Details
| Original language | English |
|---|---|
| Pages (from-to) | 413-429 |
| Number of pages | 17 |
| Journal | Biomedizinische Technik |
| Volume | 61 |
| Issue number | 4 |
| Publication status | Published - 6 Aug 2016 |
Abstract
This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.
Keywords
- brain tumor, classification, dictionary learning, segmentation, sparse coding, texture, topology
ASJC Scopus subject areas
- Engineering(all)
- Biomedical Engineering
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In: Biomedizinische Technik, Vol. 61, No. 4, 06.08.2016, p. 413-429.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Brain tumor classification and segmentation using sparse coding and dictionary learning
AU - Salman Al-Shaikhli, Saif Dawood
AU - Yang, Michael Ying
AU - Rosenhahn, Bodo
PY - 2016/8/6
Y1 - 2016/8/6
N2 - This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.
AB - This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.
KW - brain tumor
KW - classification
KW - dictionary learning
KW - segmentation
KW - sparse coding
KW - texture
KW - topology
UR - http://www.scopus.com/inward/record.url?scp=84983778257&partnerID=8YFLogxK
U2 - 10.1515/bmt-2015-0071
DO - 10.1515/bmt-2015-0071
M3 - Article
C2 - 26351901
AN - SCOPUS:84983778257
VL - 61
SP - 413
EP - 429
JO - Biomedizinische Technik
JF - Biomedizinische Technik
SN - 0013-5585
IS - 4
ER -