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

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 108-111)

Edited by:

Yanwen Wu

Pages:

69-73

DOI:

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

Citation:

X. W. Li "A New 3D Medical Data Field Segmentation Algorithm Based on Improved K_Means Clustering", Advanced Materials Research, Vols. 108-111, pp. 69-73, 2010

Online since:

May 2010

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Price:

$35.00

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