Computed tomography of the major and minor fissures,' AJR, vol. 140, pp. 439-448, 1983 https://doi.org/10.2214/ajr.140.3.439
  9. Y. M. Berkman, Y.H. Auh, S,D. Davis, and E. Kazam, 'Anatomy of the minor fissure: Evaluation with thin-section CT,' Radiology, vol. 170, pp. 647-651. 1989 https://doi.org/10.1148/radiology.170.3.2916016
  10. J. Frija, P. Schmit, M. katz, D. Vadrot, et al., 'Computed tomography of pulmonary fissures: Normal anatomy,' J. Comput : Assist. Tomogr., vol. 6, pp. 1069-1074, 1982 https://doi.org/10.1097/00004728-198212000-00004
  11. M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision, PWS, 1999
  12. W. P. Eric, A. Hoffman, and M. Sonka, 'Segmentation of intrathoracic airway trees : A fuzzy logic approach,' IEEE Trans. Medical Imaging, vol. 17, No.4, 1998 https://doi.org/10.1109/42.730394
  13. D. Marr and E. Hildreth, 'Theory of edge detection,' in Proc. Roy. Soc. London, 1980, vol. B-207, pp. 187-217
  14. J F. Canny, 'A computational approach to edge detection,' IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-8, Nov. 1968
  15. M. Bertero, T. A. poggio, and V. Torre, 'Ill-posed problems in early vision,' Proc. IEEE, vol. 76, no. 8, pp. 869-889, Aug. 1988 https://doi.org/10.1109/5.5962
  16. H. Jeong and C. I. Kim, 'Adaptive determination of filter scales for edge detection,' IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-14, pp. 579-585. May. 1992 https://doi.org/10.1109/34.134062
  17. F. Bergholm, 'Edge focusing,' IEEE Trans. Pattern Anal. Machine Intell., vol. 9, no. 9, pp. 726-741, Nov. 1987 https://doi.org/10.1109/TPAMI.1987.4767980
  18. X. Changsheng and M. Songde, 'Adaptive edge detecting approach based on scale-space theory,' IEEE Inst. and Meas. Tech. Conference, Ottawa, Canada, May 19-21, 1997 https://doi.org/10.1109/IMTC.1997.603929
  19. G.Deng and L. W. Cahill, n An adaptive gaussian filter for noise reduction and edge detection,' Nuclear Science Symposium and Medical Imaging Conference, 1993, vol. 3, pp. 1615-1619
  20. T. N. Comsweet and J. I. Yellot, Jr., 'Intensity dependant spatial summstion,' J. Opt. Soc. Am., Part A, vol. 2, pp. 1769-1786, Oct. 1985 https://doi.org/10.1364/JOSAA.2.001769
  21. M. Vaezi and B. Bavarian, 'Contrast dependant spread filters,' Proc, SPIE: Image Processing Algorithms and Techniques, vol. 1244, pp.100-107, 1990 https://doi.org/10.1117/12.19500
  22. T. Chen, T. P. Chen, and L. M. Tsai, 'Computed quantification analysis of left ventricular wall motion from echocardiograrns,' Utrasonic Imaging, vol. 19, pp. 138-144, 1997 https://doi.org/10.1177/016173469701900204