Sørensen Lauge, Shaker Saher B, de Bruijne Marleen
Department of Computer Science, University of Copenhagen, Denmark.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):934-41. doi: 10.1007/978-3-540-85988-8_111.
In this paper we propose to use local binary patterns (LBP) as features in a classification framework for classifying different texture patterns in lung computed tomography. Image intensity is included by means of the joint LBP and intensity histogram, and classification is performed using the k nearest neighbor classifier with histogram similarity as distance measure. The proposed method is evaluated on a set of 168 regions of interest comprising normal tissue and different emphysema patterns, and compared to a filter bank based on Gaussian derivatives. The joint LBP and intensity histogram, achieving a classification accuracy of 95.2%, shows superior performance to using the common approach of taking moments of the filter response histograms as features, and slightly better performance than using the full filter response histograms instead. Classification results are better than some of those previously reported in the literature.
在本文中，我们提议在一个分类框架中使用局部二值模式（LBP）作为特征，用于对肺部计算机断层扫描中的不同纹理模式进行分类。通过联合LBP和强度直方图纳入图像强度，并使用以直方图相似度作为距离度量的k近邻分类器进行分类。所提出的方法在一组包含正常组织和不同肺气肿模式的168个感兴趣区域上进行评估，并与基于高斯导数的滤波器组进行比较。联合LBP和强度直方图的分类准确率达到了95.2%，与使用将滤波器响应直方图的矩作为特征的常见方法相比，表现更优，并且比直接使用完整的滤波器响应直方图略好。分类结果优于文献中先前报道的一些结果。
