Browse > Article

Segmentation of Lung and Lung Lobes in EBT Medical Images  

김영희 (경북대학교 컴퓨터학과)
이성기 (경북대학교 컴퓨터학과)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.3, 2004 , pp. 276-292 More about this Journal
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.
Keywords
EBT medical images; lung segmentation; pulmonary fissure extration;
Citations & Related Records
연도 인용수 순위
  • Reference
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   DOI   ScienceOn
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   DOI   ScienceOn
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   DOI   ScienceOn
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   DOI
7 A. V. Proto, and J. B. Ball Jr., 'Computed tomography of the major and minor fissures,' AJR, vol. 140, pp. 439-448, 1983   DOI
8 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   DOI
9 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   DOI   ScienceOn
10 D. Marr and E. Hildreth, 'Theory of edge detection,' in Proc. Roy. Soc. London, 1980, vol. B-207, pp. 187-217
11 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   DOI
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   DOI   ScienceOn
13 J F. Canny, 'A computational approach to edge detection,' IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-8, Nov. 1968
14 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   DOI   ScienceOn
15 M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision, PWS, 1999
16 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   DOI
17 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   DOI
18 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
19 M. Vaezi and B. Bavarian, 'Contrast dependant spread filters,' Proc, SPIE: Image Processing Algorithms and Techniques, vol. 1244, pp.100-107, 1990   DOI
20 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   DOI   ScienceOn
21 F. Bergholm, 'Edge focusing,' IEEE Trans. Pattern Anal. Machine Intell., vol. 9, no. 9, pp. 726-741, Nov. 1987   DOI
22 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   DOI