A New 3D Medical Data Field Segmentation Algorithm Based on Improved K_Means Clustering

Xin Wu Li

doi:10.4028/www.scientific.net/AMR.108-111.69

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 108-111A New 3D Medical Data Field Segmentation Algorithm...

A New 3D Medical Data Field Segmentation Algorithm Based on Improved K_Means Clustering

Abstract:

Direct 3D volume segmentation is one of the difficult and hot research fields in 3D medical data field processing. Using K-means clustering techniques, a new clustering segmentation algorithm is presented. Firstly, According to the physical means of the medical data, the data field is preprocessed to speed up succeed processing. Secondly, the paper deduces and analyzes the clustering and segmentation algorithm and presents some methods to increase the process speed, including improving cluster seed selection, improving calculation flow, and amending pixel processing and operational principle of algorithm. Finally, the experimental results show that the algorithm has high accuracy when used to segment 3D medical tissue and can improve process speed greatly.

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Periodical:

Advanced Materials Research (Volumes 108-111)

Pages:

69-73

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.108-111.69

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Online since:

May 2010

Authors:

Xin Wu Li

Keywords:

3D Medical Segmentation, Clustering Segmentation, K-Mean

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References

[1] Y. Cheng and F. Huang , in: A Coupled Minimization Problem for Medical Image Segmentation with Priors, International Journal of Computer Vision, Vol. 33(2007), p.259.

Google Scholar

[2] Haralick R.M., Shapiro L. G , in: Survey: Image segmentation techniques, Computer Graphics and Image Processing, Vol. 29 (1985), p.100.

DOI: 10.1016/s0734-189x(85)90153-7

Google Scholar

[3] Bradley P. S., Managasarian L, in: Survey: K-plane clustering, Journal of Global Optimization, Vol. 16 (2000), p.32.

Google Scholar

[4] Tang Y., Rong Q. S, in: Survey: An implementation of clustering algorithm based on K-means, Journal of Hubei Institute for Nationalities, Vol. 22 (2004), p.69.

Google Scholar

[5] Jiao C.L., Gao M.T., Shi Y. K, in: Survey: Image clustering and segmentation based on Improved clustering neural network, Computer Engineering and Application, Vol. 43(2007), p.93.

Google Scholar

[6] Yi S, in: Survey: Global optimization for �� training, IEEE Computers, Vol. 19(2006), p.45.

Google Scholar

[7] Zhang Y.F., Mao J. L., in: Survey: An improved K-means algorithm, Computer Application, Vol. 23(2003), p.21.

Google Scholar