计算机应用与软件Issue(8)：328-333,6.DOI:10.3969/j.issn.1000-386x.2015.08.077
融合稀疏表示和字典学习的脑部 MR 图像分割
BRAIN MR IMAGE SEGMENTATION FUSING SPARSE REPRESENTATION AND DICTIONARY LEARNING
郭小粉 1任文杰1
作者信息
- 1. 河南农业职业学院电子信息工程系 河南 郑州451450
- 折叠
摘要
Abstract
For segmentation accuracy problem of brain MR image in regard to white matter, gray matter and cerebrospinal fluid, we proposed an image segmentation method which fuses the sparse representation and dictionary learning.First, it trains the over-completed dictionary using block-based input data.Then, it obtains the high-dimensional feature represented by optimal sparse according to the dictionary learnt.Finally, it implements the segmentation by combining the local and nonlocal reconstruction errors of each pixel.Results of experiment on simulated and real image database show that the proposed method can use the formula with distance factor and sparse factor to accurately segment MR images, and is superior to other MR segmentation methods in terms of stability.关键词
磁共振图像/图像分割/稀疏表示/重构误差/字典学习Key words
Magnetic resonance(MR) image Image segmentation/Sparse representation/Reconstruction error/Dictionary learning分类
信息技术与安全科学引用本文复制引用
郭小粉,任文杰..融合稀疏表示和字典学习的脑部 MR 图像分割[J].计算机应用与软件,2015,(8):328-333,6.
