体数据分割是体素建模的前提。
在体数据分割中我们分别讨论了基于阈值和基于区域的分割方法。
In volume data segmentation, the threshold based and region based methods are separately discussed.
在本文中我们着重讨论了体视化技术中的体数据分割和重建问题。
In this paper, we emphasize on the problems of segmentation and reconstruction of volume data.
而图像的配准、图像分割、体数据集的构建、三维空间插值则是医学图像三维可视化实现过程中的关键技术环节。
The image registering, image segmentation, pixel data set construction and 3d special interpolation are the key technologies in medical images 3d reconstruction.
为提高三维医学数据场的分割效率和准确率，本文利用特征聚类技术，提出了一种新的基于改进K - means聚类的三维医学数据场的体分割算法。
A clustering segmentation algorithm based on an improved K-means clustering method is used to improve the efficiency and accuracy of 3d medical image segmentation.
为提高三维医学数据场的分割效率和准确率，本文利用特征聚类技术，提出了一种新的基于改进K - means聚类的三维医学数据场的体分割算法。
A clustering segmentation algorithm based on an improved K-means clustering method is used to improve the efficiency and accuracy of 3d medical image segmentation.
应用推荐