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21 March 2016 High-throughput mouse phenotyping using non-rigid registration and robust principal component analysis
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Intensive international efforts are underway towards phenotyping the mouse genome, by knocking out each of its ≈25,000 genes one-by-one for comparative study. With vast amounts of data to analyze, the traditional method using time-consuming histological examination is clearly impractical, leading to an overwhelming demand for some high-throughput phenotyping framework, especially with the employment of biomedical image informatics to efficiently identify phenotypes concerning morphological abnormality. Existing work has either excessively relied on volumetric analytics which is insensitive to phenotypes associated with no severe volume variations, or tailored for specific defects and thus fails to serve a general phenotyping purpose. Furthermore, the prevailing requirement of an atlas for image segmentation in contrast to its limited availability further complicates the issue in practice. In this paper we propose a high-throughput general-purpose phenotyping framework that is able to efficiently perform batch-wise anomaly detection without prior knowledge of the phenotype and the need for atlas-based segmentation. Anomaly detection is centered on the combined use of group-wise non-rigid image registration and robust principal component analysis (RPCA) for feature extraction and decomposition.
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Zhongliu Xie, Asanobu Kitamoto, Masaru Tamura, Toshihiko Shiroishi, and Duncan Gillies "High-throughput mouse phenotyping using non-rigid registration and robust principal component analysis", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978415 (21 March 2016);

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