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CLC number: TN911.73

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Received: 2005-09-29

Revision Accepted: 2006-02-27

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Cited: 4

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Journal of Zhejiang University SCIENCE B 2006 Vol.7 No.5 P.365~372

10.1631/jzus.2006.B0365


SVM for density estimation and application to medical image segmentation


Author(s):  Zhang Zhao, Zhang Su, Zhang Chen-xi, Chen Ya-zhu

Affiliation(s):  Biomedical Instrument Institute, Shanghai Jiao Tong University, Shanghai 200030, China

Corresponding email(s):   z_ball@sjtu.edu.cn

Key Words:  Support vector machine (SVM), Density estimation, Medical image segmentation, Level set method


Zhang Zhao, Zhang Su, Zhang Chen-xi, Chen Ya-zhu. SVM for density estimation and application to medical image segmentation[J]. Journal of Zhejiang University Science B, 2006, 7(5): 365~372.

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author="Zhang Zhao, Zhang Su, Zhang Chen-xi, Chen Ya-zhu",
journal="Journal of Zhejiang University Science B",
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pages="365~372",
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publisher="Zhejiang University Press & Springer",
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%T SVM for density estimation and application to medical image segmentation
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.B0365

TY - JOUR
T1 - SVM for density estimation and application to medical image segmentation
A1 - Zhang Zhao
A1 - Zhang Su
A1 - Zhang Chen-xi
A1 - Chen Ya-zhu
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SP - 365
EP - 372
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Y1 - 2006
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.B0365


Abstract: 
A method of medical image segmentation based on support vector machine (SVM) for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process. Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Adalsteinsson, D., Sethian, J.A., 1995. A fast level set method for propagating interface. Journal of Computational Physics, 118(2):269-277.

[2] Chan, T.F., Vese, L.A., 2001. Active contours without edges. IEEE Transactions on Image Processing, 2(10):141-151.

[3] Hampton, C., Persons, T., Wyatt, C., Zhang, Y., 1998. Survey of Image Segmentation. Available: http://citeseer.ist.psu.edu/hampton98survey.html.

[4] Leventon, M.E., Faugeras, O., Grimson, W.E., Wells, W.M., 2002. Level Set Based Segmentation with Intensity and Curvature Priors. Biomedical Imaging, IEEE International Summer School, Brittany.

[5] Sethian, J.A., 1999. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press, New York.

[6] Vapnik, V., Mukherjee, S., 2000. Support Vector Method for Multivariate Density Estimation. Advances in Neural Information Processing Systems, MIT Press, Massachusetts, p.659-665.

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