Share Email Print

Proceedings Paper

Pixel level image fusion for medical imaging: an energy minimizing approach
Author(s): Brandon Miles; Max W. K. Law; Ismail Ben-Ayed; Greg Garvin; Aaron Fenster; Shuo Li
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In an attempt to improve the visualisation techniques for diagnosis and treatment of musculoskeletal injuries, we present a novel image fusion method for a pixel-wise fusion of CT and MR images. We focus on the spine and it's related diseases including osteophyte growth, degenerate disc disease and spinal stenosis. This will have benefit to the 50-75% of people who suffer from back pain, which is the reason for 1.8% of all hospital stays in the United States.1 Pre-registered CT and MR image pairs were used. Rigid registration was performed based on soft tissue correspondence. A pixel-wise image fusion algorithm has been designed to combine CT and MR images into a single image. This is accomplished by minimizing an energy functional using a Graph Cut approach. The functional is formulated to balance the similarity between the resultant image and the CT image as well as between the resultant image and the MR image. Furthermore the variational smoothness of the resultant image is considered in the energy functional (to enforce natural transitions between pixels). The results have been validated based on the amount of significant detail preserved in the final fused image. Based on bone cortex and disc / spinal cord areas, 95% of the relevant MR detail and 85% of the relevant CT detail was preserved. This work has the potential to aid in patient diagnosis, surgery planning and execution along with post operative follow up.

Paper Details

Date Published: 23 February 2012
PDF: 7 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831511 (23 February 2012); doi: 10.1117/12.911613
Show Author Affiliations
Brandon Miles, The Univ. of Western Ontario (Canada)
Max W. K. Law, The Univ. of Western Ontario (Canada)
GE Healthcare (Canada)
Ismail Ben-Ayed, GE Healthcare (Canada)
Greg Garvin, St. Joseph's Hospital (Canada)
Aaron Fenster, The Univ. of Western Ontario (Canada)
Robarts Research Institute (Canada)
Shuo Li, The Univ. of Western Ontario (Canada)
GE Healthcare (Canada)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

© SPIE. Terms of Use
Back to Top