基于多尺度融合的甲状腺结节图像特征提取

甲状腺结节是一种常见的多发病，超声技术是该疾病首选的检查方法。在超声图像中提取区分甲状腺结节良恶性的纹理特征并进行判别具有广阔的临床应用前景。双树复小波变换（Dual-tree complex wavelet transform，DT-CWT）和Gabor小波是纹理特征提取的常用方法。本文提出一种基于多尺度的DT-CWT和Gabor特征融合的甲状腺结节识别方法。该方法首先通过高斯金字塔将甲状腺超声图像分解到多尺度空间，然后提取图像的DT-CWT和Gabor的多尺度特征，最后实现特征融合。通过应用支持向量机（Support vector machine，SVM）分类器实现分类，验证特征提取方法的有效性。实验结果表明，本文提出的方法能达到较高的识别率。

Image Feature Extraction of Thyroid Nodule Based on Multi-scale Fusion

Thyroid nodule is a kind of frequently-occurring disease. Ultrasound technology is the preferred examination method for the disease. Extracting the texture feature distinguishing the benign and malignancy in the ultrasound images and discriminate them has a wide prospect of clinical application. Dual-tree complex wavelet transform (DT-CWT) and Gabor wavelet are the important approaches to texture feature extraction. Here, we present an approach of thyroid nodules recognition by fusing multi-scale DT-CWT and Gabor wavelet features. Firstly, we use Gaussian pyramid to decompose the thyroid ultrasound image into multi-scale space. Followed by extracting DT-CWT and Gabor multi-scale features, the feature fusion is performed. Support vector machine (SVM) is applied to classify so as to verify the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a high recognition rate.