Brain tumor classification and segmentation using sparse coding and dictionary learning

Biomedizinische Technik. Biomedical Engineering
Saif Dawood Salman Al-ShaikhliBodo Rosenhahn

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...Continue Reading

References

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Citations

Jul 23, 2016·Biomedizinische Technik. Biomedical Engineering·Erik Schkommodau

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Methods Mentioned

BETA
feature extraction
feature patch extraction

Software Mentioned

Brain Extraction Tool
Virtual Skeleton
eqalign
Medical Image Processing , Analysis , and Visualization
BraTS

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