IEEE Trans Med Imaging. 2017 Oct;36(10):2077-2086. doi: 10.1109/TMI.2017.2708988. Epub 2017 May 26.
This paper deals with a new anisotropic discrete dual-tree wavelet transform (ADDTWT) to characterize the anisotropy of bone texture. More specifically, we propose to extend the conventional discrete dual-tree wavelet transform (DDTWT) by using the anisotropic basis functions associated with the hyperbolic wavelet transform instead of isotropic spectrum supports. A texture classification framework is adopted to assess the performance of the proposed transform. The generalized Gaussian distribution is used to model the distribution of the sub-band coefficients. The estimated vector of parameters for each image is then used as input for the support vector machine classifier. Experiments were conducted on synthesized anisotropic fractional Brownian motion fields and on a real database composed of osteoporotic patients and control cases. Results show that the ADDTWT outperforms most of the competing anisotropic transforms with an area under curve rate of 93%.
本文提出了一种新的各向异性离散双树复小波变换（ADDTWT）来描述骨纹理的各向异性。具体来说，我们提出通过使用与双曲小波变换相关的各向异性基函数来扩展传统的离散双树复小波变换（DDTWT），而不是各向同性频谱支持。采用纹理分类框架来评估所提出变换的性能。广义高斯分布用于建模子带系数的分布。然后，将每个图像的参数估计向量用作支持向量机分类器的输入。在合成各向异性分数布朗运动场和由骨质疏松症患者和对照组组成的真实数据库上进行了实验。结果表明，ADDTWT 的性能优于大多数竞争的各向异性变换，曲线下面积率为 93%。
