中文摘要：

针对乳腺X光医学图像多类分类精度普遍较低的问题,提出了一种基于边缘检测的医学图像多类分类新方法。首先对乳腺X光医学图像进行预处理包括图像去噪和图像增强,再通过边缘检测方法,获取乳腺X光医学图像中的肿块区域,对检测到的肿块区域使用灰度共生矩阵提取特征,对于提取到的特征,采用支持向量机（Support vector machine,SVM）的方法进行分类;对于检测不到肿块区域的乳腺X光医学图像可直接分类为无乳腺癌（即正常）类。实验结果表明,与传统的支持向量机多类分类算法相比,基于边缘检测的医学图像多类分类新方法在乳腺X光医学图像上具有更高的分类精度。

英文摘要：

To improve the low accuracy of breast X-ray medical image multi-class classification,a new medical image multi-class classification method based on edge detection is proposed.The breast X-ray medical image is firstly preprocessed,including image denoising and enhancement.Tumor region in X-ray medical image can be acquired through edge detection algorithm.Feature selection on tumor is implemented using gray level co-occurrence matrix.This method uses support vector machine（SVM）to classify the medical image according to selected features.The X-ray medical image without any detected edge can be directly classified into the normal without breast cancer.The experiment result shows that the new medical image multi-class classification method based on edge detection has a higher precision than the traditional SVM multi-class classification algorithm on breast X-ray medical image.