3d Lung Vessel Segmentation In Computed Tomography Angiography Images

Abstract

In this paper, a novel lung vessel segmentation method is introduced. In this method, some Reference Points (RPs) were determined by making use of the properties of unchangeable anatomical structure. Due to these RPs, truncus, left-right pulmonary artery, lobar segment vessels have been segmented and subsegment vessels have been detected by looking at the differences of intensities in lung region. If there is pulmonary emboli (PE), heart disease, or abnormal tissues, vessel structure doesn't regularly continue and decreases the sensitivity of segmentation. Using RPs, vessel structure becomes more definite and sensitivity of the segmentation increases. CTA images belonging 30 patients including different disease are examined and 95% of sensitivity is obtained. The performance of the method for lung vessel segmentation is found to be quite well for radiologists and it gives enough results to the surgeries medically.

Keywords

• -

References

1. J. M. Remy, L. I. Tillie, D. Szapiro, “CT angiography of pulmonary embolism in patients with underlying respiratory disease: impact of multislice CT on image quality and negative predictive value”. Eur Radiol 12:1971–1978, 2002.
2. J. E. Dalen, “Pulmonary embolism: What have we learned since Virchow”, Chest,122: 1440-1446, 2002.
3. H. P. Chan, L. Hadjiiski, C. Zhou, et al., “Computer- aided diagnosis of lung cancer and pulmonary embolism in computed tomography – a review”, Acad Radiol 15:535–555, 2008.
4. Y. Masutani, H. Macmahon, and K. Doi, “Computer-assisted embolism”, In SPIE Medical Imaging 2000, San Diego, USA, February 2000. of pulmonary
5. J. N. Kaftan, A. P. Kiraly, A. Bakai, M. Das, C. L. Novak, and T. Aach, “Fuzzy Pulmonary Vessel Segmentation in Contrast Enhanced CT data”, Medical Imaging, February 2008.
6. S. Ozekes, O. Osman, “Computerized Lung Nodule Detection Using 3D Feature Extraction and Learning Based Algorithms”, Journal of Medical Systems, Volume: 34 Issue: 2 Pages: 185-194, APR 2010.
7. S. Ozekes, O. Osman, O. N. Ucan, “Nodule Detection in the Lung Region, which is Segmented with Genetic Cellular Neural Networks, Using 3D Template Matching with Fuzzy Rule Based Thresholding”, Korean Journal of Radiology, Vol.9, No.1, pp.1-9, 2008.
8. R. Uppaluri, E. Hoffman, M. Sonka, P. Hartley, “Hunninghake, and G. Mclennan, “Computer recognition of regional lung disease patterns”, Am. J. Respir. Crit. Care Med. 160, 648–654, 1999.

9. Y. Uchiyama, S. Katsuragawa, H. Abe, J. Shiraishi, F. Li, Q. Li, C. Zhang, K. Suzuki, and K. Doi, “Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography”, Med. Phys. 30, 2440–2454, 2003.
10. I. Sluimer, P. Waes, M. Viergever, and B. Ginneken, “Computer aided diagnosis in high resolution CT of the lungs”, Med. Phys. 30, 3081–3090, 2003.
11. R. Uppaluri, T. Mitsa, M. Sonka, E. Hoffman, and G. McLennan, “Quantification of pulmonary emphysema from lung computed tomography images”, Am. J. Respir. Crit. Care Med. 156, 248– 254, 1997.
12. Y. Xu, M. Sonka, G. McLennan, J. Guo, and E. Hoffman, “MDCTbased 3-D texture classification of emphysema and early smoking related lung pathologies”, IEEE Trans. Med. Imaging 25, 464– 475, 2006.
13. A. P. Kiraly, E. Pichon, D. P. Naidich, and C. L. Novak, “Analysis of arterial subtrees affected by pulmonary emboli”, in SPIE Conference on Medical Imaging, May 2004, vol. 5370, pp. 1720–1729.
14. T. Buelow, R. Wiemker, T. Blaffert, C. Lorenz, and S. Renisch, “Automatic extraction of the pulmonary artery tree from multi-slice CT data”, in SPIE Medical Imaging, Apr. 2005, vol. 5746, pp. 730– 740.
15. C. Yuan, E. Lin, J. Millard, and J. Hwang, “Closed contour edge detection of blood vessel lumen and outer wall boundaries in black-blood images”, Magnetic Resonance Imaging, vol. 17, no. 2, pp. 257-266, February 1999.
16. R. Poli, and G. Valli, “An algorithm for real-time vessel enhancement and detection”, Computer Methods and Programs in Biomedicine, 1(52):1–22, November 1996.
17. X. Zhou, T. Hayashi, T. Hara, H. Fujita, R. Yokoyama, T. Kiryu, and H. Hoshi, “Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images”, Computerized Medical Imaging and Graphics 30, pp. 299–313, 2006.
18. Y. Masutani, T. Schiemann, and K. H. Höhne, “Vascular shape segmentation and structure extraction using a shape-based region-growing model”, In Medical Image Analysis and Computer Assisted Intervention (MICCAI), pages 1242–1249, October 1998.
19. H. Zhang, Z. Bian, D. Jiang, Z. Yuan, and M. Ye, “Level set method for pulmonary vessels extraction”, in IEEE International Conference on Image Processing. ICIP, pp. II: 1105–1108, 2003.
20. G. Agam, S. G. Armato, and C. Wu, “Vessel tree reconstruction in thoracic ct scans with application to nodule detection”, IEEE Transactions on Medical Imaging, 24(4):486–499, April 2005.
21. H. Shikata, G. McLennan, E. A. Hoffman, and M. Sonka, “Segmentation of Pulmonary Vascular Trees from Thoracic 3D CT Images”, International Journal of Biomedical Imaging September 2009 Haydar ÖZKAN was born in Kırşehir in 1979. He