基于局部纹理特征的超声甲状腺结节良恶性识别

为了实现超声甲状腺结节的自动分类,本文提出了一种利用局部纹理特征与多示例学习方法相结合以克服结节区域特征信息的重叠性。从感兴趣区域提取其局部纹理特征,将感兴趣区域看作由所有局部特征构成的示例包,再采用多示例学习方法中的Citation-kNN算法来实现对样本进行识别分类。实验结果表明,本文方法对超声甲状腺结节良恶性识别具有较高的分类准确率,且分类准确率达85.59%,可应用于甲状腺临床诊断并为其相关领域提供有效参考。

Ultrasound Thyroid Images Classification Based on Local Texture Features

To accomplish the automatic classification of thyroid nodules, the local texture features combining with the multiple instance learning method is proposed to overcome the overlap of the thyroid nodules. The local texture features are abstracted from the region of interest which is taken as the instance package composed of local features. The citation-kNN algorithm of the multi-instance learning(MIL) method is adopted to classify samples of this paper. Experimental results show that the identification method has higher classification accuracy and the accuracy achieves 85.59%. It is expected to be applied to the clinical diagnosis of thyroid, and provide a valuable reference for other related domains.