Depeursinge Adrien, Foncubierta-Rodriguez Antonio, Van de Ville Dimitri, Müller Henning
University of Applied Sciences Western Switzerland.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):517-24. doi: 10.1007/978-3-642-33454-2_64.
Texture-based computerized analysis of high-resolution computed tomography images from patients with interstitial lung diseases is introduced to assist radiologists in image interpretation. The cornerstone of our approach is to learn lung texture signatures using a linear combination of N-th order Riesz templates at multiple scales. The weights of the linear combination are derived from one-versus-all support vector machines. Steerability and multiscale properties of Riesz wavelets allow for scale and rotation covariance of the texture descriptors with infinitesimal precision. Orientations are normalized among texture instances by locally aligning the Riesz templates, which is carried out analytically. The proposed approach is compared with state-of-the-art texture attributes and shows significant improvement in classification performance with an average area under receiver operating characteristic curves of 0.94 for five lung tissue classes. The derived lung texture signatures illustrate optimal class wise discriminative properties.
基于纹理的间质性肺疾病患者高分辨率计算机断层扫描图像的计算机化分析被引入，以协助放射科医生进行图像解读。我们方法的基石是使用多尺度N阶里斯模板的线性组合来学习肺纹理特征。线性组合的权重来自一对多支持向量机。里斯小波的可操纵性和多尺度特性允许纹理描述符具有无限精度的尺度和旋转协方差。通过对里斯模板进行局部对齐，在纹理实例之间对方向进行归一化，这是通过解析方法实现的。将所提出的方法与现有最先进的纹理属性进行比较，结果表明在分类性能方面有显著提高，对于五种肺组织类别，接收器操作特征曲线下的平均面积为0.94。所推导的肺纹理特征说明了最佳的类别判别特性。
