Adaptive axes-generation algorithm for 3D tubular structures

Three-Dimensional (3D) radiologic images are widely used to assess the condition of thin tubular structures, such as the pulmonary airways, coronary arteries, and colon. Precise 3D central axes of these structures are needed, however, for accurate quantization. Commonly employed manual-axes identification techniques are time-consuming and error-prone. Recently proposed automated techniques do not adequately exploit the available gray-scale or anatomic structural information and they are also prone to errors. The authors propose a method for computing the precise central axes of branching structures contained in 3D images. The method is robust to data anisotropy and uses true gray-scale information. These axes can then be used for automated navigation and assessment in a virtual-endoscopic system. The authors present the application of their method to a human lung-cancer case