This paper proposes a parallel and direct semantic feature curve extraction method from 3D volume image for vectorization and illustration. Our approach is motivated by reconstructing 3D geometric information from multiple rendered images under multi-view in computer vision. The 2D rendered images are rich in the visual sense by color and opacity that convey the structure of volume data, so it is significant for the user to understand the structure of 3D volume data better if we can recover feature curves from those 2D images. Compared with conventional line extraction methods, which mainly focus on extracting feature curves from iso-surfaces in object space, we extract feature curves directly from volume images. Most of the computation can be computed in parallel on GPU with CUDA acceleration.