Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules
Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules
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DOI：
10.1007/s10278-014-9718-8
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发表时间：
2015-02-01
影响因子：
4.4
通讯作者：
Liang, Zhengrong
中科院分区：
文献类型：
--
作者：
Han, Fangfang;Wang, Huafeng;Liang, Zhengrong
肺结节的良恶性鉴别具有重要的临床意义。CT图像中肺结节的纹理特征除了与几何相关的测量外，还反映了恶性肿瘤的一个强有力的特征。本研究首先比较了三种众所周知的二维（2D）纹理特征（Haralick，Gabor和局部二进制模式或局部二进制模式特征）的肺结节CADx使用最大的公共数据库建立的肺图像数据库联盟和图像数据库资源倡议，然后调查从2D扩展到三维（3D）空间。定量比较的措施是由完善的支持向量机（SVM）分类器，下的受试者工作特征曲线（AUC）和p值的假设t检验的面积。虽然三种特征类型显示出约90%的区分率，但Haralick特征在足够的图像切片厚度下实现了92.70%的最高AUC值，其中较薄或较厚的厚度将由于过度的图像噪声或轴向细节的丢失而使性能恶化。与单个最大切片相比，在计算所有图像切片上的2D特征时观察到增益。3D扩展揭示了潜在的增益时，可以找到最佳数量的方向。所有的观察，从这个系统的调查研究的三个功能类型可以得出的结论，Haralick功能类型是一个更好的选择，使用完整的3D数据是有益的，和图像的厚度和噪声之间的充分权衡是理想的CADx性能。这些结论为进一步研究肺结节的CT鉴别诊断提供了依据。
Differentiation of malignant and benign pulmonary nodules is of paramount clinical importance. Texture features of pulmonary nodules in CT images reflect a powerful character of the malignancy in addition to the geometry-related measures. This study first compared three well-known types of two-dimensional (2D) texture features (Haralick, Gabor, and local binary patterns or local binary pattern features) on CADx of lung nodules using the largest public database founded by Lung Image Database Consortium and Image Database Resource Initiative and then investigated extension from 2D to three-dimensional (3D) space. Quantitative comparison measures were made by the well-established support vector machine (SVM) classifier, the area under the receiver operating characteristic curves (AUC) and the p values from hypothesis t tests. While the three feature types showed about 90 % differentiation rate, the Haralick features achieved the highest AUC value of 92.70 % at an adequate image slice thickness, where a thinner or thicker thickness will deteriorate the performance due to excessive image noise or loss of axial details. Gain was observed when calculating 2D features on all image slices as compared to the single largest slice. The 3D extension revealed potential gain when an optimal number of directions can be found. All the observations from this systematic investigation study on the three feature types can lead to the conclusions that the Haralick feature type is a better choice, the use of the full 3D data is beneficial, and an adequate tradeoff between image thickness and noise is desired for an optimal CADx performance. These conclusions provide a guideline for further research on lung nodule differentiation using CT imaging.