The nature of statistical learning theory
The nature of statistical learning theory
Feature normalization and likelihood-based similarity measures for image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Subdivision Methods for Geometric Design: A Constructive Approach
Subdivision Methods for Geometric Design: A Constructive Approach
A geometric database for gene expression data
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
An introduction to variable and feature selection
The Journal of Machine Learning Research
Neuroinformatics for Genome-Wide 3-D Gene Expression Mapping in the Mouse Brain
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Associating specific gene activity with functional locations in the brain results in a greater understanding of the role of the gene. To perform such an association for the over 20,000 genes in the mammalian genome, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this work, we propose a new automatic method that results in the segmentation of gene expression images into distinct anatomical regions in which the expression can be quantified and compared with other images. Our method utilizes shape models from training images, texture differentiation at region boundaries, and features of anatomical landmarks, to deform a subdivision mesh-based atlas to fit gene expression images. The subdivision mesh provides a common coordinate system for internal brain data through which gene expression patterns can be compared across images. The automated large-scale annotation will help scientists interpret gene expression patterns at cellular resolution more efficiently.