Image Registration and Realignment using Evolutionary Algorithms with High Resolution 3D Models from Human Liver

K. Yan, L. Bertens, and F. Verbeek (The Netherlands)


3D realignment, CMAES, vasculature reconstructions,multiobject registration, Pearson correlation coefficient


This paper introduces a robust solution to the realignment of high-resolution image stacks as required in the study of micro-vasculature in human liver. We discuss the design and implementation of CMA-ES alignment algorithm that provides a better and more generic solution to the realignment problem compared to existing solutions. The result of a random rigid transformation test shows that CMA-ES clearly outperforms several known solutions on both accuracy and robustness. The CMA-ES yields an accuracy of approximately 80% whilst the hierarchical chamfer matching algorithm or the Marquardt-Levenberg matching algorithm have an accuracy yield that is less than 50 %.

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