Recent advancements in high-throughput imaging have created new large datasets with tens of thousands of gene expression images. Methods for capturing these spatial and/or temporal expression patterns include in situ hybridization or fluorescent reporter constructs or tags, and results are still frequently assessed by subjective qualitative comparisons. In order to deal with available large datasets, fully automated analysis methods must be developed to properly normalize and model spatial expression patterns. We have developed image segmentation and registration methods to identify and extract spatial gene expression patterns from RNA in situ hybridization experiments of Drosophila embryos. These methods allow us to normalize and extract expression information for 78,621 images from 3724 genes across six time stages. The similarity between gene expression patterns is computed using four scoring metrics: mean squared error, Haar wavelet distance, mutual information and spatial mutual information (SMI). We additionally propose a strategy to calculate the significance of the similarity between two expression images, by generating surrogate datasets with similar spatial expression patterns using a Monte Carlo swap sampler. On data from an early development time stage, we show that SMI provides the most biologically relevant metric of comparison, and that our significance testing generalizes metrics to achieve similar performance. We exemplify the application of spatial metrics on the well-known Drosophila segmentation network. A Java webstart application to register and compare patterns, as well as all source code, are available from: http://tools.genome.duke.edu/generegulation/image_analysis/insitu firstname.lastname@example.org Supplementary data are available at Bioinformatics online.