Region Graph Spectra as Geometric Global Image Features

Authors:
Qirong Ho;Weimiao Yu;Hwee Kuan Lee
Affiliations:
Bioinformatics Institute, A*STAR, Singapore;Bioinformatics Institute, A*STAR, Singapore;Bioinformatics Institute, A*STAR, Singapore
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Year:
2009

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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.