Automated Morphometric Analysis of the Femur on Large Anatomical Databases with Highly Accurate Correspondence Detection

For a variety of medical applications, detailed knowledge on the statistical distribution of morphometric characteristics among specific patient groups is required. We present a novel approach for performing automated morphometric measurements on the surface of anatomical bone samples obtained from CT segmentation. The system developed supports various types of measurements (distances, angles, radii) on several kinds of features (points, lines, planes or circles), which are performed automatically for every bone sample in a given data set. The desired features can be specified by the user in two ways, either by marking them on a standardized template that is mapped to all samples via a correspondence mapping, or by hierarchically building new features from existing features. The system was implemented and tested on a database containing about 1200 segmented femur. The quality of the automated matching was assessed through a study comparing the performance of the system with results obtained from manual labeling by medical experts. It was found that the deviation between the two methods was generally less than