3D Active Net for Volume Extraction

3D Active Net, which is a 3D extension of Snakes, is an energy-minimizing surface model which can extract a volume 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 tted to the contour of a target object by the de nition of the image energy suitable for the contour property. It is an alternative way to the extraction of a desired 3D object by manual segmentation or by using a slice by slice approach. We present testing results of the extraction of a muscle from the Visible Human Data by two methods: manual segmentation and the application of 3D Active Net. We apply principal component analysis, which utilizes the color information of the 3D volume data to emphasize an ill-de ned contour of the muscle, and then apply 3D Active Net. We recognize that the extracted object has a smooth and natural contour in contrast with a comparable manual segmentation, proving an advantage of our approach.