Spine Image Fusion Via Graph Cuts

• Published in: IEEE transactions on biomedical engineering Vol. 60; no. 7; pp. 1841 - 1850
• Main Authors: Miles, Brandon, Ayed, Ismail Ben, Law, Max W. K., Garvin, Greg, Fenster, Aaron, Li, Shuo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological tissues; Bones; Computed tomography; Graph cuts; Histograms; Humans; Image Enhancement - methods; Image fusion; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; medical imaging; Multimodal Imaging - methods; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Spinal Diseases - diagnosis; Spine; Spine - diagnostic imaging; Spine - pathology; Studies; Subtraction Technique; Tomography, X-Ray Computed - methods; Wavelet Analysis; Wavelet transforms
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2013.2243448

This study investigates a novel CT/MR spine image fusion algorithm based on graph cuts. This algorithm allows physicians to visually assess corresponding soft tissue and bony detail on a single image eliminating mental alignment and correlation needed when both CT and MR images are required for diagnosis. We state the problem as a discrete multilabel optimization of an energy functional that balances the contributions of three competing terms: (1) a squared error, which encourages the solution to be similar to the MR input, with a preference to strong MR edges; (2) a squared error, which encourages the solution to be similar to the CT input, with a preference to strong CT edges; and (3) a prior, which favors smooth solutions by encouraging neighboring pixels to have similar fused-image values. We further introduce a transparency-labeling formulation, which significantly reduces the computational load. The proposed graph-cut fusion guarantees nearly global solutions, while avoiding the pix elation artifacts that affect standard wavelet-based methods. We report several quantitative evaluations/comparisons over 40 pairs of CT/MR images acquired from 20 patients, which demonstrate a very competitive performance in comparisons to the existing methods. We further discuss various case studies, and give a representative sample of the results.