ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2011
Landmark/Image-based Deformable Registration of Gene Expression Data.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- A chicken embryo cardiac outflow tract atlas for registering changes due to abnormal blood flow.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2016Article
- A Learning Based Fiducial-driven Registration Scheme for Evaluating Laser Ablation Changes in Neurological Disorders.Neurocomputing · 2014Article
- Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights.IEEE transactions on medical imaging · 2014Article
- Deformable medical image registration: a survey.IEEE transactions on medical imaging · 2013Review
- Renewing the respect for similarity.Frontiers in computational neuroscience · 2012Article
- Similarity-based appearance-prior for fitting a subdivision mesh in gene expression images.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2012Article
- Markov Random Field-based Fitting of a Subdivision-based Geometric Atlas.Proceedings. IEEE International Conference on Computer Vision · 2011Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.