As medical imaging moves from qualitative assessment to quantitative analysis based on bio-markers, calibration of imaging data becomes critically important. In computed tomography (CT) images values are scaled to Hounsfield units (HU), despite this, the measurements can vary due to differences in machine-level calibration and patient size. One way to ensure proper calibration at the image level is to include a phantom containing objects of known HU values in each scan so that each image can be calibrated individually by the values measured from the phantom regions within the image. This introduces a need to extract phantom measurements from each image. Given a reasonable starting point manually or heuristically, this is a straightforward segmentation problem because the phantom regions are well defined and the values are relatively uniform. However, the problem becomes challenging if the requirement is a fully automated method that is robust across variations of phantoms and that can exclude images without phantoms. In this paper, we describe a probabilistic model-based approach to tackling this problem. We use the constellation model framework first proposed by Burt el al. to represent a phantom as composed of a number of parts and determine the existence and localization of the phantom in a probabilistic sense based on the detection of candidate parts. This model based approach allows us to formally describe variations in phantom design and handle missing parts caused by phantom regions similar to the background. Initial results on 100 CT studies from a longitudinal cardiovascular study are encouraging.

Date Published: 13 March 2013
PDF: 7 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866916 (13 March 2013);

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)