Ms. Florence T. Dru Profile

Florence T. Dru

at Robarts Research Institute

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Proceedings Article | 10 March 2006 Paper

An ITK framework for deterministic global optimization for medical image registration

Florence Dru, Mark Wachowiak, Terry Peters

Proceedings Volume 6144, 61442J (2006) https://doi.org/10.1117/12.652446

KEYWORDS: Image registration, Medical imaging, Magnetic resonance imaging, Algorithm development, Optimization (mathematics), Signal to noise ratio, Software development, 3D modeling, Image analysis, Visualization

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Similarity metric optimization is an essential step in intensity-based rigid and nonrigid medical image registration. For clinical applications, such as image guidance of minimally invasive procedures, registration accuracy and efficiency are prime considerations. In addition, clinical utility is enhanced when registration is integrated into image analysis and visualization frameworks, such as the popular Insight Toolkit (ITK). ITK is an open source software environment increasingly used to aid the development, testing, and integration of new imaging algorithms. In this paper, we present a new ITK-based implementation of the DIRECT (Dividing Rectangles) deterministic global optimization algorithm for medical image registration. Previously, it has been shown that DIRECT improves the capture range and accuracy for rigid registration. Our ITK class also contains enhancements over the original DIRECT algorithm by improving stopping criteria, adaptively adjusting a locality parameter, and by incorporating Powell's method for local refinement. 3D-3D registration experiments with ground-truth brain volumes and clinical cardiac volumes show that combining DIRECT with Powell's method improves registration accuracy over Powell's method used alone, is less sensitive to initial misorientation errors, and, with the new stopping criteria, facilitates adequate exploration of the search space without expending expensive iterations on non-improving function evaluations. Finally, in this framework, a new parallel implementation for computing mutual information is presented, resulting in near-linear speedup with two processors.

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