[1] Yu Lu, Xie Jun, Wu Lenan, et al. Statistical segmentation model on lattices [J]. Journal of Southeast University (English Edition), 2008, 24 (1): 10-14. [doi:10.3969/j.issn.1003-7985.2008.01.003]

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Statistical segmentation model on lattices()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

24

Issue:

2008 1

Page:

10-14

Research Field:

Computer Science and Engineering

Publishing date:

2008-03-30

Info

Title:

Statistical segmentation model on lattices

Author(s):

Yu Lu¹;  2;  Xie Jun³;  Wu Lenan¹

¹School of Information Science and Engineering, Southeast University, Nanjing 210096, China
² Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China
³ Institute of Command Automation, PLA University of Science and Technology, Nanjing 210007, China

Keywords:

image segmentation;  curve evolution;  conditional entropy;  lattice;  labelling problem

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2008.01.003

Abstract:

To reduce the difficulty of implementation and shorten the runtime of the traditional Kim-Fisher model, an entirely discrete Kim-Fisher-like model on lattices is proposed.The discrete model is directly built on the lattices, and the greedy algorithm is used in the implementation to continually decrease the energy function.First, regarding the gray values in images as discrete-valued random variables makes it possible to make a much simpler estimation of conditional entropy.Secondly, a uniform method within the level set framework for two-phase and multiphase segmentations without extension is presented.Finally, a more accurate approximation to the curve length on lattices with multi-labels is proposed.The experimental results show that, compared with the continuous Kim-Fisher model, the proposed model can obtain comparative results, while the implementation is much simpler and the runtime is dramatically reduced.

References:

[1] Kim J, Fisher J W, Yezzi A, et al.A nonparametric statistical method for image segmentation using information theory and curve evolution [J].IEEE Transactions on Image Processing, 2005, 14(10):1486-1502.
[2] Song B, Chan T.A fast algorithm for level set based optimization, CAM 02-68 [R].Los Angeles:University of California at Los Angeles, 2002.
[3] Shi Y, Karl W C.Real-time tracking using level sets [C]//Proc of IEEE International Conference on Computer Vision and Pattern Recognition.San Diego, USA, 2005:34-41.
[4] Gibou F, Fedkiw R.A fast level set based algorithm for segmentation [C]//Proc of the 4th Annual International Conference on Statistics and Mathematics.Hawaii, USA, 2005:281-291.
[5] Yezzi A, Kichenassamy A, Kumar A, et al.A geometric snake model for segmentation of medical imagery [J].IEEE Transactions on Medical Image, 1997, 16(2):199-209.
[6] Yu Lu, Wang Qiao, Wu Lenan, et al.Mumford-shah model with fast algorithm on lattice [C]//Proc of IEEE International Conference on Acoustics, Speech, and Signal Processing.Toulouse, France, 2006:681-684.
[7] Mumford D, Shah J.Optimal approximations by piecewise smooth functions and associated variational problems [J].Communications on Pure and Applied Mathematics, 1989, 42(5):577-685.

Memo

Memo:

Biographies: Yu Lu(1973—), female, doctor;Wu Lenan(corresponding author), male, doctor, professor, wuln@seu.edu.cn.
Citation: Yu Lu, Xie Jun, Wu Lenan.Statistical segmentation model on lattices[J].Journal of Southeast University(English Edition), 2008, 24(1):10-14.

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