Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Sydney 2006, Australia.
IEEE Trans Med Imaging. 2013 Apr;32(4):797-808. doi: 10.1109/TMI.2013.2241448. Epub 2013 Jan 18.
In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches. And a new patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.
在本文中，我们提出了一种新的分类方法，用于对高分辨率计算机断层扫描（HRCT）图像中的五类肺部组织进行分类，该方法基于基于特征的图像补丁近似。我们设计了两个新的特征描述符，以提高特征描述的能力，即旋转不变的 Gabor 局部二值模式（RGLBP）纹理描述符和多坐标方向梯度直方图（MCHOG）梯度描述符。然后，每个图像补丁都基于其与参考图像补丁的特征近似来进行标记。我们设计了一种新的基于补丁的稀疏近似（PASA）方法，其主要组件包括：基于稀疏的分类的最小差异标准、用于判别近似的补丁特定自适应、以及用于距离计算的特征空间加权。然后，将补丁级别的标记累积为区域级分类的概率估计。在一个公开的ILD 数据库上对所提出的方法进行了评估，结果表明与现有技术相比，性能有了显著的提高。
