III: Small: An Automatic Framework for Processing Drosophila Embryonic Images

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High resolution embryonic images, e.g., the data set BDGP (Berkley Drosophila Genome Project), have been introduced as an important tool for the discovery of gene-gene interaction. These images contain not only temporal information of a gene but also precise spatial information of expression regions of genes. So the biologic problem of the discovery of gene-gene interaction can be characterized as a computational problem of matching expression patterns of embryos at the same developmental stage. It is, however, very challenging to design a fully automatic computational system due to severe imaging and artificial variations in embryonic images. Current research on embryonic image processing involves significant manual manipulation or addresses only a small subset of variations. In this proposal, I propose a comprehensive automatic framework to achieve the three fundamental tasks: image standardization, stage determination, and expression pattern modeling. The proposed project will essentially advance the integration of biologic, image processing and pattern recognition, and machine learning. The PI will develop a series of analysis modules to standardize the variation across images, provide for inpainting, and provide estimates of the boundaries of embryos. The expression pattern modeling will develop discriminate features to address issues of distinguishing specific pixels in varied refraction circumstances. A key concept is to develop an imbalance point detection scheme that will minimize the occurrences of edge points and provide a measure of the imbalance degree.These methods should be adaptable for analysis of images from other model species, e.g., mouse. The proposed work will directly facilitate basic and applied research on image processing crucial to biological image analysis. The challenges in analyzing developmental biological images as compared to natural images have created increasing demands on and opportunities for developing novel image processing techniques. The algorithms and tools developed in this project will have made available to the community. This project will also facilitate the development of new courses and laboratory infrastructure for knowledge discovery from biological data.