Pixel level image fusion for medical imaging: an energy minimizing approach

Brandon Miles; Max W. K. Law; Ismail Ben-Ayed; Greg Garvin; Aaron Fenster; Shuo Li

doi:10.1117/12.911613

23 February 2012 Pixel level image fusion for medical imaging: an energy minimizing approach

Brandon Miles, Max W. K. Law, Ismail Ben-Ayed, Greg Garvin, Aaron Fenster, Shuo Li

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 831511 (2012) https://doi.org/10.1117/12.911613
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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.

Citation Download Citation

Brandon Miles, Max W. K. Law, Ismail Ben-Ayed, Greg Garvin, Aaron Fenster, and Shuo Li "Pixel level image fusion for medical imaging: an energy minimizing approach", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831511 (23 February 2012); https://doi.org/10.1117/12.911613

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