Depeursinge Adrien, Foncubierta-Rodriguez Antonio, Van de Ville Dimitri, Müller Henning
University of Applied Sciences Western Switzerland (HES-SO), Switzerland.
Med Image Comput Comput Assist Interv. 2011;14(Pt 3):231-8.
We develop a texture analysis framework to assist radiologists in interpreting high-resolution computed tomography (HRCT) images of the lungs of patients affected with interstitial lung diseases (ILD). Novel texture descriptors based on the Riesz transform are proposed to analyze lung texture without any assumption on prevailing scales and orientations. A global classification accuracy of 78.3% among five lung tissue types is achieved using locally-oriented Riesz components. Comparative performance analysis with features derived from optimized grey-level co-occurrence matrices showed an absolute gain of 6.1% in classification accuracy. The adaptability of the Riesz features is demonstrated by reconstructing templates according to the first principal components of the lung textures. The balanced performance achieved among the various lung textures suggest that the proposed methods can complement human observers in HRCT interpretation, and opens interesting perspectives for future research.
我们开发了一种纹理分析框架，以协助放射科医生解读间质性肺疾病（ILD）患者肺部的高分辨率计算机断层扫描（HRCT）图像。提出了基于里兹变换的新型纹理描述符，用于分析肺纹理，而无需对主要尺度和方向做任何假设。使用局部定向的里兹分量，在五种肺组织类型中实现了78.3%的全局分类准确率。与从优化的灰度共生矩阵导出的特征进行的比较性能分析表明，分类准确率绝对提高了6.1%。通过根据肺纹理的第一主成分重建模板，证明了里兹特征的适应性。在各种肺纹理中实现的平衡性能表明，所提出的方法可以在HRCT解读中补充人类观察者，并为未来的研究开辟了有趣的前景。
