title: 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.
authors.affiliation: University of Houston, Houston, TX, USA  http://cbl.uh.edu.
authors.forename: Uday
authors.initials: U
authors.lastname: Kurkure
authors.forename: Yen H
authors.initials: YH
authors.lastname: Le
authors.forename: Nikos
authors.initials: N
authors.lastname: Paragios
authors.forename: James P
authors.initials: JP
authors.lastname: Carson
authors.forename: Tao
authors.initials: T
authors.lastname: Ju
authors.forename: Ioannis A
authors.initials: IA
authors.lastname: Kakadiaris