Depeursinge Adrien, Sage Daniel, Hidki Asmâa, Platon Alexandra, Poletti Pierre-Alexandre, Unser Michael, Müller Henning
Service of Medical Informatics, University & University Hospitals of Geneva (HUG), Geneva, Switzerland.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:6260-3. doi: 10.1109/IEMBS.2007.4353786.
We describe a texture classification system that identifies lung tissue patterns from high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD). This pattern recognition task is part of an image-based diagnostic aid system for ILDs. Five lung tissue patterns (healthy, emphysema, ground glass, fibrosis and microdules) selected from a multimedia database are classified using the overcomplete discrete wavelet frame decompostion combined with grey-level histogram features. The overall multiclass accuracy reaches 92.5% of correct matches while combining the two types of features, which are found to be complementary.
我们描述了一种纹理分类系统，该系统可从间质性肺疾病（ILD）患者的高分辨率计算机断层扫描（HRCT）图像中识别肺组织模式。此模式识别任务是基于图像的ILD诊断辅助系统的一部分。从多媒体数据库中选择的五种肺组织模式（健康、肺气肿、磨玻璃、纤维化和小结节），使用过完备离散小波框架分解结合灰度直方图特征进行分类。在结合这两种被发现具有互补性的特征时，整体多类准确率达到了92.5%的正确匹配率。
