Journal of International Society for Simulation Surgery

International Society for Simulation Surgery (국제컴퓨터가상수술학회)
권호 표지
DOI QR코드

DOI QR Code

Segmentation of Arterial Vascular Anatomy around the Stomach based on the Region Growing Based Method

  • Kang, Jiwoo (Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University) ;
  • Kim, Doyoung (Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University) ;
  • Lee, Sanghoon (Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University)
  • Received : 2014.11.21
  • Accepted : 2014.12.07
  • Published : 2014.12.20
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose The region growing has a critical problem that it often extract vessels with unexpected objects such as a bone which has a similar intensity characteristics to the vessel. We propose the new method to extract arterial vascular anatomy around the stomach from the CTA volume without the post-processing. Materials and Methods Our method, which is also based on the region growing, requires the two seed points from the use. I automatically extracts perigastric arteries using the adaptive region growing method and it does not need any post-processing. Results The three region growing based methods are used to extract perigastric arteries - the conventional region growings with restrict and loose thresholds each and the proposed method. The 3D visualization from the result of our method shows our method extracted the all required arteries for gastric surgery. Conclusion By extracting perigastric arteries using the proposed method, over-segmentation problem that unexpected anatomical objects such as a rib or backbone are also segmented does not occurs anymore. The proposed method does not need to sensitively determine the thresholds of the similarity function. By visualizing the result, the preoperative simulation of arterial vascular anatomy around the stomach can be possible.

Keywords

References

  1. Sonka M, Vaclav H, Roger B. Image processing, analysis, and machine vision. Cengage Learning, 2014.
  2. Felzenszwalb PF, Daniel PH. "Efficient graph-based image segmentation." International Journal of Computer Vision 2004;59(2):167-181 https://doi.org/10.1023/B:VISI.0000022288.19776.77
  3. Zaidi Habib, Issam El Naqa. "PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques." European journal of nuclear medicine and molecular imaging 2010;37(11):2165-2187 https://doi.org/10.1007/s00259-010-1423-3
  4. Gao Y, Jean FM, Norman K, Jose A. "Optimal region growing segmentation and its effect on classification accuracy." International Journal of Remote Sensing 2011;32(13):3747-3763 https://doi.org/10.1080/01431161003777189
  5. Tang J."A color image segmentation algorithm based on region growing." Computer Engineering and Technology (ICCET), 2010 2nd International Conference on. Vol. 6. IEEE, 2010.
  6. Mendoza CS, Begoria A, Carmen S, Tomoas G. "Fast parameter-free region growing segmentation with application to surgical planning." Machine Vision and Applications 2012;23(1):165-177 https://doi.org/10.1007/s00138-010-0274-z
  7. Kumar M., Kamal KM. "A Texture based tumor detection and automatic segmentation using seeded region growing method." International Journal of Computer Technology and Applications 2011;2(4).
  8. Qin AK, David AC. "Multivariate image segmentation using semantic region growing with adaptive edge penalty." Image Processing, IEEE Transactions on 2010;19(8):2157-2170 https://doi.org/10.1109/TIP.2010.2045708
  9. Kass M, Andrew W, Demetri T. "Snakes: Active contour models." International Journal of Computer Vision 1988;1(4):321-331 https://doi.org/10.1007/BF00133570
  10. Malladi R, James AS, Baba CV. "Shape modeling with front propagation: A level set approach." Pattern Analysis and Machine Intelligence, IEEE Transactions On 1995;17(2):158-175 https://doi.org/10.1109/34.368173
  11. Fedkiw S, Osher R. "Level set methods and dynamic implicit surfaces", 2003
  12. Cremers D, Mikael R, Rachid D. "A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape." International Journal of Computer Vision 2007;72(2):195-215 https://doi.org/10.1007/s11263-006-8711-1
  13. Huang R, Kwan-Liu M. "Rgvis: Region growing based techniques for volume visualization." Computer Graphics and Applications, 2003. Proceedings. 11th Pacific Conference on. IEEE, 2003
  14. Dougherty Geoff. Digital image processing for medical applications. Cambridge University Press, 2009
  15. Kumano S, Tsuda T, Tanaka H, Hirata M, Kim T, Murakami T, et al. "Preoperative evaluation of perigastric vascular anatomy by 3-dimensional computed tomographic angiography using 16-channel multidetector- row computed tomography for laparoscopic gastrectomy in patients with early gastric cancer." Journal of Computer Assisted Tomography 2007;31(1):93-97 https://doi.org/10.1097/01.rct.0000233123.75560.08
  16. Laparoscopy MB, Matsuki M, Kani H, Tatsugami F, Yoshikawa S, Narabayashi I, et al. "Preoperative assessment of vascular anatomy around the stomach by 3D imaging using MDCT before laparoscopy-assisted gastrectomy." American Journal of Roentgenology 2004;183(1):145-151 https://doi.org/10.2214/ajr.183.1.1830145
  17. Lee SW, Shinohara H, Matsuki M, Okuda J, Nomura E, Mabuchi H, et al. "Preoperative simulation of vascular anatomy by three-dimensional computed tomography imaging in laparoscopic gastric cancer surgery." Journal of the American College of Surgeons 2003;197(6):927-936 https://doi.org/10.1016/j.jamcollsurg.2003.07.021
  18. Miyaki A, Imamura K, Kobayashi R, Takami M, Matsumoto J, Takada Y. "Preoperative assessment of perigastric vascular anatomy by multidetector computed tomography angiogram for laparoscopyassisted gastrectomy." Langenbeck's Archives of Surgery 2012;397(6):945-950 https://doi.org/10.1007/s00423-012-0956-2
  19. Rajon DA, Bolch WE. "Marching cube algorithm: review and trilinear interpolation adaptation for image-based dosimetric models." Computerized Medical Imaging and Graphics 2003;27(5):411-435 https://doi.org/10.1016/S0895-6111(03)00032-6