Optical-Tracker-Based 3D Reconstruction for Endoscopic Environment

Abstract:

Endoscopic surgery is increasing for minimally invasive treatment in recent years.But the conventional two-dimensional endoscope technique cannot provide three-dimensional information for surgeon. Lacking depth and other 3D information often made doctorshave to carry out the surgery depending heavily clinical experience.In response to the above described issues,this paper proposed a method based on improved SIFT algorithmto recover 3D structure information of the endoscopic environment, with the help of an optical tracking system which can provide the orientation of the camera in real time. The proposed approach is evaluated on sequence digital images gotten from an 1394 camera and the experimental results show that the proposed approach iseffective.

• Periodical: Applied Mechanics and Materials (Volumes 249-250)
• Pages: 1277-1282
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.249-250.1277
• Online since: December 2012
• Authors: Bo Yang, Ya Zhou, Xiao Ming Hu, Jun Qin Lin, Qiao Na Xing
• Keywords: 3D Reconstruction, Delaunay, endoscope, Optical Tracker, SIFT

References

[1] Burschka, D., Li, M., Ishii, M., Taylor, R.H., Hager, G.D. Scale-invariant registration of monocular endoscopic images to CT-scans for sinus surgery [J]. Medical Image Analysis. 2005, 9(5 SPEC.ISS.): 413.

DOI: 10.1016/j.media.2005.05.005

[2] Dipanda, A,Woo, S.Towards a real-time 3D shape reconstruction using a structured light system[J].Pattern Recognition,2005,38(10):1632-1650.

DOI: 10.1016/j.patcog.2005.01.006

[3] Deguchi, K., Sasano. T., Arai, H., Yoshikawa, H. 3-D shape reconstruction from endoscope image sequences by the factorization method [J]. Ieice Transactions On Information And Systems. 1996, E79D(9): 1329-1336.

[4] Lowe D, Distinctive image features from scale-invariant key points, International Journal of Computer Vision 2004; 60(2);91-110.

DOI: 10.1023/b:visi.0000029664.99615.94

[5] Lowe, D.G., Object recognition from local scale-invariant features, Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, 1999, p.1150.

DOI: 10.1109/iccv.1999.790410

[6] Pengfei Du, Ya Zhou, Xiaoming Hu. Improved SIFT Matching Algorithm For 3D Reconstruction from Endoscopic Images. ACM-SIGGRPH-VRCAI2011,2011,12(1):4-16.

[7] David C S, Da F. A greedy Delaunay-based surface reconstruction algorithm [J]. The Visual Computer, 2004, 20(1): 4-16.

[8] Chuan-Chu Kuo,Hong-TzongYau.A Delaunay-based region-growing approach to surface reconstruction from unorganized points[J].Computer-Aided Design,2005,37(8):825-835.

DOI: 10.1016/j.cad.2004.09.011

[9] FeiYou,QingxiHu,QiLu.Study on the Algorithm of Surface Reconstruction of Defective Bone Based on Delaunay Triangulation[C].//7th Asian-Pacific Conference on Medical and Biological Engineering.2008.

DOI: 10.1007/978-3-540-79039-6_64

[10] Zhang, Z. A flexible new technique for camera calibration [J].Ieee Transactions On Pattern Analysis And Machine Intelligence. 2000.22(11):1330.

DOI: 10.1109/34.888718