基于mRIVIR和SVM的弹性图像特征选择与分类

为客观的评价弹性图像，利用图像处理与模式识别技术进行分析．首先通过彩色变换获取弹性信 息，然后提取弹性图像用户感兴趣区域的一阶统计特征和纹理特征，采用“最小冗余最大相关”( mRMR)算 法选择优化的特征，最后使用带有核函数的SVM分类器对弹性图像进行分类．实验结果表明：该方法具有较 高的准确率(92%)．采用计算机辅助诊断技术对弹性图像进行定量分析可有助于提高诊断准确率

Elastogram features selection and classification based on mRMR and SVM

For evaluating elastogram objectively, image processing and pattern recogniton techniques are pro- posed. First the real elasticity information encoded in color was extracted by transform the image from RGB color space to HSV space. Then the statistical features and texture features were extracted from region of inter- est on the elastogram. The important and reliable features were selected by using Minimum-Redundancy-Maxi- mum-Relevance ( mRMR) algorithm. Finally the selected features were input to the SVM classifier to classify the thyroid nodules into benign and malignant. The experiment results confirmed the method had higher accu- racy (92% ). It is helpful to improve the clinical accuracy by using CAD techniques