一种新的甲状腺肿瘤超声图像特征提取算法  

A Novel Feature Extraction Algorithm of Thyroid Tumor Ultrasound Image

作　　者：赵杰[1] 祁永梅[1] 

机构地区：[1]河北大学电子信息工程学院,河北保定071000

出　　处：《光电工程》2013年第9期8-15,共8页Opto-Electronic Engineering

基　　金：河北省卫生厅科研基金资助项目(20120395)

摘　　要：本文提出一种新的结合纹理、形状和衰减特征信息的超声图像特征提取算法,并用于甲状腺肿瘤的良恶性鉴别。重点研究并改进了甲状腺肿瘤的纹理特征提取算法,在传统局部二值模式(Local Binary Pattern)算法的基础上,将邻域改成椭圆状,更有利于肿瘤的表示并有效提取了肿瘤的各向异性结构信息;对距离编码采用模糊逻辑建模,克服了超声图像斑点噪声带来的不确定性;此外,提取了肿瘤的圆形度,归一化径向长度的标准差,面积比率、粗糙度指数和衰减系数作为表征甲状腺肿瘤的特征向量;最后,采用支持向量机(Support Vector Machine)对甲状腺结节进行分类识别。与其他特征提取方法相比较,本文提出的特征融合算法描述准确率高,具有较高的分类准确性,通过实验验证了所提方法的合理性和有效性。A novel feature extraction algorithm of ultrasound image combined with the texture, shape and the attenuation characteristics information was proposed, which could be used to identify the benign or malignant thyroid tumors. This paper focused on improving the texture feature extraction algorithm of thyroid tumors. On the basis of the traditional Local Binary Pattern （LBP） algorithm, we extended the neighborhood distribution in the elongated manner, which was more conducive to describe thyroid tumors and extract anisotropic properties of tumor effectively. Also, we used fuzzy logic to encode distance, which overcame the uncertainty of speckle noise in the ultrasound image. Furthermore, we extracted tumor circularity, standard deviation of the normalized radial length, area ratio, roughness index and the attenuation coefficient, which formed a feature vector to characterize thyroid tumors. Finally, Support Vector Machine （SVM） was used to classify and identify the thyroid nodules. Compared with other methods of feature extraction, the proposed feature fusion algorithm has a high accuracy of description, which can achieve higher classification accuracy and the reasonableness and effectiveness of the proposed method is verified by experiments

关 键 词：甲状腺肿瘤 纹理信息 衰减特征 形状信息 局部二值模式 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]