Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Fundamentals of digital image processing
Fundamentals of digital image processing
Elements of information theory
Elements of information theory
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document Image Recognition Based on Template Matching of Component Block Projections
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic mining of fruit fly embryo images
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
Contour Extraction of Drosophila Embryos
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Principles of bioimage informatics: focus on machine learning of cell patterns
ISMB/ECCB'09 Proceedings of the 2009 workshop of the BioLink Special Interest Group, international conference on Linking Literature, Information, and Knowledge for Biology
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In situ staining of a target mRNA at several time points during the development of a D. melanogaster embryo gives one a detailed spatio-temporal view of the expression pattern of a given gene. We have developed algorithms and software for analyzing a database of such images with the goal of being able to identify coordinately expressed genes and further our understanding of cis-regulatory control during embryogenesis. Our approach combines measures of similarity at both the global and local levels, based on Gaussian Mixture Model (GMM) decompositions. At the global level, the observed distribution of pixel values is quantized using an adaptive GMM decomposition and then quantized images are compared using mutual information. At the local level, we decompose quantized images into 2-dimensional Gaussian kernels or "blobs" and then develop a blob-set matching method to search for the best matching traits in different pattern-images. A hybrid scoring method is proposed to combine both global and local matching results. We further develop a voting scheme to search for genes with similar spatial staining patterns over the time course of embryo development. To evaluate the effectiveness of our approach, we compare it with several global image matching schemes and a controlled vocabulary method. We then apply our method to 4400 images of 136 genes to detect potentially co-regulated genes that have similar spatio-temporal patterns, using expert-annotation to evaluate our results.