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Analysis of Neural Data

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Point-Based Warping with Optimized Weighting Factors of Displacement Vectors.
Citation key Pielot2000b
Author Pielot, R. and Scholz, M. and Obermayer, K. and Gundelfinger, E. and Hess, A.
Title of Book Medical Imaging 2000
Pages 1387 – 1395
Year 2000
Volume 3979
Editor Hanson K. M.
Abstract The accurate comparison of inter-individual 3D image brain datasets requires non-affine transformation techniques (warping) to reduce geometric variations. Constrained by the biological prerequisites we use in this study a landmark-based warping method with weighted sums of displacement vectors, which is enhanced by an optimization process. Furthermore, we investigate fast automatic procedures for determining landmarks to improve the practicability of 3D warping. This combined approach was tested on 3D autoradiographs of Gerbil brains. The autoradiographs were obtained after injecting a non- metabolizable radioactive glucose derivative into the Gerbil thereby visualizing neuronal activity in the brain. Afterwards the brain was processed with standard autoradiographical methods. The landmark- generator computes corresponding reference points simultaneously within a given number of datasets by Monte-Carlo-techniques. The warping function is a distance weighted exponential function with a landmark-specific weighting factor. These weighting factors are optimized by a computational evolution strategy. The warping quality is quantified by several coefficients (correlation coefficient, overlap-index, and registration error). The described approach combines a highly suitable procedure to automatically detect landmarks in autoradiographical brain images and an enhanced point-based warping technique, optimizing the local weighting factors. This optimization process significantly improves the similarity between the warped and the target dataset. Keywords: Warping, Brain Imaging, Registration, Landmark Detection
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