Direct Segmentation Algorithm Research for 3D Medical Data Field

摘要

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.