Study on Coordinate Information Generation Method of Interested Area in IMRT Inverse Planning System

Zhen Chen; Guo Li Li

doi:10.4028/www.scientific.net/AMR.267.289

Paper Titles

The Study on the Quality Management of Supply Chain Production in Operations
p.265

Main Converter Fault Diagnosis for Power Locomotive Based on PSO-BP Neural Networks
p.271

Research on Software Trustworthiness Level Evaluating Model Based on Layered Idea and its Application
p.277

Research on Face Recognition Based on Pulse Coupled Neural Network
p.283

Study on Coordinate Information Generation Method of Interested Area in IMRT Inverse Planning System
p.289

Topology Optimization in the Conceptual Design: Take the Frame of a Bender as Example
p.297

An Efficient DoS Attacks Detection Method Based on Data Mining Scheme
p.302

An Effective Intrusion Detection Model Based on Random Forest and Neural Networks
p.308

The Application of Cloud Storage in the Library
p.314

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 267Study on Coordinate Information Generation Method...

Study on Coordinate Information Generation Method of Interested Area in IMRT Inverse Planning System

Abstract:

In inverse planning system of Intensity Modulated Radiation Therapy (IMRT), the coordinate of interested area (target, organ at risk) is the necessary information for optimization algorithm. This paper analyzes several classical region filling algorithms firstly. Based on the VTK toolkits, Bezier curve algorithm, and the improved Watershed algorithm, a method using interactive delineation tool to save the coordinate information of interested area is realized. The information in the delineated area is accurately extracted by the method.

You might also be interested in these eBooks

Manufacturing Systems and Industry Application

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 267)

Pages:

289-296

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.267.289

Citation:

Cite this paper

Online since:

June 2011

Authors:

Zhen Chen, Guo Li Li

Keywords:

Bezier Curse Algorithm, IMRT, Interested Area, Inverse Planning, Visualization Toolkit (VTK), Watershed Algorithm

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Zhuo Chen. Relevant Research on Visualization Based on VTK and Its Application in TPS, Hefei University of Technology (2004).

Google Scholar

[2] La-sheng Yu, De-yao Shen. A Refinement of the Scan Line Seed Fill Algorithm, Computer Engineering, vol. 29, 2003, pp.70-74.

Google Scholar

[3] Xi-yao Chen, Wei Chen, Li-fang Tong. The Existing Problem and Solution for Arithmetic of Scan-Line Filling, Journal Of Northeast Dianli University Natural Science Edition, vol. 26, 2006, pp.52-56.

Google Scholar

[4] Xiao-song Hao. The common problems and solutions of boundary-labeling method in the course of realization", Journal of Xi, an University of Engineering Science and Technology, vol. 20, 2006, pp.215-220.

Google Scholar

[5] B.D. Ackland, N.H. Weste. The edge flag algorithm–A fill method for raster scan display, IEEE Transactions on Computers, vol. 30, 1981, pp.41-48.

DOI: 10.1109/tc.1981.6312155

Google Scholar

[6] M.R. Dunlavey. Efficient polygon-filling algorithms for raster displays, ACM Transactions on Graphics, (1983).

DOI: 10.1145/245.248

Google Scholar

[7] Shi-liang Xu. The computer algorithm, Tsinghua University Press, Beijing, (1992).

Google Scholar

[8] Hua Ma, Feng Liu, Chun-li Ren. Computer aided drawing of the Bezier curve, Journal of Xidian University, vol. 29, 2002, pp.566-571.

Google Scholar

[9] Nian Cai, Xiao-yan Tang, Shao-rui Xu, Fang-zhen Li. Segmentation of MELK images based on watershed algorithm, Application Research of Computer, vol. 26, 2009, pp.3175-3191.

Google Scholar

[10] Hong-xia You, Wen-bo Xu. Iris Segmentation Based on Watersheds Algorithm, Micro-Computer Information, vol. 21, 2005, pp.175-181.

Google Scholar

[11] E.N. Mortensen, A. Barrentw. Toboggan-based intelligent scissors with a four-parameter edge model, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington DC: IEEE Computer Society, 1999, pp.452-458.

DOI: 10.1109/cvpr.1999.784720

Google Scholar

[12] Vapn IK v. The nature of statistical learning theory, NY Springer, (1995).

Google Scholar

[13] D. Wang. Unsupervised video segmentation based on watersheds and temporal tracking, IEEE Trans. on Circuits Syst Video Technology, vol. 8, 1998, pp.539-546.

DOI: 10.1109/76.718501

Google Scholar

[14] Xin-ping Guan, Na Huang, Ying-gan Tang. New watershed segmentation algorithm via marker threshold, Systems Engineering and Electronics, vol. 31, 2009, pp.972-975.

Google Scholar