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

TY - JOUR

T1 - Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling

AU - Tong, Tong

AU - Wolz, Robin

AU - Coupe, Pierrick

AU - Hajnal, Joseph V.

AU - Rueckert, Daniel

AU - Alzheimer's Dis Neuroimaging

PY - 2013/8/1

Y1 - 2013/8/1

N2 - 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. (c) 2013 Elsevier Inc. All rights reserved.

AB - 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. (c) 2013 Elsevier Inc. All rights reserved.

KW - Structural MR images

KW - Patch-based segmentation

KW - Discriminative dictionary learning

KW - Sparse representation

KW - MULTI-ATLAS SEGMENTATION

KW - ALZHEIMERS-DISEASE

KW - FACE RECOGNITION

KW - BRAIN

KW - AMYGDALA

KW - FUSION

KW - SELECTION

KW - CLASSIFICATION

KW - REPRESENTATION

KW - COMBINATION

U2 - 10.1016/j.neuroimage.2013.02.069

DO - 10.1016/j.neuroimage.2013.02.069

M3 - Article

SN - 1053-8119

VL - 76

SP - 11

EP - 23

JO - NeuroImage

JF - NeuroImage

IS - 1

ER -