Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Digital Image Processing
Subdivision Methods for Geometric Design: A Constructive Approach
Subdivision Methods for Geometric Design: A Constructive Approach
IEEE Computer Graphics and Applications
A geometric database for gene expression data
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Hybrid segmentation framework for tissue images containing gene expression data
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
The allen brain atlas: delivering neuroscience to the web on a genome wide scale
DILS'07 Proceedings of the 4th international conference on Data integration in the life sciences
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Similarity-Based appearance-prior for fitting a subdivision mesh in gene expression images
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
An approach to segmentation of mouse brain images via intermodal registration
Pattern Recognition and Image Analysis
A Probabilistic Latent Semantic Analysis Model for Coclustering the Mouse Brain Atlas
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Large scale gene expression studies in the mammalian brain offer the promise of understanding the topology, networks and ultimately the function of its complex anatomy, opening previously unexplored avenues in neuroscience. High-throughput methods permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions that control behavior. Previous gene expression mapping studies in model organisms have employed situ hybridization (ISH), a technique that uses labeled nucleic acid probes to bind to specific mRNA transcripts in tissue sections. A key requirement for this effort is the development of fast and robust algorithms for anatomically mapping and quantifying gene expression for ISH. We describe a neuroinformatics pipeline for automatically mapping expression profiles of ISH data and its use to produce the first genomic scale 3-D mapping of gene expression in a mammalian brain. The pipeline is fully automated and adaptable to other organisms and tissues. Our automated study of over 20,000 genes indicates that at least 78.8% are expressed at some level in the adult C56BL/6J mouse brain. In addition to providing a platform for genomic scale search, high-resolution images and visualization tools for expression analysis are available at the Allen Brain Atlas web site (http://www.brain-map.org).