Pulmonary emphysema classification based on an improved texton learning model by sparse representation
Pulmonary emphysema classification based on an improved texton learning model by sparse representation
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基于稀疏表示改进纹理学习模型的肺气肿分类
DOI：
10.1117/12.2007934
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
and H.Fujita
中科院分区：
文献类型：
--
作者：
M.Zhang;X.Zhou;H.Chen;C.Muramatsu;T.Hara;R.Yokoyama;M.Kanematsu;and H.Fujita
在本文中，我们提出了一种纹理分类方法的基础上学习纹理基元通过稀疏表示（SR）与新的特征直方图地图在肺气肿的分类。首先，通过KSVD学习训练数据集中的每一类图像块来学习纹理基元的过完备字典。在这一阶段，高通滤波器被引入到排除在平滑区域的补丁，以加快字典学习过程。其次，3D联合SR系数和强度直方图的测试图像被用来表征感兴趣的区域（ROI），而不是传统的特征直方图构建的测试图像的SR系数的字典。然后使用分类器进行分类，其中距离作为直方图相异性度量。从14个受试者中提取了470个带注释的ROI，其中包括6个间隔旁肺气肿（PSE）受试者，5个小叶中心肺气肿（CLE）受试者和3个全小叶肺气肿（PLE）受试者，用于评估所提出的方法的有效性和鲁棒性。所提出的方法进行了测试，167 PSE，240 CLE和63 PLE ROI组成的轻度，中度和重度肺气肿。所提出的系统的准确性是约74%，88%和89%的PSE，CLE和PLE，分别。
In this paper, we present a texture classification method based on texton learned via sparse representation (SR) with new feature histogram maps in the classification of emphysema. First, an overcomplete dictionary of textons is learned via KSVD learning on every class image patches in the training dataset. In this stage, high-pass filter is introduced to exclude patches in smooth area to speed up the dictionary learning process. Second, 3D joint-SR coefficients and intensity histograms of the test images are used for characterizing regions of interest (ROIs) instead of conventional feature histograms constructed from SR coefficients of the test images over the dictionary. Classification is then performed using a classifier with distance as a histogram dissimilarity measure. Four hundreds and seventy annotated ROIs extracted from 14 test subjects, including 6 paraseptal emphysema (PSE) subjects, 5 centrilobular emphysema (CLE) subjects and 3 panlobular emphysema (PLE) subjects, are used to evaluate the effectiveness and robustness of the proposed method. The proposed method is tested on 167 PSE, 240 CLE and 63 PLE ROIs consisting of mild, moderate and severe pulmonary emphysema. The accuracy of the proposed system is around 74%, 88% and 89% for PSE, CLE and PLE, respectively.