Knee Articular Cartilage Segmentation with Priors Based On Gaussian Kernel Level Set Algorithm 


Vol. 39,  No. 6, pp. 490-496, Jun.  2014


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  Abstract

The thickness of knee joint cartilage causes most diseases of knee. Therefore, an articular cartilage segmentation of knee magnetic resonance imaging (MRI) is required to diagnose a knee diagnosis correctly. In particular, fully automatic segmentation method of knee joint cartilage enables an effective diagnosis of knee disease. In this paper, we analyze a well-known level-set based segmentation method in brain MRI, and apply that method to knee MRI with solving some problems from different image characteristics. The proposed method, a fully automatic segmentation in whole process, enables to process faster than previous semi-automatic segmentation methods. Also it can make a three-dimension visualization which provides a specialist with an assistance for the diagnosis of knee disease. In addition, the proposed method provides more accurate results than the existing methods of articular cartilage segmentation in knee MRI through experiments.

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  Cite this article

[IEEE Style]

C. Ahn, T. Bui, Y. Lee, J. Shin, "Knee Articular Cartilage Segmentation with Priors Based On Gaussian Kernel Level Set Algorithm," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 6, pp. 490-496, 2014. DOI: .

[ACM Style]

Chunsoo Ahn, Toan Bui, Yong-woo Lee, and Jitae Shin. 2014. Knee Articular Cartilage Segmentation with Priors Based On Gaussian Kernel Level Set Algorithm. The Journal of Korean Institute of Communications and Information Sciences, 39, 6, (2014), 490-496. DOI: .

[KICS Style]

Chunsoo Ahn, Toan Bui, Yong-woo Lee, Jitae Shin, "Knee Articular Cartilage Segmentation with Priors Based On Gaussian Kernel Level Set Algorithm," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 6, pp. 490-496, 6. 2014.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880