Gangeh Mehrdad J, Sørensen Lauge, Shaker Saher B, Kamel Mohamed S, de Bruijne Marleen, Loog Marco
Department of Electrical and Computer Engineering, University of Waterloo, Canada.
Med Image Comput Comput Assist Interv. 2010;13(Pt 3):595-602. doi: 10.1007/978-3-642-15711-0_74.
In this paper, a texton-based classification system based on raw pixel representation along with a support vector machine with radial basis function kernel is proposed for the classification of emphysema in computed tomography images of the lung. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. The results show the superiority of the proposed approach to common techniques in the literature including moments of the histogram of filter responses based on Gaussian derivatives. The performance of the proposed system, with an accuracy of 96.43%, also slightly improves over a recently proposed approach based on local binary patterns.
本文提出了一种基于原始像素表示的纹理分类系统，并结合具有径向基函数核的支持向量机，用于在肺部计算机断层扫描图像中对肺气肿进行分类。该方法在168个带注释的感兴趣区域上进行了测试，这些区域包括正常组织、小叶中心型肺气肿和间隔旁肺气肿。结果表明，该方法优于文献中的常用技术，包括基于高斯导数的滤波器响应直方图矩。所提出系统的性能准确率为96.43%，也比最近提出的基于局部二值模式的方法略有提高。
