摘要
图像的中层特征将图像中的全局信息和局部信息结合,同时具备代表性和特异性,能够更好地表达图像的信息。已有的研究工作成功地将中层特征用于医学图像的分割,主要的方法包括稀疏编码和空间金字塔匹配(spatial pyramid matching,SPM)算法,词典学习,以及神经网络等算法。中层特征的应用提高了算法性能。本文介绍了现有的基于中层特征的医学图像分割算法,并对今后的研究工作进行了展望。
The mid-level features combining the local features into a global image representation are representative and discriminative,which can serve as an image representation. Many successful models for medical image segmentation propose efficient methods to learn mid-level features,such as sparse coding technology combined with spatial pyramid matching（ SPM）,dictionary learning,neural network,etc. The application of mid-level feature improves the performance of segmentation algorithm. This paper introduces the medical image segmentation methods based on mid-level features and prospects the feature research work.
出处
《北京生物医学工程》
2016年第3期325-328,共4页
Beijing Biomedical Engineering
关键词
中层特征
医学图像
图像分割
特征提取
mid-level feature
medical image
image segmentation
feature extraction
