Segmentation of Medical Images Using Three-Dimensional Active Shape Models

In this paper a fully automated segmentation system for the femur in the knee in Magnetic Resonance Images and the brain in Single Photon Emission Computed Tomography images is presented. To do this several data sets were segmented manually. The resulting structures were first represented by unorganised point clouds. With level set methods surfaces were fitted to these point clouds. The iterated closest point algorithm was then applied to establish correspondences between different surfaces. Both surfaces and correspondences were used to build a three dimensional statistical shape model of the major bones in the knee. The resulting model is then used to automatically segment structures in subsequent data sets through three dimensional Active Shape Models. The result of the segmentation is promising, but the quality of the segmentation is dependent on the initial guess.

• Type: Conference
• Year: 2005
• Where: SCIA
• Authors: Klas Josephson, Anders Ericsson, Johan Karlsson
• Keywords: Data Sets, Point Clouds, SCIA 2005, Single Photon Emission Computed Tomography