基于局部纹理特征的超声甲状腺结节良恶性识别  

Ultrasound Thyroid Images Classification Based on Local Texture Features

作　　者：熊伟[1] 龚勋[1] 罗俊[2] 李天瑞[1] 

机构地区：[1]西南交通大学信息技术与科学学院,成都610031 [2]四川省医学科学院四川省人民医院,成都610000

出　　处：《数据采集与处理》2015年第1期186-191,共6页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(6117504)资助项目;计算智能重庆市重点实验室开放基金(CQ-LCI-2013-06)资助项目

摘　　要：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。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.

关 键 词：图像分类 多示例学习 灰度共生矩阵 甲状腺超声图像 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]