Model driven segmentation and the detection of bone fractures

Abstract

The introduction of lower dosage image acquisition devices and the increase in computational power means that there is an increased focus on producing diagnostic aids for the medical trauma environment. The focus of this research is to explore whether geometric criteria can be used to detect bone fractures from Computed Tomography data. Conventional image processing of CT data is aimed at the production of simple iso-surfaces for surgical planning or diagnosis - such methods are not suitable for the automated detection of fractures. Our hypothesis is that through a model-based technique a triangulated surface representing the bone can be speedily and accurately produced. And, that there is sufficient structural information present that by examining the geometric structure of this representation we can accurately detect bone fractures. In this dissertation we describe the algorithms and framework that we built to facilitate the detection of bone fractures and evaluate the validity of our approach.