Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery

Yanwu Xu, Lixin Duan, Huazhu Fu, Xiaoqin Zhang, Damon Wing Kee Wong, Baskaran Mani, Tin Aung, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


We present a one-step approach based on low-rank tensor recovery for axial alignment in 360-degree anterior chamber optical coherence tomography. Achieving translational alignment and rotation correction of cross-sections simultaneously,this technique obtains a better anterior segment topographical representation and improves quantitative measurement accuracy and reproducibility of disease related parameters. Through its use of global information,the proposed method is more robust compared to using only individual or paired slices,and less sensitive to noise and motion artifacts. In angle closure analysis on 30 patient eyes,the preliminary results indicate that the proposed axial alignment method can not only facilitate manual qualitative analysis with more distinct landmark representation and much less human labor,but also can improve the accuracy of automatic quantitative assessment by 2.9%,which demonstrates that the proposed approach is promising for a wide range of clinical applications.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsLeo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319467252
Publication statusPublished - 2016
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9902 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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