TY - GEN
T1 - Automatic quality control for population imaging
T2 - A generic unsupervised approach
AU - Farzi, Mohsen
AU - Pozo, Jose M.
AU - McCloskey, Eugene V.
AU - Mark Wilkinson, J.
AU - Frangi, Alejandro F.
N1 - Funding Information:
M Farzi was funded through a PhD Fellowship from the United Kingdom Medical Research Council-Arthritis Research-UK Centre for Integrated research into Musculoskeletal Ageing (CIMA).
Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Population imaging studies have opened new opportunities for a comprehensive characterization of disease phenotypes by providing large-scale databases. A major challenge is related to the ability to amass automatically and accurately the vast amount of data and hence to develop streamlined image analysis pipelines that are robust to the varying image quality. This requires a generic and fully unsupervised quality assessment technique. However,existing methods are designed for specific types of artefacts and cannot detect incidental unforeseen artefacts. Furthermore,they require manual annotations,which is a demanding task,prone to error,and in some cases ambiguous. In this study,we propose a generic unsupervised approach to simultaneously detect and localize the artefacts. We learn the normal image properties from a large dataset by introducing a new image representation approach based on an optimal coverage of images with the learned visual dictionary. The artefacts are then detected and localized as outliers. We tested our method on a femoral DXA dataset with 1300 scans. The sensitivity and specificity are 81.82% and 94.12%,respectively.
AB - Population imaging studies have opened new opportunities for a comprehensive characterization of disease phenotypes by providing large-scale databases. A major challenge is related to the ability to amass automatically and accurately the vast amount of data and hence to develop streamlined image analysis pipelines that are robust to the varying image quality. This requires a generic and fully unsupervised quality assessment technique. However,existing methods are designed for specific types of artefacts and cannot detect incidental unforeseen artefacts. Furthermore,they require manual annotations,which is a demanding task,prone to error,and in some cases ambiguous. In this study,we propose a generic unsupervised approach to simultaneously detect and localize the artefacts. We learn the normal image properties from a large dataset by introducing a new image representation approach based on an optimal coverage of images with the learned visual dictionary. The artefacts are then detected and localized as outliers. We tested our method on a femoral DXA dataset with 1300 scans. The sensitivity and specificity are 81.82% and 94.12%,respectively.
UR - http://www.scopus.com/inward/record.url?scp=84996542564&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46723-8_34
DO - 10.1007/978-3-319-46723-8_34
M3 - Conference contribution
AN - SCOPUS:84996542564
SN - 9783319467221
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 291
EP - 299
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
A2 - Unal, Gozde
A2 - Ourselin, Sebastian
A2 - Joskowicz, Leo
A2 - Sabuncu, Mert R.
A2 - Wells, William
PB - Springer-Verlag Italia
ER -