@inproceedings{ed9d95b747354588805ebeb2bcbc7e12,
title = "Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery",
abstract = "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.",
author = "Yanwu Xu and Lixin Duan and Huazhu Fu and Xiaoqin Zhang and Wong, {Damon Wing Kee} and Baskaran Mani and Tin Aung and Jiang Liu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",
year = "2016",
doi = "10.1007/978-3-319-46726-9_51",
language = "English",
isbn = "9783319467252",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "441--449",
editor = "Leo Joskowicz and Sabuncu, {Mert R.} and William Wells and Gozde Unal and Sebastian Ourselin",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",
address = "Germany",
}
