Automated Morphometric Analysis of the Femur on Large Anatomical Databases with Highly Accurate Correspondence Detection
Manuel Schröder*, 1, Heiko Gottschling1, Nils Reimers*, 2, Matthias Hauschild1, Rainer Burgkart1
Identifiers and Pagination:Year: 2014
First Page: 15
Last Page: 22
Publisher ID: MEDJ-1-15
Article History:Received Date: 5/9/2013
Revision Received Date: 12/5/2014
Acceptance Date: 19/5/2014
Electronic publication date: 27/6/2014
Collection year: 2014
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
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 2mm.