CAJ | 학술논문

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。
본문제출일충신적결합문리、형상화쇠감특정신식적초성도상특정제취산법，병용우갑상선종류적량악성감별。중점연구병개진료갑상선종류적문리특정제취산법，재전통국부이치모식(Local Binary Pattern)산법적기출상，장린역개성타원상，경유리우종류적표시병유효제취료종류적각향이성결구신식；대거리편마채용모호라집건모，극복료초성도상반점조성대래적불학정성；차외，제취료종류적원형도，귀일화경향장도적표준차，면적비솔、조조도지수화쇠감계수작위표정갑상선종류적특정향량；최후，채용지지향량궤(Support Vector Machine)대갑상선결절진행분류식별。여기타특정제취방법상비교，본문제출적특정융합산법묘술준학솔고，구유교고적분류준학성，통과실험험증료소제방법적합이성화유효성。
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.