抄録
3D Active Net is an energy-minimizing surface model which can extract a volume around features of interest from 3D volume data. It is deformable and evolves in 3D space to be attracted to salient features, according to its internal and image energy. The net can be fitted to the contours of a target by defining the image energy suitable for the contour property. We present test results of the extraction of a muscle from Visible Human Data by two methods : manual segmentation and the application of 3D Active Net. We use principal component analysis, which utilizes color information of the 3D volume data to define an ill-defined contour of the muscle, and then we use 3D Active Net. We recognize that the extracted object has a smooth and natural contour which is in contrast with that of a comparable manual segmentation, proving the advantage of our approach.
