Landmark/image-based deformable registration of gene expression data

Abstract : Analysis of gene expression patterns in brain images obtained from high-throughput in situ hybridization requires accurate and consistent annotations of anatomical regions/subregions. Such annotations are obtained by mapping an anatomical atlas onto the gene expression images through intensity- and/or landmark-based registration methods or deformable model-based segmentation methods. Due to the complex appearance of the gene expression images, these approaches require a pre-processing step to determine landmark correspondences in order to incorporate landmark-based geometric constraints. In this paper, we propose a novel method for landmark-constrained, intensity-based registration without determining landmark correspondences a priori. The proposed method performs dense image registration and identifies the landmark correspondences, simultaneously, using a single higher-order Markov Random Field model. In addition, a machine learning technique is used to improve the discriminating properties of local descriptors for landmark matching by projecting them in a Hamming space of lower dimension. We qualitatively show that our method achieves promising results and also compares well, quantitatively, with the expert's annotations, outperforming previous methods.
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Submitted on : Friday, August 30, 2013 - 3:06:50 PM
Last modification on : Tuesday, February 5, 2019 - 1:52:14 PM

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Uday Kurkure, Yen Le, Nikos Paragios, James Carson, Tao Ju, et al.. Landmark/image-based deformable registration of gene expression data. 24th IEEE Conference on Computer Vision and Pattern Recognition - CVPR 2011, Jun 2011, Rhode Island, United States. pp.1089-1096, ⟨10.1109/CVPR.2011.5995708⟩. ⟨hal-00856140⟩

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