The early hepatic fibrosis staging is very important for timely diagnosis, prognosis, and treatment of all chronic liver diseases. Diffraction-enhanced imaging, which can provide much more information on soft tissue morphology than conventional absorption radiography, might be a potential noninvasive technique to diagnose and stage hepatic fibrosis. This paper presents different feature extraction strategies and classification methods to automatically classify hepatic fibrosis using diffraction-enhanced imaging images. Texture features are obtained using a total of three methods including first order feature, gray level co-occurrence matrix, and grayscale gradient co-occurrence matrix. The fusion of these texture features is also studied. The principal component analysis is used to reduce the dimension of the features and redundant information among data. The features are classified using two popular classification techniques, namely, K-nearest neighbors and support vector machines. On the basis of the comparison of different feature strategies and classification methods, we can identify the suitable methods for grading hepatic fibrosis. The proposed approach efficiently classifies the hepatic fibrosis DEI images into four classes with the highest classification accuracy of 99.99%. We further demonstrate the potential of the DEI images in staging hepatic fibrosis.

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肝纤维化分期方法的衍射增强成像研究

肝纤维化的早期分期对于所有慢性肝病的及时诊断，预后和治疗非常重要。衍射增强成像可以提供比常规吸收射线照相更多的关于软组织形态的信息，它可能是诊断和分阶段肝纤维化的潜在非侵入性技术。本文提出了使用衍射增强成像图像对肝纤维化进行自动分类的不同特征提取策略和分类方法。使用总共三种方法获得纹理特征，包括一阶特征，灰度共生矩阵和灰度梯度共生矩阵。还研究了这些纹理特征的融合。主成分分析用于减少特征的维度和数据之间的冗余信息。使用两种流行的分类技术对特征进行分类，即K近邻和支持向量机。在比较不同特征策略和分类方法的基础上，我们可以确定适合的肝纤维化分级方法。所提出的方法将肝纤维化DEI图像有效地分类为四类，最高分类精度为99.99％。我们进一步证明了DEI图像在肝纤维化分期中的潜力。在比较不同特征策略和分类方法的基础上，我们可以找到合适的肝纤维化分级方法。所提出的方法将肝纤维化DEI图像有效地分类为四类，最高分类精度为99.99％。我们进一步证明了DEI图像在肝纤维化分期中的潜力。在比较不同特征策略和分类方法的基础上，我们可以找到合适的肝纤维化分级方法。所提出的方法将肝纤维化DEI图像有效地分类为四类，最高分类精度为99.99％。我们进一步证明了DEI图像在肝纤维化分期中的潜力。