Paper Info
Title
Non-Rigid Registration And Robust Principal Component Analysis With Variation Priors: A High-Throughput Mouse Phenotyping Approach
Abstract
Intensive global efforts are underway towards phenotyping the mouse genome, by systematically knocking out all approximate to 25,000 genes for comparative study. Analytical work of this scale easily overwhelms the traditional method using histological examination, leading to a significant demand for high-throughput approaches, especially via image informatics to efficiently identify phenotypes concerning morphological anomaly. We propose a high-throughput batch-wise anomaly detection framework without prior knowledge of the phenotype and the need for segmentation. Anomaly detection is centered on feature decomposition using robust principal component analysis (RPCA), which has previously been applied to many computer vision domains. However, baseline RPCA does not work well in the biomedical domain due to substantial natural variation in imaging data. In contrast, we develop a modified version (RPCA-P) that incorporates variation priors, coupled with non-rigid image registration to achieve this goal.
Year
DOI
Venue
2016
10.1109/ISBI.2016.7493462
2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
Keywords
Field
DocType
Mouse phenotyping, anomaly detection, robust principal component analysis (RPCA), natural variation, non-rigid image registration
Anomaly detection,Informatics,Computer vision,Pattern recognition,Computer science,Segmentation,Robust principal component analysis,Artificial intelligence,Throughput,Prior probability,Image registration
Conference
ISSN
Citations 
PageRank 
1945-7928
0
0.34
References 
Authors
7
5
Name
Order
Citations
PageRank
Zhongliu Xie151.20
Asanobu Kitamoto28415.31
Masaru Tamura311.39
Toshihiko Shiroishi4324.33
Duncan Fyfe Gillies59717.86