Abstract
In quantitative biology studies such as drug and siRNA screens, robotic systems automatically acquire thousands of images from cell assays. Because these images are large in quantity and high in content, detecting specific patterns (phenotypes) in them requires accurate and fast computational methods. To this end, we have developed a geometric global image feature for pattern retrieval on large bio-image data sets. This feature is derived by applying spectral graph theory to local feature detectors such as the Scale Invariant Feature Transform, and is effective on patterns with as few as 20 keypoints. We demonstrate successful pattern detection on synthetic shape data and fluorescence microscopy images of GFP-Keratin-14-expressing human skin cells.
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Keywords
- Receiver Operating Characteristic Curve
- Scale Invariant Feature Transform
- Connectivity Function
- Region Spectrum
- Training Exemplar
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Ho, Q., Yu, W., Lee, H.K. (2009). Region Graph Spectra as Geometric Global Image Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_24
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DOI: https://doi.org/10.1007/978-3-642-10331-5_24
Publisher Name: Springer, Berlin, Heidelberg
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