Abstract: A recognition pipeline automatically partitions a 3D image of the human body into regions of interest (head, rib cage, pelvis, and legs) and correctly labels each region. The 3D image is projected onto a 2D image using volume rendering. The 3D image may contain the whole body region or any subset. In a learning phase, training datasets are manually partitioned and labeled, and a training database is computed. In a recognition phase, images are partitioned and labeled based on the knowledge from the training database. The recognition phase is computationally efficient and may operate under 2 seconds in current conventional computer systems. The recognition is robust to image variations, and does not require the user to provide any knowledge about the contained regions of interest within the image.

Abstract: A recognition pipeline automatically partitions a 3D image of the human body into regions of interest (head, rib cage, pelvis, and legs) and correctly labels each region. The 3D image is projected onto a 2D image using volume rendering. The 3D image may contain the whole body region or any subset. In a learning phase, training datasets are manually partitioned and labeled, and a training database is computed. In a recognition phase, images are partitioned and labeled based on the knowledge from the training database. The recognition phase is computationally efficient and may operate under 2 seconds in current conventional computer systems. The recognition is robust to image variations, and does not require the user to provide any knowledge about the contained regions of interest within the image.