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Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling.

We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstru... Full description

Journal Title: NeuroImage August 1, 2013, Vol.76, pp.11-23
Main Author: Tong, Tong
Other Authors: Wolz, Robin , Coupé, Pierrick , Hajnal, Joseph V , Rueckert, Daniel , Tong, Tong
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2013.02.069
Link: http://search.proquest.com/docview/1349094614/?pq-origsite=primo
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title: Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling.
format: Article
creator:
  • Tong, Tong
  • Wolz, Robin
  • Coupé, Pierrick
  • Hajnal, Joseph V
  • Rueckert, Daniel
  • Tong, Tong
subjects:
  • Adult–Methods
  • Aged–Physiology
  • Brain Mapping–Physiology
  • Discrimination Learning–Methods
  • Female–Methods
  • Hippocampus–Methods
  • Humans–Methods
  • Image Interpretation, Computer-Assisted–Methods
  • Magnetic Resonance Imaging–Methods
  • Male–Methods
ispartof: NeuroImage, August 1, 2013, Vol.76, pp.11-23
description: We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labeling approaches that rely on comparing image similarities between atlases and target images. In addition, we propose a Fixed Discriminative Dictionary Learning for Segmentation (F-DDLS) strategy, which can learn dictionaries offline and perform segmentations online, enabling a significant speed-up in the segmentation stage. The proposed method has been evaluated for the hippocampus segmentation of 80 healthy ICBM subjects and 202 ADNI images. The robustness of the proposed method, especially of our F-DDLS strategy, was validated by training and testing on different subject groups in the ADNI database. The influence of different parameters was studied and the performance of the proposed method was also compared with that of the nonlocal patch-based approach. The proposed method achieved a median Dice coefficient of 0.879 on 202 ADNI images and 0.890 on 80 ICBM subjects, which is competitive compared with state-of-the-art methods. •Sparse representation technique is applied to segmentations of brain MR images.•Discriminative dictionary learning is used to achieve a fast implementation.•Validation is carried out on hippocampus of 80 ICBM subjects and 202 ADNI images.•Segmentation results demonstrate the accuracy of the proposed method.•The proposed method may provide a potential direction for human brain labeling.
language: eng
source:
identifier: E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2013.02.069
fulltext: fulltext
issn:
  • 10959572
  • 1095-9572
url: Link


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titleSegmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling.
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ispartofNeuroImage, August 1, 2013, Vol.76, pp.11-23
identifierE-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2013.02.069
subjectAdult–Methods ; Aged–Physiology ; Brain Mapping–Physiology ; Discrimination Learning–Methods ; Female–Methods ; Hippocampus–Methods ; Humans–Methods ; Image Interpretation, Computer-Assisted–Methods ; Magnetic Resonance Imaging–Methods ; Male–Methods
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descriptionWe propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labeling approaches that rely on comparing image similarities between atlases and target images. In addition, we propose a Fixed Discriminative Dictionary Learning for Segmentation (F-DDLS) strategy, which can learn dictionaries offline and perform segmentations online, enabling a significant speed-up in the segmentation stage. The proposed method has been evaluated for the hippocampus segmentation of 80 healthy ICBM subjects and 202 ADNI images. The robustness of the proposed method, especially of our F-DDLS strategy, was validated by training and testing on different subject groups in the ADNI database. The influence of different parameters was studied and the performance of the proposed method was also compared with that of the nonlocal patch-based approach. The proposed method achieved a median Dice coefficient of 0.879 on 202 ADNI images and 0.890 on 80 ICBM subjects, which is competitive compared with state-of-the-art methods. •Sparse representation technique is applied to segmentations of brain MR images.•Discriminative dictionary learning is used to achieve a fast implementation.•Validation is carried out on hippocampus of 80 ICBM subjects and 202 ADNI images.•Segmentation results demonstrate the accuracy of the proposed method.•The proposed method may provide a potential direction for human brain labeling.
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titleSegmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling.
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36Fox, Nick
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42Senjem, Matt
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46Ward, Chad
47Koeppe, Robert A
48Foster, Norm
49Reiman, Eric M
50Chen, Kewei
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54Cairns, Nigel J
55Householder, Erin
56Taylor-Reinwald, Lisa
57Lee, Virginia
58Korecka, Magdalena
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titleSegmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling.
authorTong, Tong ; Wolz, Robin ; Coupé, Pierrick ; Hajnal, Joseph V ; Rueckert, Daniel ; Tong, Tong
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15Saykin, Andrew J
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atitleSegmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling.
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date2013-08-01