Efficient curve-skeleton computation for the analysis of biomedical 3d images - biomed 2010

Advances in three dimensional (3D) biomedical imaging techniques, such as magnetic resonance (MR) and computed tomography (CT), make it easy to reconstruct high quality 3D models of portions of human body and other biological specimens. A major challenge lies in the quantitative analysis of the resulting models thus allowing a more comprehensive characterization of the object under investigation. An interesting approach is based on curve-skeleton (or medial axis) extraction, which gives basic information concerning the topology and the geometry. Curve-skeletons have been applied in the analysis of vascular networks and the diagnosis of tracheal stenoses as well as a 3D flight path in virtual endoscopy. However curve-skeleton computation is a crucial task. An effective skeletonization algorithm was introduced by N. Cornea in [1] but it lacks in computational performances. Thanks to the advances in imaging techniques the resolution of 3D images is increasing more and more, therefore there is the need for efficient algorithms in order to analyze significant Volumes of Interest (VOIs). In the present paper an improved skeletonization algorithm based on the idea proposed in [1] is presented. A computational comparison between the original and the proposed method is also reported. The obtained results show that the proposed method allows a significant computational improvement making more appealing the adoption of the skeleton representation in biomedical image analysis applications.