Zhao Wei, Xu Rui, Hirano Yasushi, Tachibana Rie, Kido Shoji
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5457-60. doi: 10.1109/EMBC.2013.6610784.
This paper describes a computer-aided diagnosis (CAD) method to classify diffuse lung diseases (DLD) patterns on HRCT images. Due to the high variety and complexity of DLD patterns, the performance of conventional methods on recognizing DLD patterns featured by geometrical information is limited. In this paper, we introduced a sparse representation based method to classify normal tissues and five types of DLD patterns including consolidation, ground-glass opacity, honeycombing, emphysema and nodular. Both CT values and eigenvalues of Hessian matrices were adopted to calculate local features. The 2360 VOIs from 117 subjects were separated into two independent set. One set was used to optimize parameters, and the other set was adopted to evaluation. The proposed technique has a overall accuracy of 95.4%. Experimental results show that our method would be useful to classify DLD patterns on HRCT images.
本文描述了一种用于在高分辨率计算机断层扫描（HRCT）图像上对弥漫性肺疾病（DLD）模式进行分类的计算机辅助诊断（CAD）方法。由于DLD模式的高度多样性和复杂性，传统方法在识别以几何信息为特征的DLD模式方面的性能有限。在本文中，我们引入了一种基于稀疏表示的方法来对正常组织和包括实变、磨玻璃影、蜂窝状、肺气肿和结节状在内的五种DLD模式进行分类。同时采用CT值和黑塞矩阵的特征值来计算局部特征。将来自117名受试者的2360个感兴趣区域（VOIs）分为两个独立的集合。一个集合用于优化参数，另一个集合用于评估。所提出的技术总体准确率为95.4%。实验结果表明，我们的方法对于在HRCT图像上对DLD模式进行分类是有用的。
