Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Tong:2015:10.1016/j.media.2015.04.015,
author = {Tong, T and Wolz, R and Wang, Z and Gao, Q and Misawa, K and Fujiwara, M and Mori, K and Hajnal, JV and Rueckert, D},
doi = {10.1016/j.media.2015.04.015},
journal = {Medical Image Analysis},
pages = {92--104},
title = {Discriminative dictionary learning for abdominal multi-organ segmentation.},
url = {http://dx.doi.org/10.1016/j.media.2015.04.015},
volume = {23},
year = {2015}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.
AU - Tong,T
AU - Wolz,R
AU - Wang,Z
AU - Gao,Q
AU - Misawa,K
AU - Fujiwara,M
AU - Mori,K
AU - Hajnal,JV
AU - Rueckert,D
DO - 10.1016/j.media.2015.04.015
EP - 104
PY - 2015///
SN - 1361-8423
SP - 92
TI - Discriminative dictionary learning for abdominal multi-organ segmentation.
T2 - Medical Image Analysis
UR - http://dx.doi.org/10.1016/j.media.2015.04.015
UR - http://hdl.handle.net/10044/1/23317
VL - 23
ER -