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, based on analyzing the limitation of the original K-means algorithm, the paper improves the principle of the K-means, the selection of initial cluster centers and algorithm flow of K-means cluster algorithm to improve efficiency and stability of original K_means algorithm; Thirdly, based on physical characteristics of medical 3D volume segmentation, a new pixel processing method and operational principle are designed in the improved K-means segmentation algorithm to improve segmentation accuracy and speed; Finally, the experimental results show that the algorithm has high segmentation accuracy and can improve process stability and segmentation speed greatly when used to segment 3D medical data field directly.
3D medical data field processing, Direct Volume segmentation, K-means clustering, initial cluster centers
Share This Article
© The Author(s) 2013. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, sharing, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.