A novel multi-atlas and multi-channel (MAMC) approach for multiple sclerosis lesion segmentation in brain MRI-Reference-Cited by-同舟云学术

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A novel multi-atlas and multi-channel (MAMC) approach for multiple sclerosis lesion segmentation in brain MRI

Published:2019-02-23 Issue:5 Volume:13 Page:1019-1027
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Wang Jingjing,Hu Changjun,Xu Huaqiang,Leng Yan,Zhang Liren,Zhao Yuefeng

Funder

the National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://link.springer.com/article/10.1007/s11760-019-01440-5/fulltext.html

Reference28 articles.

1. Li, C., Gore, J.C., Davatzikos, C.: Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn. Reson. Imaging 32(7), 913–923 (2014)

2. Lladó, X., Oliver, A., Cabezas, M., Freixenet, J., et al.: Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Inf. Sci. 186(1), 164–185 (2012)

3. Gaser, C., Schmidt, P., Arsic, M., et al.: An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. NeuroImage 59(4), 3774–3783 (2012)

4. Tohka, J.: Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: a review. World J. Radiol. 6(11), 855–864 (2014)

5. Cabezas, M., Oliver, A., Valverde, S., Beltran, B., et al.: BOOST: a supervised approach for multiple sclerosis lesion segmentation. J. Neurosci. Methods 237, 108–117 (2014)

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