Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10127223 | Biomedical Signal Processing and Control | 2019 | 6 Pages |
Abstract
An automatic brain tumor segmentation method based on texture feature and kernel sparse coding from FLAIR (fluid attenuated inversion recovery) contrast-enhanced MRIs (magnetic resonance imaging) is presented in this paper. First, the MRIs are pre-processed to reduce noise, enhance contrast and correct the intensity non-uniformity. Then sparse coding is performed on the first order and second order statistical eigenvector extracted from original MRIs which is a patch of 3â¯Ãâ¯3 around the voxel. The kernel dictionary learning is used to extract the non-linear features to construct two adaptive dictionaries for healthy and pathologically tissues respectively. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels, then the linear discrimination method is used to classify the target pixels. In the end, the flood-fill operation is used to improve the segmentation quality. The results demonstrate that the method based on kernel sparse coding has better capacity and higher segmentation accuracy with low computation cost.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Jijun Tong, Yingjie Zhao, Peng Zhang, Lingyu Chen, Lurong Jiang,