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Brain tumor classification and segmentation using sparse coding and dictionary learning
- Source :
- Biomedical engineering = Biomedizinische Technik, 61(4), 413-429. De Gruyter
- Publication Year :
- 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.
- Subjects :
- Databases, Factual
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Biomedical Engineering
Scale-space segmentation
02 engineering and technology
computer.software_genre
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Image texture
Voxel
Image Interpretation, Computer-Assisted
0202 electrical engineering, electronic engineering, information engineering
Humans
Computer vision
Segmentation
METIS-319888
Brain Neoplasms
Segmentation-based object categorization
business.industry
Pattern recognition
Image segmentation
Feature (computer vision)
020201 artificial intelligence & image processing
Artificial intelligence
Neural coding
business
computer
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 00135585
- Database :
- OpenAIRE
- Journal :
- Biomedical engineering = Biomedizinische Technik, 61(4), 413-429. De Gruyter
- Accession number :
- edsair.doi.dedup.....f0c9d213229c34240be0a8f0ffcdbb59