A New Clustering Segmentation Algorithm of 3D Medical Data Field Based on Data Mining

Direct 3D volume segmentation is one of the difficult and hot research fields in 3D medical data field processing. Using the clustering and analyzing techniques of data mining, a new clustering and segmentation algorithm for 3D medical image based on density-isoline is presented. Firstly, According to the physical means of the medical data, the voxel’s gray level value in data field is redefined to speed up succeed processing. Secondly, the paper analyzes and improves the clustering and segmentation algorithm through preclustering image, defineing seeds and selecting process cells to improve algorithm efficiency. The experimental results show that the time consuming of the algorithm is only one tenth of the traditional methods and the algorithm has high accuracy when used to segment complicated 3D medical tissue.